"Artificial Intelligence without Big Data Analytics is lame, and Big Data Analytics without Artificial Intelligence is blind." Dr. O. Aly, Computer Science.
The purpose of this discussion is to address one of the sectors that utilizes a few unique information technology (IT) requirements. The selected sector for this discussion is health care. The discussion addresses the IT needs based on a case study. The discussion begins with Information Technology Key Role in Business, followed by the Healthcare Industry Case Study.
Information Technology Key Role in
Business
Information technology (IT) is a critical
resource for businesses in the age of Big Data and Big Data Analytics (Dewett & Jones, 2001; Pearlson & Saunders,
2001). IT supports and consumes a significant amount
of the resources of enterprises. IT
needs to be managed wisely like other significant
types of business resources such as people, money, and machines. These resources
must return a value to the business. Thus, enterprises must carefully evaluate
its resources including the IT resources that can be efficiently and effectively used.
Information system and technology are now integrated with almost every aspect of
every business. IT and IS play significant roles in business, as it simplifies the
organizational activities and processes.
Enterprises can gain competitive advantages when utilizing appropriate information technology. The inadequate
information system can cause a breakdown in providing services to customers or
developing products which can harm sales and eventually the businesses (Bhatt & Grover, 2005; Brynjolfsson & Hitt,
2000; Pearlson & Saunders, 2001). The same thing applies when inefficient
business processes sustained by ill-fitting information system and technology
as they increase the cost on the business without any return on investment or
value. The lag in the implementation or
poor process adaptation reduce the profits and the growth and can place the
business behind other competitors. The failure of the information system and technology in business is caused primarily
by ignoring them during the planning of the business strategy and
organizational strategy. IT will fail to
support business goals and organizational systems because it was not considered in the business and
organizational strategy. When the business
strategy is misaligned with the
organizational strategy, IT is subject to failure (Pearlson & Saunders, 2001).
IT
Support to Business Goals
Enterprises should invest in IT resources that will benefit them. They should
make investment in systems that
supports their business goals including gaining competitive advantages (Bhatt & Grover, 2005).
Although IT represents a significant
investment in businesses, yet, the poorly
chosen information system can become an obstacle to achieving the business
goals (Dewett & Jones, 2001; Henderson &
Venkatraman, 1999; Pearlson & Saunders, 2001). When the IT does not allow the business to
achieve its goals, or lack the capacity required to collect, store, and
transfer critical information for the business, the results can be disastrous,
leading to dissatisfied customers, or excessive costs for production. The Toys R US store is an excellent example of such an issue (Pearlson & Saunders, 2001).
The well-publicized website was not designed to process and fulfill
orders fast enough. The site could be redesigned with an additional cost which could have been
saved if the IT strategy and business goals were discussed together to
be aligned together.
IT
Support to Organizational Systems
Organizations systems including people,
work processes, and structure represent the core elements of the business. Enterprises should plan to enable these
systems to work together efficiently to achieve the business goals (Henderson & Venkatraman, 1999; Pearlson &
Saunders, 2001; Ryssel, Ritter, & Georg Gemünden, 2004).
When the IT of the business fails to
support the business’ organization systems, the result is a misalignment of the
resources needed to achieve the business goals.
For instance, when organizations decide to use Enterprise Resource
Planning (ERP) system, the system often dictates how many business processes are executed.
When enterprises deploy a technology, they should think through various
aspects such as how the technology will be used
in the organization, who will use it, how they will use it, how to make sure
the application chosen accomplishes what is intended. For instance, an organization which plans to
institute a wide-scale telecommuting program
would need an information system strategy that is compatible with its
organization strategy (Pearlson & Saunders, 2001).
The desktop PCs located within the corporate office are not the right
solution for a telecommuting organization.
Laptop computers application that are
accessible online anywhere and anytime are a most
appropriate solution. If a business only allows the purchase of desktop
PCs and only builds systems accessible from desks within the office, the
telecommuting program is subject to failure. Thus, information systems implementation
should support the organizational systems and should be aligned with the business goals.
Advantages
of IT in Business
Business is able to transform local
business to international business with the advent of information system and
internet (Bhatt & Grover, 2005; Zimmer, 2018).
Organizations are under pressures to take advantages of information
technology to gain competitive advantages.
They are turning to information technology to streamline services and
enhance the performance. IT has become
an essential feature in the landscape of the business that aid business to
decrease the costs, improve communication, develop recognition, and release
more innovative and attractive products.
IT streamlines communication as effective communication is critical to an organization’s success (Bhatt & Grover, 2005; Zimmer, 2018). A key advantage of information system
lies in its ability to streamline communication both internally and
externally. For instance, online meeting
and video conferencing platform such as Skype, WebEx provide business the
opportunity to collaborate virtually in real-time, reducing costs associated
with bringing clients on-site or communicating with staff who work
remotely. IT enables Enterprises to
connect almost effortlessly with international suppliers and consumers.
IT can enhance the competitive advantages
in the marketplace of the business by facilitating strategic thinking and
knowledge transfer (Bhatt & Grover, 2005; Zimmer, 2018).
When using IT as a strategic investment and not as a means to an end, IT
provides business with the tools they need to properly evaluate the market and
implement strategies needed for a competitive edge.
IT stores and safeguards information, as information management is another domain
of IT (Bhatt & Grover, 2005; Zimmer, 2018).
IT is essential to any business that must store and safeguard sensitive
information such as financial data for long periods. Various security techniques can be applied to
ensure the data is stored in a secure
place. Organizations should evaluate the
options available to store their data such as locally using local data center
or cloud-based storage methods.
IT cuts costs and eliminate waste (Bhatt & Grover, 2005; Zimmer, 2018). Although
IT implementation at the beginning will be expensive, in the long run, it
becomes incredibly cost-effective by streamlining the operational and
managerial processes of the business.
Thus, investing in the appropriate
IT is key for a business to gain a return on
investment. For instance, the
implementation of online training programs is a classic example of IT improving
the internal processes of the business by reducing the costs and employees’
time spent outside of work, and travel
costs. Information technology enables
organizations to implement more with less investment without sacrificing
quality or value.
Healthcare Industry Case Study
The healthcare
industry generated extensive data driven
by keeping patients’ records, complying with regulations and policies, and
patients care (Raghupathi & Raghupathi, 2014). The current trend is digitalizing this
explosive growth of the data in the age of Big Data (BD) and Big Data Analytics
(BDA) (Raghupathi & Raghupathi, 2014). BDA has made a revolution in healthcare by
transforming the valuable information, knowledge to predict epidemics, cure
diseases, improve quality of life, and avoid preventable deaths (Van-Dai, Chuan-Ming, & Nkabinde, 2016). Various applications of BDA in healthcare
include pervasive health, fraud detection, pharmaceutical discoveries, clinical
decision support system, computer-aided diagnosis, and biomedical
applications.
Healthcare
sector employs BDA in various aspect of healthcare such as detecting diseases
at early stages, providing evidence-based medicine, minimizing doses of
medication to avoid any side effects, and delivering useful medicine base on genetic analysis. The use of BD and BDA can reduce the
re-admission rate, and thereby the healthcare related costs for patients are reduced.
Healthcare BDA can be used to detect spreading diseases earlier before
the disease gets spread using real-time analytics (Archenaa & Anita, 2015; Raghupathi &
Raghupathi, 2014; Wang, Kung, & Byrd, 2018). Example of the application of BDA in the healthcare system is Kaiser Permanente
implementing a HealthConnect technique to ensure data exchange across all
medical facilities and promote the use of electronic health records (Fox & Vaidyanathan, 2016).
Despite the various
benefits of BD and BDA in the healthcare
sector, various challenges and issues are emerging from the application of BDA
in healthcare. The nature of the
healthcare industry poses challenging to
BDA (Groves, Kayyali, Knott, & Kuiken, 2016). The episodic culture, the data puddles, and
the IT leadership are the three significant
challenges of the healthcare industry to apply BDA. The episodic culture addresses the
conservative culture of the healthcare and the lack of IT technologies mindset
creating rigid culture. Few providers
have overcome this rigid culture and started to use the BDA technology. The
data puddles reflect the silo nature of healthcare. Silo is
described as one of the most significant
flaws in the healthcare sector (Wicklund, 2014). The use of the technology properly is lacking
in healthcare sector resulting in making the industry fall behind other
industries. All silos use their methods to collect data from labs, diagnosis,
radiology, emergency, case management and so forth. The IT leadership is another challenge is caused by the rigid culture of the
healthcare industry. The lack of the
latest technologies among the IT leadership in the healthcare industry is a severe problem.
The
current healthcare data is collected from clinical and non-clinical sources (InformationBuilders, 2018; Van-Dai et al., 2016; Zia & Khan, 2017). The electronic healthcare records are digital
copies of the medical history of the patients.
It contains a variety of data relevant to the care of the patients such
as demographics, medical problems, medications, body mass index, medical
history, laboratory test data, radiology reports, clinical notes, and payment
information. These electronic healthcare records are the most critical data in healthcare data analytics,
because it provides effective and efficient methods for the providers and
organizations to share data (Botta, de Donato, Persico, & Pescapé, 2016; Palanisamy &
Thirunavukarasu, 2017; Van-Dai et al., 2016; Wang et al., 2018).
The biomedical
imaging data plays a crucial role in
healthcare data to aid disease monitoring, treatment planning and
prognosis. This data can be used to
generate quantitative information and make inferences from the images that can
provide insights into a medical condition.
The images analytics is more complicated
due to the noises of the data associated with the images and is one of the significant limitations with biomedical
analysis (Ji, Ganchev, O’Droma, Zhang, & Zhang, 2014; Malik & Sangwan,
2015; Van-Dai et al., 2016).
The sensing data is ubiquitous in the medical domain both for real-time and for historical data analysis. The sensing data involve several forms of medical data collection instruments such as the electrocardiogram (ECG) and electroencephalogram (EEG) which are vital sensors to collect signals from various parts of the human body. The sensing data plays a significant role for intensive care units (ICU) and real-time remote monitoring of patients with specific conditions such as diabetes or high blood pressure. The real-time and long-term analysis of various trends and treatment in remote monitoring programs can help providers monitor the state of those patients with certain conditions(Van-Dai et al., 2016).
The biomedical signals are collected from many sources such as hearts,
blood pressure, oxygen saturation levels, blood glucose, nerve conduction, and brain activity. Examples of biomedical signals include
electroneurogram (ENG), electromyogram (EMG), electrocardiogram (ECG),
electroencephalogram (EEG), electrogastrogram (EGG), and phonocardiogram
(PCG). The biomedical signals real-time
analytics will provide better management of chronic diseases, earlier detection
of adverse events such as heart attacks, and strokes and earlier diagnosis of
disease. These biomedical signals can
be discrete or continuous based on the kind of care or severity of a particular
pathological condition (Malik &
Sangwan, 2015; Van-Dai et al., 2016).
The genomic data
analysis helps better understand the
relationship between various genetic, mutations, and disease conditions. It has
great potentials in the development of various gene therapies to cure certain
conditions. Furthermore, the genomic
data analytics can assist in translating genetic discoveries into personalized
medicine practice (Liang & Kelemen, 2016; Luo, Wu, Gopukumar, & Zhao, 2016;
Palanisamy & Thirunavukarasu, 2017; Van-Dai et al., 2016).
The clinical
text data analytics using the data mining are the transformation process of the
information from clinical notes stored in unstructured data format to useful patterns. The manual coding of clinical notes is costly
and time-consuming, because of their
unstructured nature, heterogeneity, different
format, and context across different patients and practitioners. Various methods such as natural language
processing (NLP) and information retrieval can be used to extract useful
knowledge from large volume of clinical text and automatically encoding
clinical information in a timely manner (Ghani, Zheng, Wei, & Friedman, 2014; Sun & Reddy, 2013; Van-Dai
et al., 2016).
The social
network healthcare data analytics is based
on various kinds of collected social media sources such as social networking
sites, e.g., Facebook, Twitter, Web Logs,
to discover new patterns and knowledge that can be leveraged to model and
predict global health trends such as outbreaks of infections epidemics (InformationBuilders, 2018; Luo et al., 2016; Van-Dai et al., 2016; Zia
& Khan, 2017).
IT
Requirements for Healthcare Sector
The basic
requirement for the implementation of this proposal included not only the tools and required software, but also the
training at all levels from staff, to nurses, to clinicians, to patients. The list of the requirements is divided into system requirement,
implementation requirement, and training
requirements.
Cloud Computing
Technology Adoption Requirement
The volume is
one of the significant characteristics of
BD, especially in the healthcare industry
(Manyika et al., 2011). Based on the challenges addressed earlier
when dealing with BD and BDA in healthcare, the system requirements cannot be
met using the traditional on-premise technology center, as it cannot handle the
intensive computation requirements of BD, and the storage requirement for all
the medical information from various hospitals from the four States (Hu, Wen, Chua, & Li, 2014). Thus, the cloud computing
environment is found to be more appropriate and a solution for the implantation
of this proposal. Cloud computing plays
a significant role in BDA (Assunção, Calheiros, Bianchi, Netto, & Buyya, 2015). The massive computation and storage
requirement of BDA brings the critical need for cloud computing emerging
technology (Mehmood, Natgunanathan, Xiang, Hua, & Guo, 2016). Cloud computing offers various benefits such
as cost reduction, elasticity, pay per use, availability, reliability, and maintainability (Gupta, Gupta, & Mohania, 2012; Kritikos, Kirkham, Kryza, &
Massonet, 2017). However, although cloud computing offers
various benefits, it has security and privacy issues using the standard
deployment models of public cloud, private cloud, hybrid cloud, and community
cloud. Thus, one of the major
requirements is to adopt the Virtual Private Cloud as it has been regarded as the most prominent approach to
trusted computing technology (Abdul, Jena, Prasad, & Balraju, 2014).
Cloud computing
has been facing various threats (Cloud Security Alliance, 2013, 2016, 2017). Records showed that over the last three
years from 2015 until 2017, the number of breaches, lost medical records, and settlements of fines are staggering (Thompson, 2017). The
Office of Civil Rights (OCR) issued 22 resolution agreements, requiring
monetary settlements approaching $36 million (Thompson, 2017). Table 1
shows the data categories and the total for each year.
Table 1. Approximation of Records Lost by Category Disclosed on HHS.gov (Thompson, 2017)
Furthermore, a
recent report published by HIPAA showed the first three months of 2018 experienced
77 healthcare data breaches reported to the OCR (HIPAA, 2018d). In the second quarter of 2018, at least 3.14
million healthcare records were exposed (HIPAA, 2018a). In the third quarter of 2018, 4.39 million
records exposed in 117 breaches (HIPAA, 2018c).
Thus, the
protection of the patients’ private information requires the technology to
extract, analyze, and correlated potentially sensitive dataset (HIPAA, 2018b). The implementation of BDA requires security
measures and safeguards to protect the privacy of the patients in the
healthcare industry (HIPAA, 2018b). Sensitive data should be encrypted to prevent
the exposure of data in the event of theft (Abernathy & McMillan, 2016). The security requirements involve security at
the VPC cloud deployment model as well as at the local hospitals in each State (Regola & Chawla, 2013). The security at the VPC cloud deployment
model should involve the implementation of security groups and network access
control lists to allow access to the right individuals to the right
applications and patients’ records.
Security group in VPC acts as the first
line of defense firewall for the associated instances of the VPC (McKelvey, Curran, Gordon, Devlin, & Johnston, 2015). The network access control lists act as the second
layer of defense firewall for the associated subnets, controlling the inbound
and the outbound traffic at the subnet level (McKelvey et al., 2015).
The security at
the local hospitals level in each State is mandatory to protect patients’
records and comply with HIPAA regulations (Regola & Chawla, 2013). The medical equipment must be secured with
authentication and authorization techniques so that only the medical staff,
nurses and clinicians have access to the medical devices based on their
role. The general access should be prohibited as every member of the hospital has a different role with
different responses. The encryption should be used to hide the
meaning or intent of communication from unintended users (Stewart, Chapple, & Gibson, 2015). The encryption is an essential element in
security control especially for the data in transit (Stewart et al., 2015). The hospital in all four State should
implement the encryption security control
using the same type of the encryption across the hospitals such as PKI, cryptographic application, and cryptography and
symmetric key algorithm (Stewart et al., 2015).
The system
requirements should also include the identity management systems that can
correspond with the hospitals in each state. The identity management system
provides authentication and authorization
techniques allowing only those who should have access to the patients’ medical
records. The proposal requires the
implementation of various encryption techniques such as secure socket layer
(SSL), Transport Layer Security (TLS), and Internet Protocol Security (IPSec)
to protect information transferred in public network (Zhang & Liu, 2010).
Hadoop Implementation
for Data Stream Processing Requirement
While the
velocity of BD leads to the speed of generating large volume of data and
requires speed in data processing (Hu et al., 2014), the variety of the data requires specific technology capabilities to handle
various types of dataset such as structured, semi-structured, and unstructured
data (Bansal, Deshpande, Ghare, Dhikale, & Bodkhe, 2014; Hu et al., 2014). Hadoop ecosystem is found to be the most
appropriate system that is required to implement BDA (Bansal et al., 2014; Dhotre, Shimpi, Suryawanshi, & Sanghati, 2015). The implementation requirements include
various technologies and various tools.
This section covers various components that are required when implementing
Hadoop technology in the four States for healthcare BDA system.
Hadoop has
three significant limitations, which must
be addressed in this design. The first limitation is the lack of technical
support and document for open source Hadoop (Guo,
2013). Thus, this design requires the Enterprise
Edition of Hadoop to get around this limitation using Cloudera, Hortonworks, and MapR (Guo,
2013).
The final decision for which product will be
determined by the cost analysis team.
The second limitation is that Hadoop is not optimal for real-time data
processing (Guo,
2013).
The solution for this limitation will require the integration of real-time
streaming program as Spark or Storm or Kafka (Guo,
2013; Palanisamy & Thirunavukarasu, 2017). This requirement of
integrating Spark is discussed below in a separate requirement for this design (Guo,
2013).
The third limitation is that Hadoop is not a good
fit for large graph dataset (Guo,
2013).
The solution for this limitation requires the integration of GraphLab which is
also discussed below in a separate requirement for this design.
Conclusion
Information
technology (IT) play a significant role
in various industries including the healthcare
sector. This project discussed the IT
role in businesses, the requirement to be aligned with the strategic goal and
organizational system of the business.
If IT systems are not included
during the planning of the business strategy and organizational strategy, the
IT integration into the business at a later
stage is very likely to set for failure.
IT offers various advantages to business including the competitive
advantages in the marketplace.
Healthcare industry is no exception to integrate IT systems. Healthcare sector has been suffering from
various challenges including the high cost of services and inefficient service
to patients. The case study showed the
need for IT systems requirements that can place the industry into competitive
advantages offering better care to patients with low cost. Various IT integrations have been used lately in the healthcare
industry including Big Data Analytics, Hadoop technology, security systems, and
cloud computing. Kaiser Permanente, for instance, applied Big Data Analytics
using HealthConnet to provide care to
patients with lower cost and better care, which are
aligned with the strategic goal of
its business.
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The purpose of this project is to
discuss critical information technology solutions used to gain competitive
advantages. The discussion begins with
Big Data and Big Data Analytics addressing essential topics such as the Hadoop ecosystem, NoSQL databases, Spark
integration for real-time data processing, and Big Data Visualization. Cloud computing is an emerging technology to solve
Big Data challenges such as storage for the large volume of the data, and the
high-speed data processing to extract value from data. Enterprise Resource Planning (ERP) is a
system that can aid organizations to gain competitive advantages if implemented
right. The project discusses various
success factor for the ERP system. Big Data plays a significant role in ERP,
which is also discussed in this
project. The last technology addressed
in this project is the Customer Relationship Management (CRM), its building
blocks and integration. The project
addresses the challenges and costs associated with CRM. The best practice of CRM is addressed which can assist in the successful implementation of CRM. In summary, enterprises should evaluate
various information technology systems that are developed to aid them to gain
competitive advantages.
Keywords: Big
Data Analytics; Cloud Computing; ERP; CRM.
Enterprises
should evaluate various information technologies to gain competitive advantages
in the market. Big Data and Big Data
Analytics are one of the significant topics in information technology
and computer science. Cloud computing is
another critical topic in the same domains, as cloud computing emerged to solve
the challenge of Big Data. Thus, this
project begins with these top information technologies. The discussion covers various major topics in
Big Data such as the Hadoop ecosystem,
Spark for real-time processing. The
discussion of the cloud computing covers the various service models and
deployment models which cloud computing offers.
The most common
business areas that require information technology support include Enterprise
Resource Planning (ERP), Customer Relationship Management (CRM), Product Life
Cycle Management (PLM), Supply Chain Management (SCM), and Supplier
Relationship Management (SRM) (DuttaRoy, 2016). Thus, this project discusses ERP and CRM as
additional critical information technology systems that aid Enterprises gain competitive advantages.
Big Data is
now the buzzword in the field of computer
science and information technology. Big
Data attracted the attention of various sectors, researchers, academia,
government and even the media (Géczy, 2014; Kaisler, Armour, Espinosa, & Money,
2013). In the 2011 report of the International Data
Corporation (IDC), it is reporting that the amount of the information which
will be created and replicated will exceed 1.8 zettabytes which are 1.8 trillion gigabytes in 2011. This amount
of information is growing by a factor of 9 in just five years (Gantz & Reinsel, 2011).
BD and BDA are
terms that have been used interchangeably
and described as the next frontier for innovation, competitions, and productivity (Maltby, 2011; Manyika et al., 2011). BD has a multi-V model with unique
characteristics, such as volume referring to the large dataset, velocity refers to the speed of the computation as well
as data generation, and variety referring to the various data types such as
semi-structured and unstructured (Assunção, Calheiros, Bianchi, Netto, & Buyya, 2015; Hu, Wen, Chua,
& Li, 2014). BD is
described as the next frontier for competition, innovation, and productivity. Various industries have taken this
opportunity and applied BD and BDA in their business models (Manyika et al., 2011). There are many technologies such
as Cloud Computing, Hadoop Map/Reduce
Hive, and others have emerged to deal with the phenomena of the Big Data. Data without analysis has no value to
organizations.
While the velocity of BD leads to the speed of generating large volume of data and requires speed in data processing (Hu et al., 2014), the variety of the data requires specific technology capabilities to handle various types of dataset such as structured, semi-structured, and unstructured data (Bansal, Deshpande, Ghare, Dhikale, & Bodkhe, 2014; Hu et al., 2014). Hadoop ecosystem is found to be the most appropriate system that is required to implement BDA (Bansal et al., 2014; Dhotre, Shimpi, Suryawanshi, & Sanghati, 2015). Hadoop technologies have been in the front-runner for Big Data application (Bansal et al., 2014; Chrimes, Zamani, Moa, & Kuo, 2018). Hadoop ecosystem will be part of the implementation requirement as it is proven to serve well with intensive computation using large datasets (Raghupathi & Raghupathi, 2014; Wang, Kung, & Byrd, 2018). The Hadoop version that is required is version 2.x to include YARN for resource management (Karanth, 2014). Hadoop 2.x also include HDFS snapshots to provide a read-only image of the entire or a particular subset of a filesystem to protect against user errors, backup, and disaster recovery (Karanth, 2014). The Hadoop platform can be implemented to gain more insight into various areas (Raghupathi & Raghupathi, 2014; Wang et al., 2018). Hadoop ecosystem involves Hadoop Distributed File System, MapReduce, and NoSQL database such as HBase, and Hive to handle a large volume of dataset using various algorithms and machine learning to extract values from the medical records that are structured, semi-structured, and unstructured (Raghupathi & Raghupathi, 2014; Wang et al., 2018). Other components to support Hadoop ecosystem include Oozie for workflow, Pig for scripting, and Mahout for machine learning which is part of the artificial intelligence (AI) (Ankam, 2016; Karanth, 2014). Hadoop ecosystem includes other tools such as Flume for log collector, Sqoop for data exchange, and Zookeeper for coordination (Ankam, 2016; Karanth, 2014). HCatalog is a required component to manage the metadata in Hadoop (Ankam, 2016; Karanth, 2014). Figure 1 shows the Hadoop ecosystem before integrating Spark for real-time analytics.
In the age of BD
and BDA, the traditional data store is found inadequate to handle not only the
large volume of the dataset but also the various types of the data format such
as unstructured and semi-structured (Hu et al., 2014). Thus,
Not Only SQL (NoSQL) database is emerged to meet the requirement of the
BDA. These NoSQL data stores are used for modern, and scalable databases (Sahafizadeh & Nematbakhsh, 2015). The scalability feature of the NoSQL data
stores enables the systems to increase the throughput when the demand increases
during the processing of the data (Sahafizadeh & Nematbakhsh, 2015). The platform can incorporate two scalability
types to support the large volume of the datasets; the horizontal and vertical scalability. The horizontal scaling allows the
distribution of the workload across many servers and nodes to increase the
throughput, while the vertical scaling requires more processors, more memories
and faster hardware to be installed on a
single server (Sahafizadeh & Nematbakhsh, 2015).
NoSQL data stores have various types such as MongoDB, CouchDB, Redis, Voldemort, Cassandra, Big Table, Riak, HBase, Hypertable, ZooKeeper, Vertica, Neo4j, db4o, and DynamoDB. These data stores are categorized into four types: document-oriented, column-oriented or column-family stores, graph database, and key-value (EMC, 2015; Hashem et al., 2015). The document-oriented data store can store and retrieve collections of data and documents using complex data forms in various formats such as XML and JSON as well as PDF and MS word (EMC, 2015; Hashem et al., 2015). MongoDB and CouchDB are examples of document-oriented data stores (EMC, 2015; Hashem et al., 2015). The column-oriented data store can store the content in columns aside from rows with the attributes of the columns stored contiguously (Hashem et al., 2015). This type of datastore can store and render blog entries, tags, and feedback (Hashem et al., 2015). Cassandra, DynamoDB, and HBase are examples of column-oriented data stores (EMC, 2015; Hashem et al., 2015). The key-value can store and scale large volumes of data and contains value and a key to access the value (EMC, 2015; Hashem et al., 2015). The value can be complicated, but this type of data stores can be useful in storing the user’s login ID as the key referencing the value of patients. Redis and Riak are examples of the key-value NoSQL data store (Alexandru, Alexandru, Coardos, & Tudora, 2016). Each of these NoSQL data stores has its limitations and advantages. The graph NoSQL database can store and represent data using graph models with nodes, edges, and properties related to one another through relations which will be useful for unstructured medical data such as images, and lab results. Neo4j is an example of this type of graph NoSQL database (Hashem et al., 2015). Figure 2 summarizes these NoSQL data stores, data types for storage, and examples.
Figure 2. Big Data Analytics NoSQL
Data Store Types.
While the architecture of Hadoop ecosystem has been designed in various scenarios for data storage, data management statistical analysis, and statistical association between various data sources distributed computing and batch processing, businesses requires real-time data processing to gain competitive advantages. However, the real-time data processes cannot be met by Hadoop alone (Basu, 2014). Real-time analytics will tremendous value to the healthcare proposed system. Thus, Apache Spark is another component which is required for real-time data processing. Spark allows in-memory processing for fast response time, bypassing MapReduce operations (Basu, 2014). With Spark integration with Hadoop, stream processing, machine learning, interactive analytics, and data integration will be possible (Scott, 2015). Spark will run on top of Hadoop to benefit from YARN and the underlying storage of HDFS, HBase and other Hadoop ecosystem building blocks (Scott, 2015). Figure 3 shows the core engines of the Spark.
Visualization is
one of the most powerful presentations of
the data (Jayasingh, Patra, & Mahesh, 2016). It helps in viewing the data in a more
meaningful way in the form of graphs, images, pie charts that can be understood
easily. It helps in synthesizing a large
volume of data set such as healthcare data to get at the core of such raw big data and convey the key points
from the data for insight (Meyer, M., 2018). Some of
the commercial visualization tools include Tableau, Spotfire, QlikView, and
Adobe Illustrator. However, the most
commonly used visualization tools in healthcare include Tableau, PowerBI, and
QlikView.
Numerous studies
discussed and addressed the definition of cloud computing, as it was not well
defined (Foster, Zhao, Raicu, & Lu, 2008). As an effort
to identify precisely the term cloud computing IT practitioners, the
academics and research community came up with various definitions. (Vaquero, Rodero-Merino, Caceres, & Lindner, 2008) suggested twenty-two
definitions to cloud computing from different research studies. The underlying concepts of cloud computing
rely heavily on providing computing
power, storage services, software services, and platform services on demand to
customers over the internet (Lewis, 2010). The access to cloud computing services can
scale up or down as needed, and the
consumers use the pay-per-use or
pay-as-you-go model (Armbrust et al., 2009; Lewis, 2010).
The
National Institute of Standards and Technology (NIST)
proposed an official definition of cloud
computing. Cloud computing enables ubiquitous, convenient, on-demand
network access to a shared pool of configurable computing resources such as
network, servers, storage, applications, and services. Organizations can quickly provision and release these resources with
minimal effort of management or interaction from a service provider (Mell & Grance, 2011).
The
essential characteristics of cloud computing technology identified by NIST include
on-demand self-service, broad network access, resource pooling, rapid
elasticity, and measured service (Mell & Grance, 2011). The on-demand self-service feature provides cloud consumers
the computing capabilities such as server time and network storage as needed
automatically eliminating the need for any human interaction with a service
provider. The broad network access
feature provides capabilities to cloud consumers over the network and the use
of various devices such as mobile phones, and tablets from anywhere enabling
the heterogeneous client platforms. The resource pooling feature provides a
multi-tenant model that serve multiple consumers sharing the pool of
resources. This feature provides location independence, where the consumers do not know the exact location of the provided
resources. The consumer may be able to
specify the location at a higher level of abstraction such as country, state,
or datacenter (Mell & Grance, 2011). The rapid elasticity feature provides
capabilities to scale horizontally and vertically to meet the demand. The measured services feature enables the measurement of the consumption of resources
such as processing, storage, and bandwidth. The resource utilization can be
monitored, controlled, and reported, providing transparency for both the
provider and consumer of the utilized services (Mell & Grance, 2011).
Cloud
computing offers three essential service models as Infrastructure-as-a-Service
(IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) (Mell & Grance, 2011). The IaaS layer provides the capability to the consumers to
provision storage, processing, networks, and other fundamental computing
resources. Using IaaS, the consumer can deploy and run arbitrary software,
which can include operating systems and application. When using IaaS, the users do not manage or control the underlying infrastructure of the
cloud. The consumers have control over
the storage, the operating systems, and the deployed application and limited
control of some networking components such as host firewall. The PaaS allows the cloud computing consumers
to deploy applications that are created using programming languages, libraries,
services, and tools supported by the providers.
Using PaaS, the cloud computing consumers
do not manage or control the underlying infrastructure of the cloud
including network, servers, operating systems, or storage. The consumers have control over the deployed
applications and possibly configuration settings for the application-hosting
environment. The SaaS allows cloud
computing consumers to use the provider’s applications running on the
infrastructure of the cloud. The SaaS service model consumers can access the
applications from various client devices through either a thin client interface,
such as a web-based email from a web browser, or a program interface. The SaaS consumers do not control or manage the underlying infrastructure of the cloud such as network,
operating systems, storage, or even individual application capabilities, with the
possible exception of limited user-specific application configuration settings (Mell & Grance, 2011).
Cloud computing offers four essential deployment models known as public cloud, private cloud, community cloud, and hybrid cloud (Mell & Grance, 2011). The public cloud reflects the infrastructure of the cloud available to the general public. It can be managed, owned and operated by organizations, academic entities, government entities, or a combination of them. This deployment model resides on the premises of the cloud provider. The private cloud is the cloud infrastructure designed exclusively for a single organization. This deployment model can be managed, owned and operated by the organization, or a third party or a combination of both. This model may reside either on-premises or off-premises. The community cloud is the cloud infrastructure designed exclusively for a specific community of consumers from organizations that have such as security requirement, compliance consideration, and policy. One or more of organizations in the community, a third party or some combination of them can manage, own, operate the community cloud. The community cloud can reside on-premises or off-premises. The hybrid cloud is the cloud infrastructure combining two or more cloud infrastructures such as private, public, or community (Mell & Grance, 2011). Figure 4 presents the full representation of cloud computing technology per NIST including the standard service models, deployment models, and essential characteristics.
Figure 4. Overview of Cloud Computing based on NIST’s
Definitions.
Cloud Computing Role in Big Data and Big Data Analytics
Cloud computing plays a significant role in BDA (Assunção et al., 2015). The massive computation and storage requirement of BDA brings the critical need for cloud computing emerging technology (Mehmood, Natgunanathan, Xiang, Hua, & Guo, 2016). Cloud computing offers various benefits such as cost reduction, elasticity, pay per use, availability, reliability, and maintainability (Gupta, Gupta, & Mohania, 2012; Kritikos, Kirkham, Kryza, & Massonet, 2017). However, although cloud computing offers various benefits, it has security and privacy issues using the standard deployment models of public cloud, private cloud, hybrid cloud, and community cloud.
American
Production and Inventory Control Society (2001), as cited in (Madanhire & Mbohwa, 2016) defined ERP as a method for
the effective planning and controlling of all resources needed to take, make,
ship and account for customer orders in a manufacturing, distribution or
service organization. This functions
integration can be achieved through a
software package solution offered by vendors to support the seamless
integration of all information flowing through the enterprise, such as
financial, accounting and human resources.
ERP is a business management
software that is designed to integrate
data sources and processes of the entire organization into a combined system (Bahssas, AlBar, & Hoque, 2015).
ERP system is a
popular solution which is used by the organization
to integrate and automate various processes, performance improvements, and cost
reduction. ERP provides business with a real-time view of its core business processes
such as production, planning, manufacturing, inventory management and
development (Bahssas et al., 2015). The ERP software is a
multi-module application that integrates
activities across functional departments such as production, planning,
purchasing, inventory control, product distribution, and order tracking. It
allows the automation and integration of business process by enabling data and
information sharing to reach best practices in managing the process of the
business.
ERP involves
various modules such as accounting, finance, supply chain, human resources,
customer information and others (Bahssas et al., 2015; Madanhire & Mbohwa, 2016). ERP production planning module is used to
optimize the utilization of manufacturing capacity, parts, components, and material resources. ERP purchases module is used to streamline
procurement of required raw materials, as it automates the process of
identifying potential suppliers, negotiating
prices, placing orders to suppliers and related billing processes. ERP inventory control module is used to facilitate the process of maintaining an appropriate level of stocks in the warehouse
through identifying inventory requirements, setting targets, providing
replenishment techniques and options, monitoring item usage, reconciling
inventory balances and reporting inventory status. ERP sales module is used for order placement, order scheduling, shipping and
invoicing. ERP marketing module is used to support lead generation, direct mailing campaign. ERP financial module is used to gather
financial data from various departments and generate reports such as balance
sheet, general ledger, trial balance.
ERP human resources (HR) module is used to maintain a complete employee database to include contact information, salary
details, attendance and so forth (Madanhire & Mbohwa, 2016).
Innovations in technology trends have forced ERP designers to establish new
development. Thus, new ERP system
designs are implemented to satisfy organizations and customers by evolving new
ERP business models. Furthermore, one of
the biggest challenges for ERP is to keep
speed with the manufacturing sector which has been moving rapidly from product-centric to customer-centric focus (Bahssas et al., 2015). Most ERP vendors are required to add a
variety of functions and modules to their
core systems.
The
implementation of ERP systems is costly, and
organizations should be careful when implementing it to ensure its
success. Some believe that ERP systems
could hurt their business because of the potential problems of ERP (Umble, Haft, & Umble, 2003). Various studies identified
success factors for ERP. (Umble et al., 2003) addressed
the most prominent factors for successful implementation of ERP. The first
critical success factor is that organizations should have a clear understanding of the strategic
goals. The commitment by top management
is another success factor. Successful
ERP implementation requires excellent project management. The existing organizational structure and processes found in
most enterprises are not compatible with the structure, tools, and types of
information provided by ERP systems.
Thus, organizational change management is required to ensure the successful implementation of ERP. ERP implementation teams should be composed
of highly skilled professionals that are chosen
for their skills, past accomplishments, reputation, and flexibility. Data accuracy is another success factor for
ERP implementation. The education and
training are another success factor for
the implementation of the ERP
system. (Bahssas et al., 2015) Indicated
that reserving 10-15% of the total ERP implementation budget for training will
give an organization an 80% chance of successful implementation. Focused performance measures must be included
from the beginning of the implementation because if the system is not associated with compensation, it will
not be successful.
Big Data Analytics plays a
significant role in ERP applications (Carlton, 2014; ERP Solutions, 2018; Woodie, 2016). Enterprise data comprises various departments
such as HR, finance, CRM and other essential business functions of a
business. This data can be leveraged to
make ERP functionality better. When Big
Data tools are brought together with the ERP
system, can
unfold valuable insights that can businesses make smarter decisions (Carlton, 2014; Cornell University, 2017; Wailgum,
2018). Many ERP
systems fail to make use of real-time inventory and supply chains data because these systems lack the
intelligence to make predictions about products demands (Carlton, 2014; ERP Solutions, 2018). Big Data tools can predict
demand and help determine what company needs to go forward (ERP Solutions, 2018).
Infor co-president Duncan Angove established Dynamic Science Labs (DSL)
aiming to use data science techniques to solve a particular class of business problems for its customers. Employees
with big data, math, and coding skills were hired in Cambridge, Massachusetts-based
organization to develop proof of concept (POC) (Woodie, 2016).
Big Data systems such as Apache’s Hadoop are creating node-level
operating transparencies which affect nearly every current ERP module in
real-time (Carlton, 2014).
Managers will be able to quickly leverage ERP Big Data capabilities,
thereby enhancing information density and speeding up overall decision-making.
In brief, Big Data and Big Data Analytics impact
business at all levels, and ERP is no
exception.
Customer
Relationship Management (CRM) systems assist organizations to manage customer
interaction and customer data, automate marketing, sales, and customer support, assess business
information and managing partner, vendor,
and employee relationships. A quality
CRM system can be scalable to serve the needs of small, medium or large
business (Financesonline, 2018). CRM systems can be customized to allow business is taking actionable customer insights
using back-end analytics, identify opportunities with predictive analytics,
personalize customer support, and streamline operations based on the history of
the customers’ interaction with the business.
Organizations must be aware of the CRM system software available to
select the most appropriate CRM system that can
better serve their needs.
Various reports
identified various CRM systems. The best
CRM systems include Salesforce CRM, Hubspot CRM, Fresh sales, Pipedrive, Insightly, Zoho CRM, Nimble, PipelineDeals,
Nutshell CRM, Microsoft Dynamics CRM, SalesforceIQ, Spiro, and
ExxpertApps. Table 1 shows the best CRM
systems available in the market.
Table 1. CRM Systems (Financesonline, 2018).
Customer
satisfaction is the critical element to
the success of the business (Bygstad, 2003; Pearlson & Saunders, 2001). Businesses need to continuously satisfy
customers, understand their needs and expectations, provide high-quality
products or service at a competitive price to maintain success. These interactions needed to be tracked by the business and analyzed in an organized
way to foster long-lasting customer relationships which get transformed into long-term success.
CRM can aid
business increase sales efficiency, drive the satisfaction of customers,
streamline the process of the business and make it more efficient, and identify and resolve bottlenecks at any of the
operational processes from marketing,
sales to the product development (Ahearne, Rapp, Mariadoss, & Ganesan, 2012; Bygstad, 2003). The development of customer relationship is
not a trivial or straightforward task.
When it is done right, it places the
business in a competitive edge. However, the implementation of CRM is
challenging.
The implementation of
CRM demonstrates the value of customers to the business
and placing customer service on top priority
(Pearlson & Saunders, 2001). CRM plays a significant role in collaborating
the effort between customer service, marketing, and
sales in an organization. However, the
implementation of CRM is challenging especially for small business and
startups. Various reports addressed
various challenges when implementing CRM.
The cost is the most significant
challenges organizations are confronted
with when implementing the CRM solution (Sage Software, 2015). The development of a clear objective to
achieve with the CRM system is another challenge when implementing CRM. Organizations are confronted with the type of
deployment whether it should be on-premise or cloud-based CRM. Other challenges involve the employees’
training, the right CRM solution provider and the integration plan in advance (Sage Software, 2015).
The cost of CRM systems
varies from one vendor to another based on the features and deployment key such
as data importing, analytics, email integrations, mobile accessibility, email
marketing, multi-channel support, SaaS platform, on-premise platform, and SaaS
and on-premise. Some vendors offer CRM
for small and medium, or small only, while others offer CRM systems for small,
medium and large businesses. In a report
by (Business-Software, 2019), the cost is categorized for more expensive to least
expensive using the dollar sign as $$$$ for most expensive, $$$ for expensive,
$$ for less expensive and $ for least expensive. Each vendor CRM system has certain features
which must be examined by organizations before making
the decision to adopt such a system.
Table 2 provides an idea about the cost from the most expensive, expensive, less expensive, to least expensive.
Table 2. CRM System Costs based on the Report by (Business-Software, 2019).
Understanding the
buildings blocks of the CRM system can assist in the implementation and
integration of CRM systems. CRM involves
four core building blocks (Meyer, Matthias & Kolbe, 2005). The acquirement and
continuous update of the knowledge base on the needs of customers, motivations,
and behavior over the lifetime of the
relationship with customers. The
application of the customers’ knowledge to continuously improve performance
through a process of learning from success and failures is the second building
block of CRM system. The integration of
marketing, sales, and service activities to achieve a common goal is another
building block of the CRM system. The
last building block of the CRM system
involves the implementation of appropriate systems to support customer
knowledge acquisition, sharing, and the measurement of CRM effectiveness.
CRM integration is a
critical building block for CRM success (Meyer, Matthias, 2005). The process of integrating CRM involves various organizational and operational
functions of the business such as marketing, sales and service activities. CRM requires detailed business processes
which can be categorized into three core
elements; CRM delivery process, CRM support process, and CRM analysis
process. The delivery process involves
direct contact with customers to cover part of the customer process such as
campaign management, sales management, service management, and complaint
management. The support process involves direct contact with the customer that are not designed to fulfill
supporting functions within the CRM context such as market research and loyalty
management. The analysis process
consolidates and analyzes the knowledge
of customers collected in other CRM processes.
The result of this analysis process is passed to the delivery process,
support process and to the service innovation and service production processes
to enhance their effectiveness such as customer scoring and lead management,
customer profiling and segmentation, feedback and knowledge management.
Various studies
and reports addressed best practices in the implementation and integration of CRM
systems into the business (Salesforce, 2018; Schiff, 2018). Organizations must choose a CRM that fits
their needs. Not every CRM is created equally, and if organizations choose
a CRM system without properly researching its features, capabilities, and weaknesses, organizations could end up committed to a system that is not
appropriate for the business, and as a result,
could lose money. Organizations should
decide whether CRM should be cloud-based
or on-premise base CRM (Salesforce, 2018; Schiff, 2018; Wailgum, 2008). Organizations should decide whether CRM
should be a service contract or one that
costs more upfront to install. Business
should also decide whether it needs in-depth, highly customizable features, or
basic functionality will be sufficient to serve the needs of the business. Organizations should analyze the options and
decide on the CRM system that is most appropriate for the business which can
serve the needs to build strong customer relationship and gain a competitive edge in the market.
Well-trained
personnel and workforce will help organizations achieve its strategic CRM goal.
If organizations do not invest in the
training of the workforce on how to utilize the CRM system, CRM tools will become useless.
The CRM systems become effective as organizations allow them to be. When
the workforce is not using the CRM system to its full potentials, or if the
workforce is misusing the CRM systems,
CRM will not perform its functions properly and will not serve the needs of the
business as expected (Salesforce, 2018; Schiff, 2018).
Automation is another critical factor for best practice when
implementing CRM systems. Tasks that are associated with data entry can be automated
so that CRM systems will be up to date.
The automation will increase the efficiency of the CRM systems as well
as the business overall (Salesforce,
2018; Schiff, 2018).
One of the significant benefits of
CRM is its potential in improving and enhancing the cooperative efforts across
departments of the business. When the
same information is accessible across various departments, CRM systems
eliminate confusions that can be caused by using different terms and different
information. Data without analysis is
not meaningless. Organizations should
consider mining the data to get the value
that can aid in making sound business decisions. CRM systems are designed to capture and
organize massive amounts of data. If
organizations do not take advantages of this massive amount of data to turn it
into actionable data, the implementation of CRM will be so limited. The
best CRM systems are those that come with built-in analytics features which use
advanced programming to mine all captured data and use that information to
produce valuable conclusions which can be used
for future business decisions. When
organizations take advantages of the CRM built-in analytical feature and analyze the data that CRM system
procures, the valuable information can provide insight for business decisions (Salesforce,
2018). The last element for best practice of the
implementation of CRM is for organizations to keep it simple. The best CRM
system is the one that will best fit the needs and requirements of the
business. The simplicity is a crucial
element when implementing CRM.
Organizations should implement CRM that is not complex while it is useful and provides everything the business
needs. Organizations should also
consider making changes to the CRM policies where necessary. The effectiveness of day-to-day operations will
be the best indicator of whether the CRM performs as expected, and if it is
not, some changes must be made until it
performs as expected (Salesforce,
2018; Wailgum, 2008).
This project
discussed critical information technology solutions used to gain competitive
advantages. The discussion began with
Big Data and Big Data Analytics addressing essential topics such as the Hadoop ecosystem, NoSQL databases, Spark
integration for real-time data processing, and Big Data Visualization. Cloud computing is an emerging technology to solve
Big Data challenges such as storage for the large volume of the data, and the
high-speed data processing to extract value from data. Enterprise Resource Planning (ERP) is a
system that can aid organizations to gain competitive advantages if implemented
right. The project discussed various
success factor for the ERP system. Big Data plays a significant role in ERP,
which is also discussed in this
project. The last technology addressed
in this project is the Customer Relationship Management (CRM), its building
blocks and integration. The project
addressed the challenges and costs associated with CRM. The best practice of CRM is addressed which can assist in the successful implementation of CRM. In summary, enterprises should evaluate
various information technology systems that are developed to aid them to gain
competitive advantages.
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The purpose of this discussion is to
address two good-quality research papers on customer relationship management
(CRM). The chosen articles for this
discussion are (Ngai, Xiu, & Chau, 2009;
Rygielski, Wang, & Yen, 2002). The reason for
selecting these two papers is that they discuss CRM in the context of business
intelligence and data mining.
The first journal (Rygielski et al., 2002) is about data mining techniques for CRM. The authors discussed various aspects of the
CRM as well as data mining. They also
discussed the importance of understanding the customers’ lifecycle and the data
mining techniques that can be used to extract value from the customers’ data. Various data mining techniques are discussed and their application with CRM.
The second journal (Ngai et al., 2009) is about the application of data mining techniques in CRM,
and a literature review and classification.
The authors identified nine hundred articles to the application of data
mining techniques to CRM. Seven data
mining techniques are identified to include association, classification,
clustering, forecasting, regression sequence discovery, and visualization. The
authors indicated that classification and association models are the two
commonly used models for data mining in CRM.
Four CRM dimensions are identified
as customer identification, customer attraction, customer retention, and customer development.
Customer Relationship Management (CRM)
(Rygielski et al., 2002) defined CRM using
four elements of a simple framework; know, target, sell and service. CRM includes a set of processes and enabling
systems to support the enterprise strategy
to develop long term, profitable relationships with specified customers (Ngai et al., 2009). The foundation for
successful CRM strategy involves the customers’ data and information technology
tools. The rapid growth of the internet
and the emerging technologies increased the opportunities for marketing and
transformed the way relationship between
business and customers are managed (Ngai et al., 2009).
Enterprises are required to know and
understand its market and customers, which involve detailed customer
intelligence to select the most profitable customers and identify those no
longer worth targeting (Ngai et al., 2009; Rygielski
et al., 2002). The target entails the products to be sold to
certain customers through specific channels. The selling element of CRM requires
enterprises to campaign management to increase the effectiveness of the
marketing department. Enterprises seek
to retain their customers through services such as call center and help
desk.
CRM Old Model and Relationship Marketing
Technology plays a significant role
in marketing. Relationship marketing has become a reality due to the technology
application and the advancement in technology (Ngai et al., 2009; Rygielski et al., 2002). Various enterprises
and businesses gained competitive advantages due to the application of
technologies such as business intelligence, data mining, data warehouse. Data mining technique assists organizations
to extract value from the data. When
organizations apply data mining techniques, they can determine valuable
customers and predict hidden behaviors and allowing businesses to make
proactive knowledge-driven decisions. Data
mining provides automated and future-oriented analysis which is beyond the past
events that are based on historical data (Rygielski et al., 2002).
The old model of ‘design-build-sell’
which is a product-oriented view is being replaced by ‘sell-build-redesign
which is a customer-oriented view (Rygielski et al., 2002). The new approach to
one-to-one marketing challenged the traditional process of mass-marketing. The marketing goal of the traditional
approach is to reach more customers and expand the customer base.
Two-Stage CRM Concepts
Customer Focus: The
first stage is to master the basics of building and developing customer focus.
This concept shifts the focus from product orientation to customer orientation and define market strategy
from outside-in and not from inside-out.
The focus should be on the needs of customers and not on the product’s features (Rygielski et al., 2002).
CRM Integration: The
second stage goes beyond the basics by integrating CRM across the entire
customer experience chain, leveraging technology to achieve real-time customer
management, and continuously innovating
the value proposition to customers (Rygielski et al., 2002).
CRM Components
Customer Data: CRM
involves several components. Enterprises
must first process customer information before the process of CRM begins. Customers data can be collected through internal customer data or external sources.
Customer internal data sources include summary tables that describe
customers via billing records, customer surveys of a subset of customers who
answer the detailed question, and
behavioral data contained in transaction systems such as weblogs, credit card records and so forth (Rygielski et al., 2002).
Data Warehouse: Data
warehouse is a critical component for a successful
CRM strategy. Data required for CRM can
be limited to a marketing data mart with limited feeds from other corporate
systems. External data sources can be a key
source for gaining customer knowledge advantage. These external data sources
include lookups for current address and phone, household hierarchies,
Fair-Isaacs Corp (FICO) credit scores, and webpage viewing profiles (Rygielski et al., 2002).
Analytical Tools: CRM
system must analyze the data using statistical tools, OLAP and data
mining. Marketing professionals are
required to understand the customer data and business imperative whether the
enterprise uses the traditional statistical techniques or one of the data
mining software tools. Enterprises should employ data mining analysts who will
be involved in the analysis and make sure the business does not lose sight of
the original reason for implementing the data mining technique. The
segmentation of the market is the result,
and decisions are made regarding which
segments are attractive (Rygielski et al., 2002).
Campaign Execution and Tracking: Enterprises should execute campaigns and track the
results. Campaign management software
manages and monitors the communications of customers across multiple
touchpoints such as direct mail, telemarketing, customer service,
point-of-sale, email, and the web.
People and processes contribute to facilitating
the interaction between marketing, information technology and sales channels (Rygielski et al., 2002).
Data Mining and Knowledge Discovery
Data mining is defined as a sophisticated data search capability using
statistical algorithms to discover correlations and patterns in data (Rygielski et al., 2002). The term data
mining is an analogy to the gold or coal
mining, indicating that data nuggets are buried
in the large volume of the corporate data warehouses, or information dropped on
a website, most of which can lead to better understanding and use of the
data. Data mining approach is
complementary to other analysis techniques such as statistics, on-line analytical
processing (OLAP), spreadsheets, and necessary
data access. In summary, data mining is
another approach to find meaning and value in the data that can aid enterprises
to make better strategic and tactic decisions (Ngai et al., 2009; Rygielski
et al., 2002).
When organizations apply data mining
techniques, they can discover patterns and relationships hidden in the data.
This process of discovering patterns and relationships is part of a more extensive process known as ‘knowledge
discovery” (Rygielski et al., 2002). The process of knowledge discovery describes the required
steps to ensure meaningful output. Data
mining does not eliminate the need for organizations to understand the data and
basic statistical methods. Data mining
does not find patterns or relationships that can be trusted blindly without
verification. The result must be verified.
Data mining assists in generating hypotheses. However, data mining does
not validate these hypotheses.
Data Mining Evolution and Building Blocks
Data mining evolved through four significant phases from the 1960s to 1980s, to 1990s, and 2000s (Rygielski et al., 2002). Data mining began
with the data collection in the 1960s for
simple calculations such as summations and average. The information at this phase answered
business questions related to figures derived from data collection sites, such
as the total revenue, or average total revenue over a specified period. Specific application programs were created for collecting data and
calculations. Data access is the second
data mining generation phase in the 1980s,
where databases were used to store data in a structured format. Organizations
were able to query the database to access certain
data for a specific period. In the 1990s,
data navigation phase began as a logical step after the data access where
organizations could obtain either a global view or drill down to a particular
site for comparison with its peers. In the
2000s, data mining phase began with the online analytic tools for real-time feedback
and information exchange with collaborating business units.
The primary building blocks of data mining have been developing for decades. These building blocks include statistics, artificial intelligence, and machine learning (Rygielski et al., 2002). These data mining core components are mature. When integrating these building blocks of the data mining with a relational database, they develop a business environment which can capitalize on knowledge previously buries within the systems. Figure 1 shows the core components of data mining.
Figure 1. Core Components of Data
Mining.
Data Mining Core Process
When using data mining, the data is formed and constructed into a model. The model describes patterns and relationships derived from the data. The implementation of data mining involves three general processes. The discovery phase is the process of looking in the database to find hidden patterns without pre-determined hypotheses about the patterns. The predictive phase is the process of taking the discovered pattern and using them for future prediction. The forensic analysis is the process of applying the extracted patterns to find anomalous or unusual data elements (Rygielski et al., 2002). Figure 2 illustrates these three essential processes.
Figure 2. Data Mining Three Core
Processes (Rygielski et al., 2002).
Data Mining Models and Benefits
Data mining has six types of data
models to solve various types of business problems; classification, regression,
association analysis, sequence discovery, clustering (Ngai et al., 2009; Rygielski
et al., 2002),
time series (Rygielski et al., 2002), and visualization (Ngai et al., 2009). Classification and
regressions are used to make predictions, while association and sequence
discovery is used to describe
behavior. Clustering model can be used for either forecasting or description. Prediction and descriptive data mining are used for retail, banking,
telecommunication, and other applications.
In the retail sector, retailers can
keep detailed records of every shopping transactions via store-branded credit
cards and point-of-sale systems. Retailers can better understand the various
customer segments. Retail applications
include performing basket analysis, sales forecasting, database marketing,
merchandise planning and allocation (Rygielski et al., 2002). The banking sector
can deploy knowledge discovery for
various applications such as card marketing, cardholder pricing and
profitability, fraud detection, and predictive life-cycle management. The telecommunications sector can utilize
knowledge discovery for various applications such as call detail record
analysis, and customer loyalty. Other knowledge discovery applications are
emerging in a variety of sectors such as customer segmentation, manufacturing,
warranties, and frequent flier incentives. For the forensic analysis, banks and
financial entities can use it for fraud detection to analyze the abnormalities
in the data.
Enterprises can integrate data
mining into the decision-making process. However, data mining implementation
requires skill sets and technology. While data mining is frequently implemented at the regional or central organization,
front line management and operations should have the knowledge gained through
the data mining. The communication of
this knowledge gained through data mining can be through an algorithm for
scoring, a score or a recommended action associated with a particular customer,
employee or a transaction (Rygielski et al., 2002).
Data Mining Techniques
Data mining techniques involve the retention-based technique and the distillation-based technique (Rygielski et al., 2002). The retention-based technique applies to tasks of predictive modeling and forensic analysis, and not to the knowledge discovery because they do not distill any patterns. The distillation-based technique has three categories; logical, cross-tabulation, and equational. These three methods extract patterns from a dataset and use the patterns for various purposes. The logical approach handles numeric and non-numeric data, while equations require all data to be numeric, and cross-tabulation work only with non-numeric data. Figure 3 shows the data mining techniques.
Figure 3. Data Mining Techniques (Rygielski et al., 2002).
Data Mining and CRM
CRM is a broad topic with many
layers, one of which is data mining, which is a method or tool that can aid
enterprises in their quest to become more customer-oriented. (Rygielski et al., 2002) discussed the customer lifecycle and the data mining that
can aid organizations to gain competitive
advantages and customer privacy.
Customer’s Lifecycle and Data Mining: CRM lifecycle involves the stages in the relationship between customer and the business. Enterprises can increase the customer’s value by increasing their use or purchase of products they already have, selling them more or higher-margin products, and keeping the customers for a more extended period. The customer relationship changes over time, evolving as the business and customer learn more about each other. The customer lifecycle involves four stages; prospects, responders, active customers former customers. The prospects customers are not yet customers but are in the target market. The responders are prospects who show interest in the product. The active customers are those who are currently using the product or service. The former customer is those who fall into various categories, such as bad customers who did not pay their bills, customers who moved their business to the competing products, customers who incurred a high cost, or customers who are no longer in the target (Rygielski et al., 2002).
Marketing Data Intelligence (MDI): Marketing data intelligence (MDI) is defined as “combining data-driven marketing and technology to increase the knowledge and understanding of customers, products, and transactional data to improve strategic decision making and tactical marketing activity, delivering the CRM challenge” (Rygielski et al., 2002). Enterprises should understand the customers’ lifecycle because it provides a good framework for applying data mining to CRM. The customer’s lifecycle tells what information is available on the input side of the data mining, and what is likely to be interesting on the output side of the data mining. Data mining can be used over some time to predict changes in detail. Enterprises can predict the behavior surrounding a particular lifecycle event such as retirement and find other people in a similar life stage and determine which customers are following similar behavior patterns. The marketing data intelligence is the outcome of this process.
Marketing Data Intelligence (MDI) Components: MDI involves two critical components; customer data transformation, and customer knowledge discovery. The raw data extracted and transformed from a wide range of internal and external databases, marts or warehouses. The collected data gets stored in a centralized location where it can be accessed and explored. The process is continued through customer knowledge discovery, where data mining is implemented, and useful patterns and inferences can be drawn from the data. The process must be measured and tracked to ensure results are pushed to campaign management software. Data mining plays a significant role in the process of CRM (Rygielski et al., 2002). The data mining process involves the interactions with data mart or warehouse in one direction, and the interaction with campaign management software in the other direction. The link between data mining and the campaign management was mostly manual. The trend today is to integrate the data mining and the campaign management to gain a competitive advantage. Enterprises can gain a competitive advantage from such integration by ensuring that the data mining software and the campaign management software share the same definition of the customer segment to avoid modeling the entire database. For instance, if the ideal segment is about high-income males with the age range of 25-35 living in the northeast, the analysis should be limited to this segment.
Data Mining and Customers’ Privacy: The
data mining provides various benefits to businesses. However, it can invade the
privacy of the customers. (Rygielski et al., 2002) argued that the
personalization of CRM is far from the invasion of the privacy. Personal information can be classified into
two categories; data provided and accessible to users, and data generated and
analyzed by businesses. Before data mining techniques became popular,
customer’s data was collected on a
self-provided or transactional basis.
Customers provide general descriptive data which contain demographic
data about themselves. The transactional
data refers to data obtained when a transaction takes place, such as product
name, quantity, location, and time of purchase. Data mining helps turn customer
data into customer profiling information, which belongs to the second
category. It includes customer value,
targeting information, customer rating, and behavior tracking. When abusing this information, people may
also suffer from certain forms of discrimination such as insurance or loss of
career. The central issue of privacy is
to find a balance between privacy rights for consumers’ protection and
businesses benefits.
(Rygielski et al., 2002) argued that privacy
is more of a policy issue than a technology issue. One basic principle for Enterprises when
using personalized technology is to disclose to their customers the kinds of
information they are seeking and how that information will be used.
While some list objectives for ethical information and privacy
management, others develop a Privacy Bill of Rights that includes fair access
by individuals to their personal information. The privacy of customers can be protected when customers do not have to
reveal their identities and can remain anonymous even after implementing data
mining. Various security measures such
as encryption and firewall should be implemented.
Conclusion
The
discussion involved two main articles that discussed data mining application
and CRM. The application of data mining
techniques in CRM is an emerging trend in the industry. The relationship between business and customers
are taking a different path in the presence of the Internet, and Big Data Analytics
techniques such as data mining.
Enterprises are under pressure to gain a competitive advantage using
data mining techniques to extract value from customers’ data. Enterprises are
also under pressures to ensure the protection of the customer’s private
information. Various data mining
techniques are available such as statistics and machine learning. Enterprise should apply the appropriate data
mining technique to CRM strategy to gain competitive
advantages by not only gaining customers but also retaining the customers.
References
Ngai, E. W., Xiu, L., & Chau, D.
C. (2009). Application of data mining techniques in customer relationship
management: A literature review and classification. Expert Systems with Applications, 36(2), 2592-2602.
Rygielski,
C., Wang, J.-C., & Yen, D. C. (2002). Data mining techniques for customer
relationship management. Technology in
society, 24(4), 483-502.
In the age of big data, a considerable variety, volume, and velocity of data are being generated. The data are being generated by people, machines, the Web, and information systems. Harnessing these data and making sense of them in real time or near real time to develop actionable intelligence is one of the big challenges facing organizations. Data are stored in warehouses, and they are then mined to generate insights. Analytical techniques that are used include statistical techniques, machine learning, and others. The purpose of this discussion is to address the challenges and benefits of data warehousing and data mining techniques.
Data Warehousing
Data warehousing is defined as a subject-oriented, integrated, time-variant, and
non-volatile collection of data in support of the decision-making process (Connolly & Begg, 2015). Since the 1970s, enterprises have mostly focused their
investment in a new information system
that automates a business process. Businesses
gained competitive advantages through these systems that provided more
efficient and cost-effective services to customers. Organizations have been stored the data in the operational databases — however, the operational database designed for daily operations and not to be
part of the decision-making process.
Enterprises faced the challenge to turn the archived data into a source
of knowledge. The concept to data
warehouse was emerged as the solution to meet this requirement of a capability
system supporting decision making and receiving data from various operational
sources (Connolly & Begg, 2015; Coronel
& Morris, 2016).
The concept of data warehouse (DW) was devised by IBM as “information warehouse” as a solution for accessing data held in non-relational systems (Connolly & Begg, 2015). It was proposed to allow businesses to use the archived data to aid them to gain a business advantage. However, due to the complexity of the implementation, the early attempts at creating an information warehouse were mostly rejected. The concept of data warehousing has been raised several times since then. However, in recent years, the potential of data warehousing has been viewed as a valuable and viable solution to businesses. Bill Inmon is regarded to be the father of DW as he was one of the earliest promoters of data warehousing (Connolly & Begg, 2015; Guohong, Lijun, Junhui, & Peixin, 2010).
Data Warehouse Characteristics The database for data warehouse (DW) is another type
of database in management information system acting as ‘one-stop shopping” and focusing on supporting informed and
actionable decision making (Ally & Khan, 2016; Coronel
& Morris, 2016).
It is a central location for knowledge creation to mitigate the challenge
of various independent sources of data.
This type of database is distinguished
from other databases such as a transactional
or operational database (Ally & Khan, 2016; Coronel
& Morris, 2016).
DW unlike the operational database collects consolidated and summarized
data used in the decision-making process.
DW has four significant characteristics
proposed by two DW icon known as Kimble and Inmon. The integrated,
subject-oriented, time-variant, and non-volatile are the primary four characteristics of DW (Ally & Khan, 2016; Connolly
& Begg, 2015; Coronel & Morris, 2016).
Data Warehouse Architecture Various studies proposed a various architecture for the data warehouse. The selected architecture for this discussion includes CRM, and ERP (Guohong et al., 2010). CRM integrates the scattered, isolated data in the enterprise for a comprehensive and complete understanding of customers. Online analytical processing technology (OLAP) is a software technology allowing analysis and managers to access the data fast, consistently, and interactively. Figure 1 shows the holistic view of the data warehouse framework.
Figure 1. A Holistic View of DW
Framework (Guohong et al., 2010)
Benefits and Challenges of Data Warehousing The successful implementation of the data warehouse
can bring significant advantages to
business. Enterprises can gain potential
high returns on investment, competitive advantage, and increased the productivity of corporate
decision makers. As cited in (Connolly & Begg, 2015), data warehouse projects delivered an average three-year return on
investment of 401%. This high ROI posits
these enterprises which successfully implemented the data warehousing projects
into a competitive advantage. Businesses
gain competitive advantages when allowing decision makers to access the data
that can reveal previously unavailable, unknown and untapped information on customers,
products, trends, and demands. The successful implementation of the data
warehousing improves the productivity of enterprise
decision makers by creating an integrated database of consistent,
subject-oriented, and historical data.
The data warehouse can integrate
data from various independent data sources
and transform this data to meaningful information providing decision makers
with substantive, accurate and consistent analysis (Connolly & Begg, 2015; Coronel
& Morris, 2016).
Data warehousing is confronted with various challenges. Underestimation of resource for data ETL
(extract, transform and load) process is one of the significant challenges (Connolly & Begg, 2015; Coronel
& Morris, 2016).
Hidden problems with source systems
and required data are not captured are
other challenges that data warehouse faces.
Other challenges include increased end-user demands, data
homogenization, high demand for resources, data ownership, high maintenance,
long-duration projects, and complexity of integration. In the era of Big Data and Big Data
Analytics, a data warehouse is confronted with additional challenges of new
technologies such as Hadoop, MapReduce, Cloud Computing and so forth. The data
warehouse was initially designed for
historical data. However, with BDA, real-time (RT) and near-real-time (NRT), a data warehouse is
required. Thus, the demand is
increased to design DW to enable RT/NRT extraction, modeling RT fact table, and
scalability and query contention (Connolly & Begg, 2015; Coronel
& Morris, 2016)
Data Mining
Data warehouse, OLAP and data
mining are essential technologies forming critical
components of the Business Intelligence implementation (Connolly & Begg, 2015). The value of the data warehouse is determined by providing the data to end
users using the appropriate analytical tools such as data mining and OLAP (Connolly & Begg, 2015). Because OLAP and data mining analytical tools
are distinguished in what they offer to
the end users, they are regarded as
complementary technologies (Connolly & Begg, 2015). While OLAP employs advanced data analysis and
presentation tools including the multi-dimensional data analysis, data mining
provides advanced statistical tools not only to provide analysis of the large data available through the data
warehouses and other sources but also to
identify the possible relationships and anomalies (Connolly & Begg, 2015).
Data mining is “the process
of discovering meaningful new correlations, patterns, and trends by mining large
amounts of data using statistical, mathematical, and AI techniques. Data mining has the potential to supersede
the capabilities of OLAP tools, as the major attraction of data mining is its
ability to build predictive rather than retrospective models” (Connolly & Begg, 2015).
While the traditional BI tools are
“reactive,” data mining is regarded to be “proactive” as the end users do not
have to identify the problem, and select the data to be analyzed by the
traditional BI tools, but rather data mining tools identify the problem by
automatically searching the data for anomalies and possible relationship (Coronel & Morris, 2016). Thus, data mining involves four tasks: (1)
analyzing the data, (2) discovering the problems or opportunities that might be
hidden in the relationship of the data, (3) formulating a model that is based
on the findings, (4) utilizing the model to predict behavior of the business,
which requires minimal intervention from the end users (Coronel & Morris, 2016). As a result of these activities, the business
can use the findings to obtain knowledge that can lead to competitive
advantages (Coronel & Morris, 2016). In summary, data mining is described as the analytical tool that
“initiate analyses to create knowledge” (Coronel & Morris, 2016). This knowledge represents very specialized
information (Coronel & Morris, 2016).
Data
Mining Techniques
Data mining techniques involve four essential operations: (1)
“Predictive Modeling,” (2) “Database Segmentation,” (3) “Link Analysis,” and
(4) “Deviation Detection.” (Connolly & Begg, 2015). The “Predictive Modeling” operation
implements the classification and prediction technique. The “Database Segmentation” operation
implements demographic clustering and
neural clustering techniques (Connolly & Begg, 2015). The “Link Analysis” operation implements
association discovery, sequential pattern discovery, and similar time sequence
discovery techniques (Connolly & Begg, 2015). The “Deviation Detection” operation
implements the statistics and visualization techniques (Connolly & Begg, 2015). Although business can implement any of these
four operations, the certain association
between the business applications and the data mining techniques (Connolly & Begg, 2015). For instance, the “Retail/Marketing” applies
“database segmentation operation,” while the “Fraud Detection” applies any of
the four operations (Connolly & Begg, 2015).
The Machine Learning
Algorithm “Supervised” and “Non-supervised” learning techniques are the most common machine learning algorithm that is implemented in various domains, particularly
the “Data Mining” domain (Hall, Dean, Kabul, & Silva, 2014). Supervised learning algorithm (SLA) is a
technique that is used to label data to train a model (Hall et al., 2014).
It is comprised of “Prediction”
(“Regression”) algorithm, and “Classification” algorithm. The “Regression” or “Prediction” algorithm is
used for “interval labels,” while the “Classification” algorithm is used for
“class labels” (Hall et al., 2014). In the SL algorithm, the training data
represented in observations, measurements, and so forth are associated by labels reflecting the class of the observations (Han, Pei, & Kamber, 2011). The new data is classified based on the
“training set” (Han et al., 2011).
The unsupervised learning algorithm (ULA) occurs when a model is trained on
unlabeled data (Hall et al., 2014). UL algorithm typically segments data into
“groups of examples” called “Clusters” or “groups of features” called “Feature
Extraction” (Hall et al., 2014). The UL technique can be either the “end goal
of a machine learning task,” as the case with “Market Segmentation,” or a
“preliminary or pre-processing step in a supervised learning task” (Hall et al., 2014). When using the UL algorithm, the class labels
of training data is “unknown” (Han et al., 2011). UL algorithm is used to establish the
existence of class or clusters in the data, given a set of measurements, and
observations (Han et al., 2011).
Benefits
and Challenges
The goal of data mining is to
extract value from data. Enterprises can
utilize this information to make sound decisions to gain competitive advantages (Che, Safran, & Peng, 2013).
Organizations can benefit from data
mining in discovering concept/class descriptions, associations and
correlations, classification, prediction, clustering, trend analysis outlier,
and deviation analysis in making strategic and tactic decisions (Hand, Mannila, & Smyth, 2001; Linoff & Berry, 2011; Rygielski,
Wang, & Yen, 2002). However, data mining is confronted with various challenges include the development of
parallel or high-performance algorithms, theoretical models, and data mining
techniques (Dubitzky, 2008). Distributed data mining algorithms should
support the complete data mining process from pre-processing, to data mining,
to post-processing. The design of new
data mining systems and architectures to deal with efficient use of computing
resource is another challenging area for data mining. More development challenges in several areas
such as the high complexity of many data mining applications, the various data
sources with various data models, the volume of the data (Dubitzky, 2008).
Conclusion
This discussion addressed two significant topics of data warehouse and data mining. It began with the discussion about data warehouse, its evolution using information warehouse by IBM. Due to the complexity, the concept disappeared for a while but surfaced again. Bill Inmon is the father of the data warehouse. The benefits of the data warehouse are tremendous to businesses. However, data warehouse project implementation is confronted with various challenges especially in the age of Big Data Analytics and emerging technologies such as Hadoop. Data mining is another technique that organization embraces to extract value from the data. Data mining has various mining techniques including supervised and non-supervised algorithms. Like data warehouse, data mining makes organization gain a competitive edge. However, same as the data warehouse, data mining is also confronted with various challenges. Organizations should analyze each technique before embracing the technology to understand the benefits as well as the challenges.
References
Ally, S. S.,
& Khan, N. (2016, 15-17 Dec. 2016). Data
Warehouse and BI to Catalize Information Use in Health Sector for Decision
Making: A Case Study. Paper presented at the 2016 International Conference
on Computational Science and Computational Intelligence (CSCI).
Che, D., Safran,
M., & Peng, Z. (2013). From Big Data
to Big Data Mining: Challenges, Issues, and Opportunities. Paper presented
at the International Conference on Database Systems for Advanced Applications.
Connolly, T.,
& Begg, C. (2015). Database Systems:
A Practical Approach to Design, Implementation, and Management (6th Edition
ed.): Pearson.
Dubitzky, W.
(2008). Data Mining in Grid Computing
Environments: John Wiley & Sons.
Guohong, G.,
Lijun, X., Junhui, F., & Peixin, Q. (2010). The building of Customer Relationship Management system based on OLAP.
Paper presented at the Industrial Mechatronics and Automation (ICIMA), 2010 2nd
International Conference on.
Hall, P., Dean,
J., Kabul, I. K., & Silva, J. (2014). An Overview of Machine Learning with
SAS® Enterprise Miner™. SAS Institute Inc.
Han, J., Pei, J.,
& Kamber, M. (2011). Data mining:
concepts and techniques: Elsevier.
Hand, D. J.,
Mannila, H., & Smyth, P. (2001). Principles
of data mining.
Linoff, G. S.,
& Berry, M. J. (2011). Data mining
techniques: for marketing, sales, and customer relationship management:
John Wiley & Sons.
Rygielski,
C., Wang, J.-C., & Yen, D. C. (2002). Data mining techniques for customer
relationship management. Technology in
society, 24(4), 483-502.
There are several areas of
information ethics in which the control of information is crucial. Four such
areas are privacy, accuracy, property, and accessibility (PAPA). The purpose of
this discussion is to address these critical areas in the context of the importance
of the control of information for ethical reasons. The discussion begins with
the ethical issues four building blocks of PAPA, followed by the control of
information and security measures which Enterprises must follow to protect the
privacy of users since it has been a significant
concern in IS domain.
Ethical Issues Building Blocks
In the 1990s, computer ethics became
a favorite topic in the research
community. One of the virus and worms
attacks called “ILoveYou” proliferated the computer ethics dilemma dramatically
(Harris, 2000). The estimated damage of this virus reached $10
billion worldwide, mostly in the loss of the work time. FBI estimates billions of dollars lost due to computer crimes. This virus has raised the red flag for
serious ethical issues faced by computer
users and IT Professionals. The Internet has increased the seriousness of
the ethical issues when using an information
system and computers (Harris, 2000).
The information system (IS) is
becoming boundless as organizations attempt to diminish costs, increase the
efficiency and develop strategic competitive advantages (Pearlson & Saunders,
2001).
However, these advantages exist in a business domain that lacks moral clarity.
Enterprises are under pressure to evaluate the current information
system with more focus on ethical
issues. The building blocks of ethical computing issues have not been clear to
many computer and information system users.
In the age of information system,
computers, internet, and digital world, (Mason, 2015) indicated that many unique challenges exist stemming from
the nature of information. However, although many ethical issues exist, focused
on four major ethical issues; privacy, accuracy, property, and accessibility (PAPA). Figure 1 shows these four building blocks with
their related critical questions.
Privacy is defined in today’s information-oriented
world as the ability of the individual to personally control information about
self (Pearlson & Saunders,
2001). Privacy has been a significant issue around the globe as users are concerned about
revealing and discoloring information that they do not want to make it public
or shared with other entities (Mason, 2015; Pearlson &
Saunders, 2001).
Accuracy represents
the correctness of the information. When the information presented does not
reflect the accurate information, it can cause serious issues. (Mason, 2015; Pearlson &
Saunders, 2001)
referred to a bank case where the
customer made a payment on a mortgage
which was not recorded in the bank system and eventually the bank took
foreclosed on the house. This example
shows how serious inaccurate information can cause to individuals.
The propertyrepresents
the owner of the data. The question of
intellectual property rights is one of the most complex issues. Organizations collect
information about customers, users, and
employees. The data gets stored either
internally or in the cloud. Who owns the
data is the question for the property ethical issue (Mason, 2015; Pearlson &
Saunders, 2001)
Accessibility raises the question about what information a person or organization have the right to access and obtain, under what condition, and what safeguards (Mason, 2015; Pearlson & Saunders, 2001).
Figure 1. PAPA Ethical Issues Model
based on (Mason, 2015; Pearlson &
Saunders, 2001).
Control of Information and Security Measures
(Abernathy & McMillan, 2016) identified personally identifiable information (PII) that can be used alone or with other information to identify a single person. PII includes full name, an identification number such as driving license, social security, date of birth and so forth. Enterprises must ensure that they understand international, national, state and local regulations and laws regarding the PII. Figure 2 shows the magnitude of personal data.
Figure 2. PII Complex List of Personal Data
(Abernathy & McMillan,
2016).
Various regulations and policies
have been established around the world to protect the privacy of the
individuals (Abernathy & McMillan,
2016; Pearlson & Saunders, 2001). In the U.S., privacy legislation
includes the 1974 Privacy Act which
regulates the government’s collection and use of personal information and the 1998 Children’s Online
Privacy Protection Action which regulates the online collection and use of
children’s personal information. Other regulations are industry-based
legislation to protect the privacy of the individuals such as the Gramm-Leach-Bliley Act of 1999 and the Health Insurance Portability and Accountability
Act (HIPAA) of 1996. Gramm-Leach-Bliley
Act of 1999 was issued because banks were
selling sensitive information about their customers such as social security
number, credit card purchase history to telemarketing companies. This law has mitigated sharing such sensitive
information with other entities. HIPAA
was issued to safeguard the electronic exchange privacy and security of the
information in the healthcare industry. Patients’ records must be protected from unauthorized access,
manipulation, and transmissions (Abernathy & McMillan,
2016; Pearlson & Saunders, 2001).
Various studies have discussed the
ethical issues in information system domain (Harris, 2000; Kuzu, 2009;
Ponelis, 2013). Organizations are under pressure to protect
the privacy of the users in the age of information system, computers, and the Internet. They should limit inappropriate access to
customers’ information to respect the privacy of their customers, users, and employees (Pearlson & Saunders,
2001). Security measures must be implemented to
ensure the appropriate data protection so that nonauthorized users and
malicious attacks can be prevented and mitigated. These security measures can be a firewall, authentication, authorization, access
control, and encryption. At the network
level when data is moving from one system to another, security measures include
secure socket layer (SSL) protocol, Transport
Layer Security (TLS) protocol, secure IP (IPSec), secure HTTP (HTTPS), secure
email (S/MIME) (Kuzu, 2009). When using cloud computing, the security measures to
protect the privacy and the integrity of the data are more complicated as cloud
computing has different service models, and different deployment models (Kumar, Ranjan, &
Gangwar, 2012).
Organizations must evaluate the options for selecting the appropriate security
measure not only to protect themselves
from outrageous fines and penalties but also to protect the privacy of the
users.
Conclusion
This discussion addressed critical
ethical issues using the privacy, accuracy, property, and accessibility (PAPA)
model of (Mason, 2015). These ethical issues raise
the flag to protect data from unauthorized user access, from sharing private
information, or from any malicious attacks that can cause loss of data or data
breach. Enterprises
are under pressure to ensure the protection of the user’s information. Various
security measures can be implemented at the
various level of the information system
for data at rest or data in motion. For
data in motion, security measures such as SSL, HTTPS, and IPSec can be
implemented to protect data. For data at
rest, security measures can include encryption and access control. Organizations should take into consideration
additional security measures to control access to information, especially when using cloud computing.
References
Abernathy, R., & McMillan, T.
(2016). CISSP Cert Guide: Pearson IT
Certification.
Harris, A. L.
(2000). IS ethical attitudes among
college students: A comparative study.
Kumar, A.,
Ranjan, A., & Gangwar, U. (2012). An understanding approach towards cloud
computing. International Journal of
Emerging Technology and Advanced Engineering, 2(9).
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Mason, R. O.
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Pearlson, K.,
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Ponelis,
S. (2013). Ethical risks of social media use by academic libraries. Innovation: journal of appropriate
librarianship and information work in Southern Africa, 2013(47), 231-244.
The purpose of this discussion is to address Big Data (BD) and the challenges associated with BD in the context of business analytics. The discussion begins with a brief overview of Big Data and Big Data Analytics, followed by the challenges. Cloud computing solution is also discussed as well as the role of BD in ERP.
Big Data Brief Overview
Big Data is now the buzzword in the field of computer science and information
technology. Big Data attracted the
attention of various sectors, researchers, academia, government and even the
media (Géczy, 2014;
Kaisler, Armour, Espinosa, & Money, 2013). In the
2011 report of the International Data Corporation (IDC), it is reporting that
the amount of the information which will be
created and replicated will exceed
1.8 zettabytes which are 1.8 trillion
gigabytes in 2011. This amount of information is growing by a factor of 9 in
just five years (Gantz & Reinsel,
2011). Big Data and Big Data Analytic are terms that have been used interchangeably (Maltby, 2011). Big Data has unique characteristics that are identified as challenging using traditional technology.
Big Data (BD) has been characterized by what is often referred to as a multi-V model such as variety, velocity, volume, veracity, and value (Assunção, Calheiros, Bianchi, Netto, & Buyya, 2015). While variety represents the data types, the velocity reflects the rate at which the data is produced and processed (Assunção et al., 2015). The volume defines the amount of data, and the veracity reflects how much the data can be trusted given the reliability of its source. The value, on the other hand, represents the monetary worth which organizations can derive from adopting Big Data computing. Figure 1 summarizes these characteristics.
Big Data (BD) has been characterized by what is often referred to as a multi-V model such as variety, velocity, volume, veracity, and value (Assunção, Calheiros, Bianchi, Netto, & Buyya, 2015). While variety represents the data types, the velocity reflects the rate at which the data is produced and processed (Assunção et al., 2015). The volume defines the amount of data, and the veracity reflects how much the data can be trusted given the reliability of its source. The value, on the other hand, represents the monetary worth which organizations can derive from adopting Big Data computing. Figure 1 summarizes these characteristics.
Figure 1. Big Data Multi-V Model (Assunção et al., 2015).
The variety characteristic of the Big Data reflects the data types (Assunção et al., 2015). The data types are further categorized into the structure, unstructured, semi-structured and mixed. The structured data represents the formal schema and data models, while the unstructured reflects no pre-defined data model, and semi-structured lacked strict data model structure and mixed as the term indicates that various types together (Assunção et al., 2015). Figure 2 summarizes these data types in the Big Data.
Figure 2. Variety Characteristic of Big Data (Assunção et al., 2015).
The velocity characteristics of the Big Data represents the speed or arrival and the processing of the data which have been characterized into the batch, near-time, real-time, and streams according to (Assunção et al., 2015). The batch reflects the at time intervals, while near-time refers to at small time intervals. The real-time, on the other hand, represents the continuous input, process, and output, while the streams refer to data flows (Assunção et al., 2015). Figure 3 summarizes these characteristics of the velocity feature of the Big Data.
Figure 3. Velocity Characteristic of Big Data(Assunção et al., 2015).
Big Data Challenges
With these
characteristics of Big Data, including the growth rate, challenges and issues
have come along (Jagadish et al., 2014; Meeker & Hong, 2014; Misra, Sharma, Gulia,
& Bana, 2014; Nasser & Tariq, 2015; Zhou, Chawla, Jin, & Williams,
2014).
The growth rate in the amount of data is regarded to be a significant challenge for IT researchers and
practitioners to design appropriate systems that handle the data effectively
and analyze it to extract relevant meaning for decision-making
(Kaisler et al., 2013). Various challenges and issues of the Big Data have been discussed and
analyzed in multiple research studies, such as data storage, data management,
and data processing (Fernández et al., 2014; Kaisler et al., 2013); Big Data variety, Big Data integration and cleaning,
Big Data reduction, Big Data query and indexing, and Bid Data analysis and mining
(J. Chen et al., 2013).
Extracting a meaningful value from the Big Data is a significant challenge (Fernández et al., 2014; Sagiroglu & Sinanc, 2013). Three factors must be taken into consideration to create value from Big Data (Chopra & Madan, 2015). These three factors include the user control
over the data, the security issues to be taken seriously, and the examination
of safety points on a yearly basis. (Chopra & Madan, 2015) suggested that businesses and organizations, which follow those
factors, will distinguish themselves by gaining market initiatives. Other research studies such as (Labrinidis & Jagadish, 2012) suggested that the value obtained from the analysis of the data is
broadly recognized, but the analysis of the data is regarded to be challenging
due to the challenging characteristics of the Big Data. Other research studies
such as (Assunção et al., 2015; Chopra & Madan, 2015) have indicated that the
complexity of Big Data is preventing organization to realize its benefit and
causing a business to step back from the
Big Data deployment and implementation.
Big Data Analytics and Cloud Computing Solution
The challenges of BD and BDA such as data storage, data
management, data processing, and
data-intensive computational requirements required solutions as the traditional
technology was found inadequate (Fernández et al., 2014; Hu, Wen, Chua, & Li, 2014). As indicated above, one of the significant challenges
is extracting a meaningful value from BD.
BD and BDA require advanced and unique
data storage, management, analysis, intensive computing, and visualization technologies
(H. Chen, Chiang, & Storey, 2012; J. Chen et al.,
2013). Cloud computing emerging technology has been
meeting these requirements and serving as a solution and platform to BD and BDA
challenges.
Cloud computing plays a significant role in Big Data Analytics (Assunção et al., 2015). The massive
computation and storage requirement of the BD and BDA brings the critical need
for cloud computing (Mehmood, Natgunanathan, Xiang, Hua, & Guo, 2016). Cloud computing is currently the biggest buzz in the
information technology, computer science industry, in the computer world, and the
distributed computing community (Dhanani, 2014; Saini & Sharma, 2014). It is being positioned as the “next wave of
computing” (Mvelase, Dlodlo, Makitla, Sibiya, & Adigun, 2012,
p. 214). The use of cloud computing technology in conjunction with
data has been the more recent trend for BDA
(Wang, Kung, & Byrd, 2018). Organizations have increasingly adopted BD
and BDA in the cloud, particularly, the
Software-as-a-Service (SaaS) cloud service model, which offers an attractive
alternative with lower cost (Wang et al., 2018). Cloud computing
technology for BDA systems supporting a real-time analytic capability and
cost-effective storage is becoming a preferred information technology solution (Wang et al., 2018). The cloud
computing technology is the solution and the answer to the challenges of BD and BDA (Fernández et al., 2014). Organizations and businesses are under
pressure to quickly adopt and implement technologies such as cloud computing to
address the challenges of Big Data (Hashem et al., 2015).
Big Data Analytics Role in
ERP
Big Data Analytics plays a significant role in ERP
applications (Carlton, 2014; ERP Solutions, 2018; Woodie, 2016). Enterprise data comprises various departments
such as HR, finance, CRM and other essential business functions of a
business. This data can be leveraged to
make ERP functionality better. When Big
Data tools are brought together with the ERP
system, it can unfold valuable insights that can businesses make smarter
decisions (Carlton, 2014; Cornell University, 2017; Wailgum,
2018).
Many ERP systems fail to make use of real-time inventory and supply chains data because these systems lack the intelligence
to make predictions about products demands (Carlton, 2014; ERP Solutions, 2018). Big Data tools can predict
demand and help determine the needs of the organization to go forward (ERP Solutions, 2018).
Infor co-president Duncan Angove established Dynamic Science Labs (DSL)
aiming to use data science techniques to solve particular
business problems for its customers. Employees with big data, math, and coding skills were hired in Cambridge, Massachusetts-based organization to
develop proof of concept (POC) (Woodie, 2016).
Big Data systems such as Apache’s Hadoop are creating node-level
operating transparencies which affect nearly every current ERP module in
real-time (Carlton, 2014).
Managers will be able to quickly leverage ERP Big Data capabilities,
thereby enhancing information density and speeding up overall decision-making.
In brief, Big Data and Big Data Analytics impact
business at all levels, and ERP is no
exception.
Conclusion
Big Data (BD) and Big Data Analytics (BDA) have been
the buzzwords across various industries from academic, research, practitioners,
media and government. BD has been characterized by certain features such as volume, variety, and velocity which were the first V-model of
BD. The traditional technology and
systems were found inadequate to deal with
and handle BD. The explosive growth of
the data in various forms such as structured, unstructured and semi-structured, and the speed of the growth and
the required speed for processing the data demanded technologies that can deal
with these unique characteristics. Cloud computing emerging technology was found
to provide a solution when applying BD
and BDA for storage and computation.
Other technologies include Hadoop, MapReduce, Spark and so forth. BD and BDA play a crucial role in Enterprise Resource Planning (ERP). Organizations
are under pressure to take advantage of BD and BDA to become competitive and
stay competitive in the age of the digital
world and the era of Big Data and Big Data Analytics.
References
Assunção, M. D.,
Calheiros, R. N., Bianchi, S., Netto, M. A. S., & Buyya, R. (2015). Big
Data Computing and Clouds: Trends and Future Directions. Journal of Parallel and Distributed Computing, 79, 3-15.
doi:10.1016/j.jpdc.2014.08.003
Chen, H., Chiang,
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Dhanani, M.
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The purpose of this proposal is to
design a state-of-the-art healthcare system in four States of Colorado, Utah,
Arizona, and New Mexico. Big Data and Big Data Analytics have played
significant roles in various industries including the healthcare industry. The
value that is driven by BDA can save lives and minimize costs for
patients. The project proposes a design
to apply BD and BDA in the healthcare system across these identified four
States. Cloud
computing is the most appropriate technology to deal with the large volume of
healthcare data at the storage level as well as at the data processing level. Due to
the security issue of the cloud computing, the Virtual Private Cloud (VPC) will
be used.
VPC provides a secure cloud environment using network traffic security
setup using security groups and network access control lists. The project requires other components to be
fully implemented using the latest technology such as Hadoop and MapReduce for
data streaming processing, machine learning for artificial intelligence, which
will be used for Internet of Things
(IoT). The NoSQL database HBase and
MongoDB will be used to handle the semi-structured data such as XML and
unstructured data such as logs and images.
Spark will be used for real-time
data processing which can be vital for urgent care and emergency services. This project addresses the assumptions and
limitations plus the justification for selecting these specific
components. All stakeholders in the healthcare sector including providers,
insurers, pharmaceuticals, practitioners should cooperate and coordinate to
facilitate the implementation process.
The rigid culture and silo pattern need to change for better healthcare
which can save millions of dollars to the healthcare industry and provide
excellent care to the patients at the same time.
Keywords: Big
Data Analytics; Hadoop; Healthcare Big Data System; Spark.
In
the age of Big Data (BD), information technology plays a significant role in the
healthcare industry (HIMSS, 2018). The role of information
technology in healthcare The healthcare sector generates a massive amount of data every day to conform to
standards and regulations (Alexandru, Alexandru, Coardos, & Tudora, 2016). The generated Big Data has the potential to
support many medical and healthcare operations including clinical decision support, disease surveillance
and population health management (Alexandru et al., 2016). This project proposes a state-of-the-art integrated system for hospitals located in Arizona, Colorado, New
Mexico, and Utah. The system is based on
the Hadoop ecosystem to help the
hospitals maintain and improve human
health via diagnosis, treatment and disease prevention.
It begins with Big
Data Analytics in Healthcare Overview, which covers the benefits and challenges
of BD and BDA in the healthcare
industry. The overview also covers the
various healthcare data sources for data analytics, in different formats such
as semi-structured, e.g., XML and JSON, and unstructured, e.g., images and XRays. The second section addresses the healthcare
BDA Design Proposal Using Hadoop. This section covers various components. The first component discusses the requirements
for this design. These requirements
include state-of-the-art technology such
as Hadoop/MapReduce, Spark, NoSQL database,
Artificial Intelligence (AI), Internet of Things (IoT). The project also covers various diagrams
including the data flow diagram, a communication
flow chart, and the overall system diagram.
The healthcare design system is bounded
by regulation, policies, and governance
such as HIPAA, that is also covered in
this project. The justification,
limitation, and assumptions are also discussed.
BD and BDA are
terms that have been used interchangeably
and described as the next frontier for innovation, competitions, and productivity (Maltby, 2011; Manyika et al., 2011). BD has a multi-V model with unique
characteristics, such as volume referring to the large dataset, velocity refers to the speed of the computation as well
as data generation, and variety referring to the various data types such as
semi-structured and unstructured (Assunção, Calheiros, Bianchi, Netto, & Buyya, 2015; Hu, Wen, Chua,
& Li, 2014). BD is
described as the next frontier for competition, innovation, and productivity. Various industries including healthcare have
taken this opportunity and applied BD and BDA in their business models (Manyika et al., 2011). McKinsey Institute predicted $300 billion as
a potential annual value to US healthcare (Manyika et al., 2011).
The healthcare
industry generated extensive data driven
by keeping patients’ records, complying with regulations and policies, and
patients care (Raghupathi & Raghupathi, 2014). The current trend is digitalizing this
explosive growth of the data in the age of Big Data (BD) and Big Data Analytics
(BDA) (Raghupathi & Raghupathi, 2014). BDA has made a revolution in healthcare by
transforming the valuable information, knowledge to predict epidemics, cure
diseases, improve quality of life, and avoid preventable deaths (Van-Dai, Chuan-Ming, & Nkabinde, 2016). Various applications of BDA in healthcare
include pervasive health, fraud detection, pharmaceutical discoveries, clinical
decision support system, computer-aided diagnosis, and biomedical applications.
Healthcare
sector employs BDA in various aspect of healthcare such as detecting diseases
at early stages, providing evidence-based medicine, minimizing doses of
medication to avoid any side effects, and delivering useful medicine base on genetic analysis. The use of BD and BDA can reduce the
re-admission rate, and thereby the healthcare related costs for patients are reduced.
Healthcare BDA can be used to detect spreading diseases earlier before
the disease gets spread using real-time analytics (Archenaa & Anita, 2015; Raghupathi &
Raghupathi, 2014; Wang, Kung, & Byrd, 2018). Example of the application of BDA in the healthcare system is Kaiser Permanente
implementing a HealthConnect technique to ensure data exchange across all
medical facilities and promote the use of electronic health records (Fox & Vaidyanathan, 2016).
Despite the various
benefits of BD and BDA in the healthcare
sector, various challenges and issues are emerging from the application of BDA
in healthcare. The nature of the healthcare
industry poses challenging to BDA (Groves, Kayyali, Knott, & Kuiken, 2016). The episodic culture, the data puddles, and
the IT leadership are the three significant
challenges of the healthcare industry to apply BDA. The episodic culture addresses the
conservative culture of the healthcare and the lack of IT technologies mindset
creating rigid culture. Few providers
have overcome this rigid culture and started to use the BDA technology. The
data puddles reflect the silo nature of healthcare. Silo is
described as one of the most significant
flaws in the healthcare sector (Wicklund, 2014). The use of the technology properly is lacking
in healthcare sector resulting in making the industry fall behind other
industries. All silos use their methods to collect data from labs, diagnosis,
radiology, emergency, case management and so forth. The IT leadership is another challenge is caused by the rigid culture of the
healthcare industry. The lack of the
latest technologies among the IT leadership in the healthcare industry is a severe problem.
The
current healthcare data is collected from clinical and non-clinical sources (InformationBuilders, 2018; Van-Dai et al., 2016; Zia & Khan, 2017). The electronic healthcare records are digital
copies of the medical history of the patients.
It contains a variety of data relevant to the care of the patients such
as demographics, medical problems, medications, body mass index, medical
history, laboratory test data, radiology reports, clinical notes, and payment
information. These electronic healthcare records are the most important data in healthcare data analytics,
because it provides effective and efficient methods for the providers and
organizations to share data (Botta, de Donato, Persico, & Pescapé, 2016; Palanisamy &
Thirunavukarasu, 2017; Van-Dai et al., 2016; Wang et al., 2018).
The biomedical
imaging data plays a crucial role in
healthcare data to aid disease monitoring, treatment planning and
prognosis. This data can be used to
generate quantitative information and make inferences from the images that can
provide insights into a medical condition.
The images analytics is more complicated
due to the noises of the data associated with the images and is one of the significant limitations with biomedical
analysis (Ji, Ganchev, O’Droma, Zhang, & Zhang, 2014; Malik & Sangwan,
2015; Van-Dai et al., 2016).
The sensing data is ubiquitous in the medical domain both for real-time and for historical data analysis. The sensing data involve several forms of medical data collection instruments such as the electrocardiogram (ECG) and electroencephalogram (EEG) which are vital sensors to collect signals from various parts of the human body. The sensing data plays a significant role for intensive care units (ICU) and real-time remote monitoring of patients with specific conditions such as diabetes or high blood pressure. The real-time and long-term analysis of various trends and treatment in remote monitoring programs can help providers monitor the state of those patients with certain conditions(Van-Dai et al., 2016).
The biomedical signals are collected from many sources such as hearts,
blood pressure, oxygen saturation levels, blood glucose, nerve conduction, and brain activity. Examples of biomedical signals include electroneurogram
(ENG), electromyogram (EMG), electrocardiogram (ECG), electroencephalogram
(EEG), electrogastrogram (EGG), and phonocardiogram (PCG). The biomedical signals real-time analytics
will provide better management of chronic diseases, earlier detection of
adverse events such as heart attacks, and strokes and earlier diagnosis of
disease. These biomedical signals can be discrete or
continuous based on the kind of care or severity of a particular pathological
condition (Malik &
Sangwan, 2015; Van-Dai et al., 2016).
The genomic data
analysis helps better understand the
relationship between various genetic, mutations, and disease conditions. It has
great potentials in the development of various gene therapies to cure certain
conditions. Furthermore, the genomic
data analytics can assist in translating genetic discoveries into personalized
medicine practice (Liang & Kelemen, 2016; Luo, Wu, Gopukumar, & Zhao, 2016;
Palanisamy & Thirunavukarasu, 2017; Van-Dai et al., 2016).
The clinical text
data analytics using the data mining are the transformation process of the
information from clinical notes stored in unstructured data format to useful patterns. The manual coding of clinical notes is costly
and time-consuming, because of their
unstructured nature, heterogeneity, different
format, and context across different patients and practitioners. Various methods such as natural language
processing (NLP) and information retrieval can be used to extract useful
knowledge from large volume of clinical text and automatically encoding
clinical information in a timely manner (Ghani, Zheng, Wei, & Friedman, 2014; Sun & Reddy, 2013; Van-Dai
et al., 2016).
The social network healthcare data analytics is based on various kinds of collected social media sources such as social networking sites, e.g., Facebook, Twitter, Web Logs, to discover new patterns and knowledge that can be leveraged to model and predict global health trends such as outbreaks of infections epidemics (InformationBuilders, 2018; Luo et al., 2016; Van-Dai et al., 2016; Zia & Khan, 2017). Figure 1 shows a summary of these healthcare data sources.
The implementation
of BDA in the hospitals within the four States aims
to improve the safety of the patient, the clinical outcomes, promoting wellness
and disease management (Alexandru et al., 2016; HIMSS, 2018). The BDA system will take advantages of the large healthcare-generated data to provide
various applied analytical disciplines such as statistical, contextual,
quantitative, predictive and cognitive spectrums (Alexandru et al., 2016; HIMSS, 2018). These applied analytical disciplines will
drive the fact-based decision making for planning management and learning in hospitals (Alexandru et al., 2016; HIMSS, 2018).
The proposal begins with the
requirements, followed by the data flow diagram, the communication flowcharts, and the overall system
diagram. The proposal addresses the
regulations, policies, and governance for the medical system. The limitation and assumptions are also addressed in this proposal, followed
by the justification for the overall design.
The basic
requirement for the implementation of this proposal included not only the tools and required software, but also the
training at all levels from staff, to nurses, to clinicians, to patients. The list of the requirements is divided into system requirement,
implementation requirement, and training
requirements.
The volume is one
of the significant characteristics of BD,
especially in the healthcare industry (Manyika et al., 2011). Based on the challenges addressed earlier
when dealing with BD and BDA in healthcare, the system requirements cannot be
met using the traditional on-premise technology center, as it cannot handle the
intensive computation requirements of BD, and the storage requirement for all
the medical information from various hospitals from the four States (Hu et al., 2014). Thus, the cloud computing environment is found
to be more appropriate and a solution for the implantation of this proposal. Cloud computing plays a significant role in
BDA (Assunção et al., 2015). The massive computation and storage
requirement of BDA brings the critical need for cloud computing emerging
technology (Mehmood, Natgunanathan, Xiang, Hua, & Guo, 2016). Cloud computing offers various benefits such
as cost reduction, elasticity, pay per use, availability, reliability, and maintainability (Gupta, Gupta, & Mohania, 2012; Kritikos, Kirkham, Kryza, &
Massonet, 2017).
However, although cloud computing offers
various benefits, it has security and privacy issues using the standard deployment
models of public cloud, private cloud, hybrid cloud, and community cloud. Thus, one of the major requirements is to
adopt the Virtual Private Cloud as it has been
regarded as the most prominent approach to trusted computing technology (Abdul, Jena, Prasad, & Balraju, 2014).
Cloud computing
has been facing various threats (Cloud Security Alliance, 2013, 2016, 2017).
Records showed that over the last three years
from 2015 until 2017, the number of breaches, lost medical records, and settlements of fines are staggering (Thompson, 2017). The
Office of Civil Rights (OCR) issued 22 resolution agreements, requiring
monetary settlements approaching $36 million (Thompson, 2017). Table 1
shows the data categories and the total for each year.
Table 1. Approximation of Records Lost by Category Disclosed on HHS.gov (Thompson, 2017)
Furthermore, a
recent report published by HIPAA showed the first three months of 2018
experienced 77 healthcare data breaches reported to the OCR (HIPAA, 2018d). In the second quarter of 2018, at least 3.14
million healthcare records were exposed (HIPAA, 2018a). In the third quarter of 2018, 4.39 million
records exposed in 117 breaches (HIPAA, 2018c).
Thus, the
protection of the patients’ private information requires the technology to
extract, analyze, and correlated potentially sensitive dataset (HIPAA, 2018b). The implementation of BDA requires security
measures and safeguards to protect the privacy of the patients in the
healthcare industry (HIPAA, 2018b). Sensitive data should be encrypted to prevent
the exposure of data in the event of theft (Abernathy & McMillan, 2016). The security requirements involve security at
the VPC cloud deployment model as well as at the local hospitals in each State (Regola & Chawla, 2013). The security at the VPC cloud deployment model
should involve the implementation of security groups and network access control
lists to allow access to the right individuals to the right applications and
patients’ records. Security group in VPC
acts as the first line of defense
firewall for the associated instances of the VPC (McKelvey, Curran, Gordon, Devlin, & Johnston, 2015). The network access control lists act as the
second layer of defense firewall for the associated subnets, controlling the
inbound and the outbound traffic at the subnet level (McKelvey et al., 2015).
The security at
the local hospitals level in each State is mandatory to protect patients’
records and comply with HIPAA regulations (Regola & Chawla, 2013). The medical equipment must be secured with
authentication and authorization techniques so that only the medical staff,
nurses and clinicians have access to the medical devices based on their
role. The general access should be prohibited as every member of the hospital has a different role with
different responses. The encryption should be used to hide the
meaning or intent of communication from unintended users (Stewart, Chapple, & Gibson, 2015). The
encryption is an essential element in security control especially for the data
in transit (Stewart et al., 2015). The hospital in all four State should
implement the encryption security control
using the same type of the encryption across the hospitals such as PKI, cryptographic application, and cryptography and
symmetric key algorithm (Stewart et al., 2015).
The system
requirements should also include the identity management systems that can
correspond with the hospitals in each state. The identity management system
provides authentication and authorization
techniques allowing only those who should have access to the patients’ medical
records. The proposal requires the
implementation of various encryption techniques such as secure socket layer
(SSL), Transport Layer Security (TLS), and Internet Protocol Security (IPSec)
to protect information transferred in public network (Zhang, R. & Liu, 2010).
While the
velocity of BD leads to the speed of generating large volume of data and
requires speed in data processing (Hu et al., 2014), the variety of the data requires specific technology capabilities to handle
various types of dataset such as structured, semi-structured, and unstructured
data (Bansal, Deshpande, Ghare, Dhikale, & Bodkhe, 2014; Hu et al., 2014). Hadoop ecosystem is found to be the most
appropriate system that is required to implement BDA (Bansal et al., 2014; Dhotre, Shimpi, Suryawanshi, & Sanghati, 2015). The implementation requirements include
various technologies and various tools.
This section covers various components that are required when implementing
Hadoop technology in the four States for healthcare BDA system.
Hadoop has three significant limitations, which must be addressed in this design. The first limitation is the lack of technical
support and document for open source Hadoop (Guo,
2013). Thus, this design requires the Enterprise
Edition of Hadoop to get around this limitation using Cloudera, Hortonworks, and MapR (Guo,
2013). The final decision for which product will be determined by the cost analysis team. The second limitation is that Hadoop is not
optimal for real-time data processing (Guo,
2013). The solution for this
limitation will require the integration of real-time streaming program as Spark
or Storm or Kafka (Guo,
2013; Palanisamy & Thirunavukarasu, 2017). This requirement of integrating
Spark is discussed below in a separate requirement for this design (Guo,
2013). The third limitation is that
Hadoop is not a good fit for large graph
dataset (Guo,
2013). The solution for this
limitation requires the integration of GraphLab which is also discussed below
in a separate requirement for this design.
1.3.1 Hadoop Ecosystem for Data Processing
Hadoop technologies have been in the front-runner for Big Data application (Bansal et al., 2014; Chrimes, Zamani, Moa, & Kuo, 2018). Hadoop ecosystem will be part of the implementation requirement as it is proven to serve well with intensive computation using large datasets (Raghupathi & Raghupathi, 2014; Wang et al., 2018). The implementation of Hadoop technology will be performed in the VPC deployment model. The Hadoop version that is required is version 2.x to include YARN for resource management (Karanth, 2014). Hadoop 2.x also include HDFS snapshots to provide a read-only image of the entire or a particular subset of a filesystem to protect against user errors, backup, and disaster recovery (Karanth, 2014). The Hadoop platform can be implemented to gain more insight into various areas (Raghupathi & Raghupathi, 2014; Wang et al., 2018). Hadoop ecosystem involves Hadoop Distributed File System, MapReduce, and NoSQL database such as HBase, and Hive to handle a large volume of dataset using various algorithms and machine learning to extract values from the medical records that are structured, semi-structured, and unstructured (Raghupathi & Raghupathi, 2014; Wang et al., 2018). Other components to support Hadoop ecosystem include Oozie for workflow, Pig for scripting, and Mahout for machine learning which is part of the artificial intelligence (AI) (Ankam, 2016; Karanth, 2014). Hadoop ecosystem will also include Flume for log collector, Sqoop for data exchange, and Zookeeper for coordination (Ankam, 2016; Karanth, 2014). HCatalog is a required component to manage the metadata in Hadoop (Ankam, 2016; Karanth, 2014). Figure 2 shows the Hadoop ecosystem before integrating Spark for real-time analytics.
1.3.2 Hadoop-specific
File Format for Splittable and Agnostic Compression
The ability of splittable files plays a significant role during the data processing (Grover, Malaska, Seidman, & Shapira, 2015). Therefore, Hadoop-specific file formats of SequenceFile, and Serialization formats like Avro, and columnar formats such as RCFile and Parquet should be used because these files share two essential characteristics that are essential for Hadoop applications: splittable compression and agnostic compression (Grover et al., 2015). Hadoop allows large files to be split for input to MapReduce and other types of jobs, which is required for parallel processing and an essential key to leveraging data locality feature of Hadoop (Grover et al., 2015). The agnostic compression is required to compress data using any compression codec without readers having to know the codec because the codec is stored in the header metadata of the file format (Grover et al., 2015). Figure 3 summarizes the three Hadoop file types with the two common characteristics.
Figure 3. Three Hadoop File Types
with the Two Common Characteristics.
1.3.3 XML and
JSON Use in Hadoop
The clinical data include semi-structured
formats such as XML and JSON. The split process of XML and JSON is not straightforward and can present unique challenges
using Hadoop (Grover et al., 2015). Since and Hadoop does not provide a
built-in InputFormat for either format of XML and JSON (Grover et al., 2015). Furthermore,
JSON presents more challenges to Hadoop than XML because no token is available
to mark the beginning or end of a record (Grover et al., 2015). When using these file format, two primary considerations must be taken.
The container format such as Avro should be used because Avro provides a compact and efficient method to store
and process the data when transforming the data into Avro (Grover et al., 2015).
A library for processing XML or JSON should be designed (Grover et al., 2015).
XMLLoader in PiggyBank library for Pig is an example when using XML data
type. The Elephant Bird project is an
example of a JSON data type file (Grover et al., 2015).
In the age of BD
and BDA, the traditional data store is found inadequate to handle not only the
large volume of the dataset but also the various types of the data format such
as unstructured and semi-structured (Hu et al., 2014). Thus,
Not Only SQL (NoSQL) database is emerged to meet the requirement of the
BDA. These NoSQL data stores are used for modern, and scalable databases (Sahafizadeh & Nematbakhsh, 2015). The scalability feature of the NoSQL data
stores enables the systems to increase the throughput when the demand increases
during the processing of the data (Sahafizadeh & Nematbakhsh, 2015). The platform can incorporate two scalability
types to support the large volume of the datasets; the horizontal and vertical scalability. The horizontal scaling allows the
distribution of the workload across many servers and nodes to increase the
throughput, while the vertical scaling requires more processors, more memories
and faster hardware to be installed on a
single server (Sahafizadeh & Nematbakhsh, 2015).
NoSQL data stores have various types such as MongoDB, CouchDB, Redis, Voldemort, Cassandra, Big Table, Riak, HBase, Hypertable, ZooKeeper, Vertica, Neo4j, db4o, and DynamoDB. These data stores are categorized into four types: document-oriented, column-oriented or column-family stores, graph database, and key-value (EMC, 2015; Hashem et al., 2015). The document-oriented data store can store and retrieve collections of data and documents using complex data forms in various formats such as XML and JSON as well as PDF and MS word (EMC, 2015; Hashem et al., 2015). MongoDB and CouchDB are examples of document-oriented data stores (EMC, 2015; Hashem et al., 2015). The column-oriented data store can store the content in columns aside from rows with the attributes of the columns stored contiguously (Hashem et al., 2015). This type of datastore can store and render blog entries, tags, and feedback (Hashem et al., 2015). Cassandra, DynamoDB, and HBase are examples of column-oriented data stores (EMC, 2015; Hashem et al., 2015). The key-value can store and scale large volumes of data and contains value and a key to access the value (EMC, 2015; Hashem et al., 2015). The value can be complicated, but this type of data stores can be useful in storing the user’s login ID as the key referencing the value of patients. Redis and Riak are examples of the key-value NoSQL data store (Alexandru et al., 2016). Each of these NoSQL data stores has its limitations and advantages. The graph NoSQL database can store and represent data using graph models with nodes, edges, and properties related to one another through relations which will be useful for unstructured medical data such as images, and lab results. Neo4j is an example of this type of graph NoSQL database (Hashem et al., 2015). Figure 4 summarizes these NoSQL data stores, data types for storage, and examples.
Figure 4. Big Data Analytics NoSQL
Data Store Types.
The proposed
design requires one or more NoSQL data stores to meet the requirement of BDA
using Hadoop environment for this healthcare BDA system. Healthcare big data has unique
characteristics which must be addressed
when selecting the data store and consideration must be taken for the various types of data. HBase
and HDFS are the commonly used storage manager in the Hadoop environment (Grover et al., 2015). HBase is a column-oriented data store which will be used to store multi-structured
data (Archenaa & Anita, 2015). HBase sets on top of HDFS in the Hadoop ecosystem framework (Raghupathi
& Raghupathi, 2014).
MongoDB will also be used to store the
semi-structured data set such as XML and JSON. Metadata
for HBase data schema, to improve the accessibility and readability of HBase
data schema (Luo et
al., 2016).
Riak will be used for a key-value dataset which can be used for the dictionary,
hash tables and associative arrays that can be used for login and user ID
information for patients as well as for providers and clinicians (Klein
et al., 2015).
Neo4j NoSQL will be used to store the images with nodes and edges such
as Lab images, XRays (Alexandru
et al., 2016).
The proposed healthcare system has a
logical data model and query patterns that need to be supported by NoSQL
databases (Klein
et al., 2015). The data model will include reading the
medical test results for patients is a core function used to populate the user
interface. It will also include a strong replica
consistency when a new medical result is written for a patient. Providers can make patient care decisions using
these records. All providers will be
able to see the same information within the hospital systems in the four
States, whether they are at the same site as the patients, or providing
telemedicine support from another location.
The logical data model includes mapping the application-specific model
into the particular data model, indexing, and query language capabilities of
each database. The HL7 Fast Healthcare
Interoperability Resources (FHIR) is used
as the logical data model for records analysis.
The patient’s data such as
demographic information such as names, addresses, and telephone will be modeled
using the FHIR Patient Resources such as result quantity, and result units (Klein
et al., 2015).
While the architecture of Hadoop ecosystem has been designed in various scenarios for data storage, data management statistical analysis, and statistical association between various data sources distributed computing and batch processing, this proposal requires real-time data processing which cannot be met by Hadoop alone (Basu, 2014). Real-time analytics will tremendous value to the healthcare proposed system. Thus, Apache Spark is another component which is required to implement this proposal (Basu, 2014). Spark allows in-memory processing for fast response time, bypassing MapReduce operations (Basu, 2014). With Spark integration with Hadoop, stream processing, machine learning, interactive analytics, and data integration will be possible (Scott, 2015). Spark will run on top of Hadoop to benefit from YARN and the underlying storage of HDFS, HBase and other Hadoop ecosystem building blocks (Scott, 2015). Figure 5 shows the core engines of the Spark.
Visualization is
one of the most powerful presentations of
the data (Jayasingh, Patra, & Mahesh, 2016). It helps in viewing the data in a more
meaningful way in the form of graphs, images, pie charts that can be understood
easily. It helps in synthesizing a large
volume of data set such as healthcare data to get at the core of such raw big data and convey the key points
from the data for insight (Meyer, 2018). Some of the commercial visualization tools
include Tableau, Spotfire, QlikView, and Adobe Illustrator. However, the most commonly used visualization
tools in healthcare include Tableau, PowerBI, and QlikView. This healthcare
design proposal will utilize Tableau.
Healthcare providers are successfully transforming data from information to insight using Tableau software. Healthcare organizations can utilize three approaches to get more from the healthcare datasets. The first approach is to break the data access by empowering the departments in healthcare to explore their data. The second approach is to uncover answers with data from multiple systems to reveal trends and outliers. The third approach is to share insights with executives, providers, and others to drive collaboration (Tableau, 2011). It has several advantages including the interactive visualization using drag-n-drop techniques, handling large amounts of data and millions of rows of data with ease, and other scripts such as Python can be integrated with Tableau (absentdata.com, 2018). It also provides mobile support and responsive dashboard. The limitation of Tableau is that it requires substantial training to fully master the platform, among other limitations including lack of automatic refreshing, conditional formatting and 16-column table limit (absentdata.com, 2018). Figure 6 shows the Patient Cycle Time data visualization using Tableau software.
Figure 6. Patient Cycle Time Data
Visualization Example (Tableau,
2011).
Artificial
Intelligence is a computational technique allowing machines to perform
cognitive functions such as acting or reacting to input, similar to the way humans do (Patrizio,
2018).
The traditional computing applications react to data, and the reactions
and responses must be hand-coded with
human intervention (Patrizio,
2018).
The AI systems are continuously in a flux mode changing their behavior
to accommodate any changes in the results and modifying their reactions
accordingly (Patrizio,
2018). The AI techniques can include video
recognition, natural language processing, speech recognition, machine learning
engines, and automation (Mills, 2018)
Healthcare system can benefit from
BDA integration with Artificial Intelligence (AI) (Bresnick, 2018). Since AI
can play a significant role in BDA in the healthcare
system, this proposal suggests the implementation of machine learning which is
part of the AI to deploy more precise and impactful interventions at the right
time in the care of patients (Bresnick, 2018). The
application of AI in the proposed design requires machine learning (Patrizio,
2018).
Since the data used in the AI and machine learning is already cleaned after removing the
duplicates and unnecessary data, AI can take advantages of these filtered data
leading to many healthcare breakthroughs
such as genomic and proteomic experiments to enable personalized medicine (Kersting
& Meyer, 2018).
The healthcare
industry has been utilizing AI, machine learning (ML) and data mining (DM) to
extract value from BD by transforming the large medical datasets into
actionable knowledge performing predictive and prescriptive analytics (Palanisamy & Thirunavukarasu, 2017). The ML will be used to utilize the AI to
develop sophisticated algorithm processing massive medical datasets including
the structured, unstructured, and semi-structured data performing advanced
analytics (Palanisamy & Thirunavukarasu, 2017). Apache Mahout, which is an open source for
ML, will be integrated with Hadoop to facilitate the execution of scalable
machine learning algorithms, offering various techniques such as
recommendation, classification, and
clustering (Palanisamy & Thirunavukarasu, 2017).
Internet of
Things (IoT) refers to the increased connected devices with IP addresses which
were not common years ago (Anand &
Clarice, 2015; Thompson, 2017).
These connected devices collect and use the IP addresses to transmit information (Thompson,
2017).
Providers in healthcare take advantages of the collected information to find new treatment methods and increase
efficiency (Thompson,
2017).
The
implementation of IoT will involve various technologies including frequency
identification (RFID), near field communication (NFC), machine to machine
(M2M), wireless sensor network (WSM), and addressing schemes (AS) (IPv6
addresses) (Anand
& Clarice, 2015; Kumari, 2017).
The implementation of IoT requires machine learning and algorithm to
find patterns, correlations, and anomalies that have the potential of enabling
healthcare improvements (O’Brien,
2016).
Machine learning is a critical component of artificial intelligence. Thus, the success of IoT depends on AI
implementation.
This design
proposal requires various training to IT professionals, providers and clinician
and those who will be using this healthcare ecosystem depending on their role (Alexandru et al., 2016; Archenaa & Anita, 2015). Each component of this
ecosystem should have training such as training for Hadoop/MapReduce, Spark,
Security, and so forth. The training
will play a significant role in the success of this design implementation to
apply BD and BDA in the healthcare system in the four States of Colorado, Utah,
Arizona, and New Mexico. Patients should be considered in training for
remote monitoring programs such as blood sugar monitoring, and blood pressure
monitoring applications. The senior generation might face some
challenges. However, with the technical
support, this challenge can be alleviated.
HBase stores data into table schema and specify the column family (Yang, Liu, Hsu, Lu, & Chu, 2013). The table schema must be predefined, and the column families must be specified. New columns can be added to families as required making the schema-flexible and can adapt to changing application requirements (Yang et al., 2013). HBase is developed in a similar way like HDFS with a NameNode and slave nodes, and MapReduce with JobTracker and TaskTracker slaves (Yang et al., 2013). HBase will play a vital role in the cluster environment of Hadoop system. In HBase master node called HMaster will manage the cluster, and region servers store portions of the tables and perform the work on the data. The HMaster reflects the Master Server and is responsible for monitoring all RegionServer instances in the cluster and is the interface for all metadata changes. This Master executes on the NameNode in the distributed cluster Hadoop environment. The HRegionServer represents the RegionServer and is responsible for serving and managing regions. The RegionServer runs on a DataNode in the distributed cluster Hadoop environment. The ZooKeeper will assist other machines are selected within the cluster as HMaster in case of a failure, unlike HDFS framework where NameNode has a single point of availability issue. Thus, the data flow between the DataNodes and the NameNodes when integrating HBase on top of HDFS is shown in Figure 7.
Figure 7. HBase Cluster Data Flow (Yang et al., 2013).
The healthcare system integrates four significant components such as HBase, MongoDB, MapReduce, and Visualization. HBase is used for data storage, MongoDB is used for metadata, MapReduce using Hadoop for computation, and data visualization tool. The signal data will be stored in HBase while the metadata and other clinical data will be stored in MongoDB. The data stored in both HBase and MongoDB will be accessible from the Hadoop/MapReduce environment for processing and the data visualization layer as well. One master node and eight slave nodes, and several supporting servers. The data will be imported to Hadoop and processed via MapReduce. The result of the computational process will be viewed through a data visualization tool such as Tableau. Figure 8 shows the data flow between these four components of the proposed healthcare ecosystem.
Figure 8. The Proposed Data Flow Between Hadoop/MapReduce and Other Databases.
Healthcare records have various types of data from structured, semi-structured to unstructured (Luo et al., 2016). Some of these healthcare records are XML-based records in the semi-structured format using tags. XML stands for eXtensible Markup Language (Fawcett, Ayers, & Quin, 2012). Healthcare sector can drive value from these XML documents which reflect semi-structured data (Aravind & Agrawal, 2014). Example of this XML-based patients records shows in Figure 9.
Figure 9. Example of the Patient’s Electronic Health
Record (HL7, 2011)
XML-based records
need to get ingested into Hadoop system for the analytical
purpose to derive value from this semi-structured XML-based data. However, Hadoop does not offer a standard
XML “RecordReader” (Lublinsky, Smith, & Yakubovich, 2013). XML is one of the standard file formats for
MapReduce. Various approaches can be
used to process XML semi-structured data.
The process of ETL (Extract, Transform and Load) can be used to process
XML data in Hadoop. MongoDB is a NoSQL database which is required in this design proposal. It handles XML document-oriented type.
The ETL process in
MongoDB starts with the extract and transform.
The MongoDB application provides
the ability to map the XML elements within the document to the downstream data structure. The application supports the ability to
unwind simple arrays or present embedded documents using appropriate data relationships
such as one-to-one (1:1), one-to-many (1: M), or many-to-many (M: M) (MongoDB, 2018). The
application infers the schema information by examining a subset of documents
within target collections. Organizations
can add fields to the discovered data
model that may not have been present within the subset of documents used for
schema inference. The application infers
information about the existing indexes for collections to be queried.
It prompts or warns of queries
that do not contain any indexes fields.
The application can return a subset of fields from documents using query
projections. For queries against MongoDB
Replica Sets, the application supports
the ability to specify custom MongoDB Read Preferences for individual query
operations. The application then infers information
about sharded cluster deployment and note the shard key fields for each sharded
collection. For queries against MongoDB
Sharded Clusters, the application warns against queries that do not use proper
query isolation. Broadcast queries in a sharded cluster can have a
negative impact on database performance (MongoDB, 2018).
The load process in MongoDB is performed after the extract and transform process. The application supports the ability to write data to any MongoDB deployment whether a single node, replica set or sharded cluster. For writes to a MongoDB Sharded Cluster, the application informs or display an error message to the user if XML documents do not contain a shard key. A custom WriteConcern can be used for any write operations to a running MongoDB deployment. For the bulk loading operations, writing documents in batches using the insert() method can be used using the MongoDB 2.6 version or above, which supports the bulk update database command. For the bulk loading into a MongoDB sharded deployment, the bulk insert into a sharded collection is supported, including the pre-splitting of the collections’ shard key and inserting via multiple mongos processes. Figure 10 shows this ETL process for XML-based patients records using MongoDB.
Figure 10. The Proposed XML ETL Process in MongoDB.
Real-Time streaming can be implemented using any real-time streaming program such as Spark, Kafka, or Storm. This healthcare design proposal will integrate Spark open-source program for the real-time streaming data such as sensing data, from various sources such as intensive care units, remote monitoring programs, biomedical signals. The data from various sources will be flow into Spark for analytics and then imported to the data storage systems. Figure 11 illustrates the data flow for real-time streaming analytics.
The communication flow involves the stakeholders involves in the healthcare system. These stakeholders include providers, insurer, pharmaceutical, and IT professionals and practitioners. The communication flow is centered with the patient-centric healthcare system using the cloud computing technology for the four States of Colorado, Utah, Arizona, and New Mexico. These stakeholders are from these states. The patient-centric healthcare system is the central point for communication. The patients communicate with the central system using the web-based platform, and clinical forums as needed. The providers communicate with the patient-centric healthcare system using resource usages, patient feedback, and hospital visits, and services details. The insurers communicate with the central system using claims database, and census and societal data. The pharmaceutical vendors will communicate with the central system using prescription and drug reports which can be retrieved by the providers from anywhere in these four states. The IT professionals and practitioners will communicate with the central system for data streaming, medical records, genomics, and all omics data analysis and reporting. Figure 12 shows the communication flow between these stakeholders and the central system in the cloud that can be accessed from any of these identified four States.
Figure 12. The Proposed Patient-Centric Healthcare System Communication Flow.
The overall system represents the state-of-the-art healthcare ecosystem system that utilizes the latest technology for healthcare Big Data Analytics. The system is bounded by the regulations and policy such as HIPAA to ensure the protection of the patients’ privacy across the various layers of the overall system. The system integrated components include the Hadoop latest technology with MapReduce and HDFS. The data government layer is the bottom layer which contains three major building blocks: master data management (MDM), data life-cycle management (DLM) components, and data security and privacy management. The MDM component is responsible for data completeness, accuracy, and availability, while the DLM is responsible for archiving the data, maintaining the data warehousing, data deletion, and disposal. The data security and privacy management building block is responsible for sensitive data discovery, vulnerability and configuration assessment, security policies application, auditing and compliance reporting, activity monitoring, identify and access management, and protecting data. The top layers include data layer, data aggregation layer, data analytics layer, and information exploration layer. The data layer is responsible for data sources and content format, while the data aggregation layer involves various components from data acquisition process, transformation engines, and data storage area using Hadoop, HDFS, NoSQL databases such as MongoDB and HBase. The data analytics layer involves the Hadoop/MapReduce mapping process, stream computing, real-time streaming, and database analytics. AI and IoT are part of the data analytics layer. The information exploration layer involves the data visualization layer, visualization reporting, real-time monitoring using healthcare dashboard, and clinical decision support. Figure 13 illustrates the overall system diagram with these layers.
Figure 13. The Proposed Healthcare Overall System Diagram.
Healthcare data
must be stored in a secure storage area to protect the information and the
privacy of patients (Liveri, Sarri, & Skouloudi, 2015). When the healthcare
industry fails to comply with the regulation and policies, the fines and the
cost can cause financial stress on the industry (Thompson, 2017). Records
showed that the healthcare industry paid millions of dollars in fines. The Advocate
Health Care in suburban Chicago agreed to the most
significant figure as of August 2016 with a total amount of $5.55
million (Thompson, 2017). Memorial
Health System in southern Florida became the second entity to top of paying $5
million (Thompson, 2017). Table 2 shows the five most substantial fines posted to the Office of Civil Rights (OCR)
site.
Table 2. Five Largest Fines Posted to OCR Web Site (Thompson, 2017)
The hospitals must adhere to the data privacy regulations and legislative rules carefully to protect the patients’ medical records from data breaches (HIPAA). The proper security policy and risk management must be implemented to ensure the protection of private information as well to minimize the impact of confidential data in case of loss or theft (HIPAA, 2018a, 2018c; Salido, 2010). The healthcare system design proposal requires the implementation of a system for those hospitals or providers who are not compliant with the regulation and policies and the escalation path (Salido, 2010). This design proposal implements four major principles as the best practice to comply with required policies and regulation and protect the confidential data assets of the patients and users (Salido, 2010). The first principle is to honor policies throughout private data life (Salido, 2010). The second principle for best practice in healthcare design system is to minimize the risk of unauthorized access or misuse of confidential data (Salido, 2010). The third principle is to minimize the impact of confidential data loss, while the fourth principle is to document appropriate controls and demonstrate their effectiveness (Salido, 2010). Figure 14 shows these four principles which this healthcare design proposal adheres to ensure protection healthcare data from unauthorized users and comply with the required regulation and policies.
Figure 14. Healthcare Design Proposal Four Principles.
This design
proposal assumes that the healthcare sector in the four States will support the
application of BD and BDA across these fours States. The support includes investment in the proper
technology, proper tools and proper training based on the requirements of this
design proposal. The proposal also
assumes that the stakeholders including the providers, patients, insurer,
pharmaceutical vendors, and practitioners will welcome the application of BDA
to take advantages of it to provide efficient healthcare services, increase
productivity, decrease costs for healthcare sector as well as for patients, and
provide better care to patients.
The
limitation of this proposal is the timeframe that is required to implement
it. With the support of the healthcare
sector from these four States, the implementation can be expedited. However, the
silo and the rigid culture of the healthcare may interfere with the
implementation which can take longer than
expected. The initial implementation
might face unexpected challenges. However, these unexpected challenges will
come from the lack of experienced IT professionals and managers in the field of
BD and BDA domain. This design proposal
will be enhanced based on the observations from the first few months of the
implementation.
The
traditional database and analytical systems are
found inadequate when dealing with healthcare data in the age of BDA. The characteristics of the healthcare datasets
including the large volume medical records, the variety of the dataset from
structured, to semi-structured, to the unstructured
dataset, and the velocity of the dataset generation and the data processing
requires technology such as cloud computing (Fernández
et al., 2014). Cloud computing is found the
best solution when dealing with BD and BDA to address the challenges of BD
storage, and the intensive-computing processing demands (Alexandru
et al., 2016; Hashem et al., 2015). The healthcare system in the four States will
shift the communication technology and services for applications across the
hospitals and providers (Hashem
et al., 2015). Some of the advantages of cloud computing adoption include virtualized resources, parallel processing,
security and data service integration with scalable data storage (Hashem
et al., 2015). With the cloud computing technology, the
healthcare sector in the four States will reduce the cost, and increase the
efficiency (Hashem
et al., 2015). When quick access to critical data for
patients care is required quickly, the mobility of accessing the data from
anywhere is one of the most significant advantages of the cloud computing
adoption as recommended by this proposed design
(Carutasu,
Botezatu, Botezatu, & Pirnau, 2016). The benefits of cloud
computing include technological benefits such as visualization, multi-tenancy,
data and storage, security and privacy compliance (Chang, 2015). The cloud computing
also offers economic benefits such as pay per use, cost reduction, return on
investment (Chang, 2015).
The non-functional benefits of the cloud computing cover the elasticity,
quality of service, reliability, and availability (Chang, 2015).
Thus, the proposed design justifies the use of cloud computing for
several benefits as cloud computing is proven the best technology for BDA
especially for healthcare data analytics.
Although
cloud computing offers several benefits to the proposed healthcare system,
cloud computing has been suffering from security and privacy concerns (Balasubramanian & Mala, 2015; Kazim & Zhu,
2015). The security concerns
involve risk areas such as external data storage, dependency on the public
internet, lack of control, multi-tenancy
and integration with internal security (Hashizume, Rosado, Fernández-medina, & Fernandez,
2013). The traditional
security techniques such as identity,
authentication, and authorization are not sufficient for cloud computing
environments in their current forms using the standard deployment models of the
public cloud, and private cloud (Hashizume et al., 2013). The increasing trend in the security threats
data breaches, and the current deployment models of private and public clouds,
which are not meeting the security challenges, have triggered the need for
another deployment to ensure security and privacy protection. Thus, the VPC deployment model which is a new
deployment model of cloud computing technology (Botta et al., 2016; Sultan, 2010; Venkatesan, 2012; Zhang,
Q., Cheng, & Boutaba, 2010). The VPC is taking advantages of technologies
such as a virtual private network (VPN) which will allow hospitals and
providers to set up their required network settings such as security (Botta et al., 2016; Sultan, 2010; Venkatesan, 2012; Zhang,
Q. et al., 2010). The VPC deployment model will have dedicated
resources with the VPN to provide the required isolation for security to
protect the patients’ information (Botta et al., 2016; Sultan, 2010; Venkatesan, 2012; Zhang,
Q. et al., 2010).
Thus, this proposed design will be using VPC cloud computing deployment mode to
store and use healthcare data in a secure and isolated environment to protect
the patients’ medical records (Regola & Chawla, 2013).
Hadoop ecosystem
is a required component in this proposed design for several reasons. Hadoop technology is a commonly used
computing paradigm for massive volume
data processing in the cloud computing (Bansal et al., 2014; Chrimes et al., 2018; Dhotre et al., 2015). Hadoop is the only technology that enables large healthcare volumes of data to be stored in its native forms (Dezyre, 2016). Hadoop is proven to develop better treatments
for diseases such as cancer by accelerating the design and testing of effective
treatments tailored to patients, expanding genetically based clinical cancer
trials, and establishing a national cancer knowledge network to guide treatment
decision (Dezyre, 2016). With Hadoop system, hospitals in the four
States will be able to monitor the patient vitals (Dezyre, 2016). The Children’s Healthcare of Atlanta is an example of using the Hadoop ecosystem to treat over six thousand
children in their ICU units (Dezyre, 2016).
The proposed
design requires the integration of NoSQL database because it offers benefits
such as mass storage support, reading and writing operations which are fast,
and the expansion is easy with a low cost
(Sahafizadeh & Nematbakhsh, 2015). HBase is proposed as a
required NoSQL database as it is faster when reading more than six million
variants which are required when
analyzing large healthcare datasets (Luo et al., 2016). Besides, query engine such as SeqWare can be integrated with HBase as
needed to help bioinformatics researchers
access large-scale whole-genome datasets (Luo et al., 2016). HBase can store clinical sensors where the
row key serves as the time stamp of a single value, and the column stores
patients’ physiological values that correspond with the row key time stamp (Luo et al., 2016). HBase is scalable, high-performance
and low-cost NoSQL data store that can be integrated with Hadoop sitting on top
of HDFS (Yang et al., 2013). As a column-oriented
NoSQL data store that runs on top of HDFS of Hadoop ecosystem, HBase is well suited to parse the healthcare large data
sets (Yang et al., 2013). HBase supports
applications written in Avro, REST and Thrift (Yang et al., 2013). MongoDB is another NoSQL data store, which
will be used to store metadata to improve the accessibility and readability of
the HBase data schema (Luo et al., 2016).
The integration of
Spark is required in order to overcome
the Hadoop limitation of real-time data processing
because Hadoop is not optimal for real-time data processing (Guo,
2013). Thus, Apache Spark is a required component to
implement this proposal so that the healthcare BDA system can take advantages
of data processing at rest using the batching technique as well as a motion using the real-time processing
technique (Liang
& Kelemen, 2016).
Spark allows in-memory
processing for fast response time, bypassing MapReduce operations (Liang
& Kelemen, 2016).
Spark is a high integration to the recent Hadoop cluster deployment (Scott,
2015).
While Spark is a powerful tool on its own for processing a large volume of medical and healthcare
datasets, Spark is not well-suited for production workload. Thus, the integration of Spark with Hadoop
ecosystem provides many capabilities
which Spark cannot offer on its own, and Hadoop cannot offer on its own.
The integration of
AI as part of this proposal is justified by the examination of Harvard Business
Review (HBR) that shows ten promising AI application in healthcare (Kalis, Collier, & Fu, 2018). The findings of HBR’s
examination showed that the application of AI could create up to $150 billion
in annual savings for U.S. healthcare by 2026 (Kalis et al., 2018). The
result also showed that AI currently creates the most value in assisting the
frontline clinicians to be more productive and in making back-end processes more
efficient (Kalis et al., 2018).
Furthermore, IBM invested $1 billion in AI through the IBM Watson Group, and healthcare industry is
the most significant application of Watson (Power, 2015).
Big Data and Big
Data Analytics have played significant roles in various industries including
the healthcare industry. The value that is driven by BDA can save
lives and minimize costs for patients.
This project proposes a design to apply BDA in the healthcare system
across four States of Colorado, Utah, Arizona, and New Mexico. Cloud
computing is the most appropriate technology to deal with the large volume of
healthcare data. Due to the security issue of the cloud computing, the Virtual
Private Cloud (VPC) will be used. VPC provides a secure cloud environment using
network traffic security setup using security groups and network access control
lists.
The project
requires other components to be fully implemented using the latest technology
such as Hadoop and MapReduce for data streaming processing, machine learning
for artificial intelligence, which will be used
for Internet of Things (IoT). The NoSQL
database HBase and MongoDB will be used to handle the semi-structured data such
as XML and unstructured data such as logs and images. Spark will be
used for real-time data processing which can be vital for urgent care
and emergency services. This project
addressed the assumptions and limitations plus the justification for selecting
these specific components.
In summary, all
stakeholders in the healthcare sector
including providers, insurers, pharmaceuticals, practitioners should cooperate
and coordinate to facilitate the implementation process. All stakeholders are responsible to
facilitate the integration of BD and BDA into the healthcare system. The rigid culture and silo pattern need to
change for better healthcare system which can save millions of dollars to the
healthcare industry and provide excellent care to the patients at the same
time.
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The purpose of this discussion is to
address the relationship between the Internet of Things (IoT) and the
Artificial Intelligence (AI), and whether one can be used efficiently without
the help from the other. The discussion
begins with the Internet of Things (IoT)
and artificial intelligence (AI) overview, followed by the relationship between
them.
Internet of Things (IoT) and Artificial Intelligence
Overview
Internet of Things (IoT) refers to
the increased connected devices with IP addresses that years ago were not
common (Anand & Clarice, 2015;
Thompson, 2017). The connected devices collect and use these IP addresses to transmit information (Thompson, 2017). Organizations take
advantages of the collected information for innovation, enhancing customer
service, optimizing processes (Thompson, 2017). Providers in healthcare take advantages of the collected information
to find new treatment methods and
increase efficiency (Thompson, 2017).
IoT implementation involves various
technologies such as radio frequency identification
(RFID), near field communication (NFC), machine to machine (M2M), wireless
sensor network (WSM), and addressing schemes (AS) (IPv6 addresses) (Anand & Clarice, 2015;
Kumari, 2017). The RFID uses electromagnetic fields to
identify and track tags attached to objects.
The NFC is a set of thoughts and technologies where smartphones and other objects want to communicate
under IoT. The M2M is used often for
remote monitoring. WSM is a set of a large
number of sensors used to monitor environmental conditions. The AS is the primary
tool which is used in IoT and giving IP addresses to each object which wants to
communicate (Anand & Clarice, 2015;
Kumari, 2017).
Machine learning (ML) is a subset of
AI. Machine learning (ML) involves
supervise and unsupervised ML (Thompson, 2017). In the AI domain, the advances in computer science
result in creating intelligent machines that resemble humans in their functions
(NMC, 2018). The access to
categories, properties, and relationships between various datasets help develop
knowledge engineering allowing computers to simulate the perception, learning,
and decision making of human (NMC, 2018). The ML enables
computers to learn without being explicitly programmed (NMC, 2018). The unsupervised ML
and AI allow for security tools such as behavior-based-analytics and anomaly
detection (Thompson, 2017). The neural network
of AI help model the biological function of the human
brain to interpret and react to specific inputs such as words and tone of voice
(NMC, 2018). The neural networks
have been used for voice recognition, and
natural language processing (NLP), enabling a human
to interact with machines.
The Relationship Between IoT and AI
Various reports and studies have
discussed the relationship between IoT and AI.
(O’Brien, 2016) has reported the need of IoT to AI to succeed. (Jaffe, 2014) suggested the same
thing that IoT will not work without AI.
IoT future depends on ML to find patterns, correlations, and anomalies
that have the potential of enabling improvement in almost every facet of the daily lives (Jaffe, 2014).
Thus, the success of IoT depends on
AI. IoT follows
five necessary steps: sense, transmit,
store, analyze and act (O’Brien, 2016). AI plays a significant role in the analyzing step, where the ML which is the subset of AI gets
involved in this step. When ML is applied in the analysis step, it can change
the subsequent step of “act” which dictates whether the action has high value
or no value to the consumer (O’Brien, 2016).
(Schatsky, Kumar, & Bumb,
2018)
suggested the AI can unlock the potential of IoT. As cited in (Schatsky et al., 2018), Gartner predicts by 2022, more than 80% of enterprise IoT
projects will include AI components which are
up from only 10% in 2018. International
Data Corp (IDC) predicts by 2019, AI will support “all effective” IoT efforts, and without AI, data from the deployments will
have limited value (Schatsky et al., 2018).
Various companies are crafting an IoT strategy to include AI (Schatsky et al., 2018). Venture capital
funding of AI-focused IoT start-ups is growing, while vendors of IoT platforms
such as Amazon, GE, IBM, Microsoft, Oracle, and Salesforce are integrating AI capabilities (Schatsky et al., 2018). The value of AI is
the ability to extract insight from data quickly.
The ML, which is a subset of AI, enables the automatic identification of
patterns and detected anomalies in the data that smart sensors and devices
generate (Schatsky et al., 2018). IoT is expected to
combine with the power of AI, blockchain, and other emerging technologies to
create the “smart hospitals” of the future (Bresnick, 2018). Example of
AI-powered IoT devices includes automated
vacuum cleaners, like that of the iRobot Roomba, smart thermostat solutions,
like that of Nest Labs, and self-driving cars, such as that of Tesla Motors (Faggella, 2018; Kumari,
2017).
Conclusion
This discussion has addressed artificial intelligence (AI) and the internet of things (IoT) and the relationship between them. Machine learning which is a subset of AI is required for IoT at the analysis phase. Without this analysis phase, IoT will not provide the value-added insight organizations anticipate. Various studies and reports have indicated that the success and the future of IoT depend on AI.
The purpose of this discussion is to discuss the influence of artificial intelligence on big data analytics. As discussed in the previous discussion, Big Data empowers artificial intelligence. This discussion is about the impact of artificial intelligence in the Big Data Analytics domain. The discussion begins with artificial intelligence building blocks and big data building blocks, following by the impact of the artificial intelligence in the BDA.
Artificial Intelligence Building Blocks and Their Impact on
BDA
Understanding the building blocks of
AI could help understand the impact of AI on BDA. Various reports and studies have identified
various building blocks for AI. Four
building blocks have been identified
In (Chibuk, 2018), four building blocks that are expected to shape the next
stage of AI. The computation methodology
is the first building block of AI. This
component is structured in a way to improve the computers move from binary to
infinite connections. The storage of the information is the second building
block of AI improving storing and accessing data in the more efficient form. Brain-computer interface is the third building
block of AI, through which the human
minds would speak silently with a computer, and our thought would turn into
actions. The mathematics and algorithms
form the last building block of AI to include advanced mathematics called
capsule network and having networks to teach each other based on rules defined (Chibuk, 2018).
(Rao, 2017) has identified five fundamental building blocks for AI in the
banking sector, while they can be easily
applicable to other sectors. Machine
learning (ML) is the first component of AI in banking where the software can learn on its own without being programmed
and adjust its algorithms to respond to new insights. The data mining algorithms
hand over findings to a human for further work, while machine learning
can act on its own (Rao, 2017). The financial and
banking industry can benefit from machine learning for fraud detection,
security settlement and alike (Rao, 2017). The deep learning
(DL) is another building block of AI in the banking
industry (Rao, 2017). DL can leverage a
hierarchy of artificial neural networks, similar to the human brain to do its
job. DL mimics the human brain to
perform non-linear deductions, unlike the linearly traditional programs (Rao, 2017). DL can produce
better decisions by factoring learning from previous transactions or
interactions to conclude (Rao, 2017). Example of DL is
the collected information about customers and their behaviors from social
networks, from which their likes and preferences can be inferred, and financial institutions can utilize this insight to
make contextual, relevant offers to those customers in real-time (Rao, 2017). Natural language process (NLP) is the third
building block for AI in banking (Rao, 2017). NLP is a key
building block in AI to help computers learn, analyze and understand human
language (Rao, 2017). NLP can be used to
organize and structure knowledge in order to answer queries, translate content
from one language to another, recognize people by their speech, mine text, and
perform sentiment analysis (Rao, 2017). The natural language generation (NLG) is another essential
building block in AI, which can help computers analyze, understand, and make
sense of human language (Rao, 2017). It can help
converse and interact intelligently with humans (Rao, 2017). NLG can transform
raw data into a narrative, which banks such as Credit Suisse are using to
generate portfolio review (Rao, 2017). Visual recognition
is the last component of AI which help recognize images and their content (Rao, 2017). It uses DL to perform its role of finding faces, tagging
images, identifying the components of visuals, and picking out similar images
from a large dataset (Rao, 2017). Various banks such as Australia’s Westpac is using this
technology to allow customers to activate their new card from their smartphone
camera, and Bank of America, Citibank, Wells Fargo, and TD Bank are using this technology of visual recognition to
allow customers to deposit checks remotely via mobile app (Rao, 2017).
(Gerbert, Hecker,
Steinhäuser, & Ruwolt, 2017) have identified ten building blocks for AI. They have suggested that the simplest AI use
cases often consist of a single building
block. However, they often evolve to combine two or more blocks over time (Gerbert et al., 2017). The machine vision is
one of the building blocks of AI. The machine vision building block of AI is
the classification and tracking of real-world objects based on visual, x-ray,
laser or other signals. The quality of
machine vision depends on the labels of a large number
of reference images which is performed by a human
(Gerbert et al., 2017). Video-based
computer vision is anticipated to recognize actions and predict motions within
the next five years (Gerbert et al., 2017). The speech
recognition is another building block which involves the transformation of
auditory signals into text (Gerbert et al., 2017). Siri and Alexa can identify most words in a general
vocabulary, but as vocabulary becomes specific, tailored programs such as the
PowerScribe of Nuance for radiologist will be needed (Gerbert et al., 2017).
Information processing building block of
AI involves searching billions of
documents or constructing basic knowledge
graphs identifying relationships in text.
This building block is closely related to NLP, which is also identified
as another building block of AI (Gerbert et al., 2017). NLP can provide
basic summaries of text and infer intent in some instances (Gerbert et al., 2017). Learning from data is another component of AI, which is a
machine learning and able to predict values or classify information based on historical data (Gerbert et al., 2017). While ML is an
element in AI building blocks of machine vision and NLP, it is also a separate
building block of AI (Gerbert et al., 2017). Other building
blocks of AI include the planning and exploring agents that can help identify
the best sequence of actions to achieve certain goals. Self-driving cars rely on this building clock
for navigation (Gerbert et al., 2017). The image
generation is another building block of AI, which is the opposite of machine
vision block, as it creates images based on models. Speech generation is another building block
of AI which covers both data-based text generation and text-based speech
synthesis. The handling and control building block of AI refers to interactions
with real-world objects (Gerbert et al., 2017). The navigating and movement building block of AI covers
the ways where robots move through a given physical environment. The
self-driving cars and drones do well with their wheels and rotors. However, walking on legs especially a single
pair of legs is challenging (Gerbert et al., 2017).
Artificial Intelligence (AI) and machine learning (ML) have observed an increasing trend across industries, and public sector (Brook, 2018). Such increasing trend plays a significant role in the digital world (Brook, 2018). This increasing trend is driven by the customer-centric view of data involving use data as part of the product or service (Brook, 2018). The customer-centric model assumes data enrichment with data from multiple sources, and the data is divided into real-time data and historical data (Brook, 2018). Businesses build a trust relationship with customers, where data is becoming the central model for many consumer services such as Amazon, and Facebook (Brook, 2018). The data value increases over time (Brook, 2018). The impact of machine learning and artificial intelligence have driven the need for “corporate memory” to be rapidly adopted in organizations. (Brook, 2018) have suggested organizations implement loosely coupled data silos and data lake which can contribute to the corporate memory and the super-fast data usage in the age of AI-driven data usage. Various examples of AL and ML impact on BDA and the value of data over time include Coca-Cola’s global market and extensive product list, IBM’s machine learning system Watson, GE Power using BD, ML, and internet of things (IoT) to build internet of energy (Marr, 2018). Figure 1 shows the impact of AI and ML on Big Data Analytics and the value of the data over time.
Figure 1. Impact of AI and ML on BDA and
the Value of Data Overtime (Brook, 2018).
AI is anticipated to be the most dominant factor that
will have a disruptive impact on organizations and businesses (Hansen, 2017). (Mills, 2018) has suggested that organizations need to
embrace BD and AI to help their businesses.
EMC survey has shown that 69% of information technology decision-makers in New Zealand believe that BDA
is critical to their business strategy, and 41% already incorporated BD into the
everyday business decision (Henderson, 2015).
The application of AI to BDA can assist businesses and organizations to detect a correlation between factors humans cannot perceive (Henderson, 2015). It can allow organizations to deal with the speed of the information change today in the business world (Henderson, 2015). AI can help organization add a level of intelligence to their BDA to understand complex issues better quicker than humans can in the absence of AI (Henderson, 2015). AI can also serve to fill the gap left by not having enough data analysts available (Henderson, 2015). AI can also reveal insights that can lead to novel solutions to existing problems or even uncover issues that are not previously known (Henderson, 2015). A good example of AI impact on BDA is the AI-powered BDA in Canada which is used to identify patterns in the vital signs of premature babies that can be used in the early detection of life-threatening infections. Figure 2 shows AI and BD working together for better analytics and better insight.
Figure 2: Artificial Intelligence and
Big Data (Hansen, 2017).
Conclusion
This assignment has discussed the impact of artificial intelligence (AI) on Big Data Analytics (BDA). It began with the identification of the building blocks of the AI and the impact of each building block on BDA. BDA has an essential impact on AI as it empowers it, and AI has a crucial role in BDA as demonstrated and proven in various fields especially in the healthcare and financial industries. The researcher would like to summarize this relationship between AI and BDA in a single statement: “AI without BDA is lame, and BDA without AI is blind.”
References
Brook, P. (2018). Trends in Big Data
and Artificial Intelligence Data
Chibuk, J. D.
(2018). Four Building Blocks for a General AI.
Gerbert, P.,
Hecker, M., Steinhäuser, S., & Ruwolt, P. (2017). The Building Blocks of
Artificial Intelligence.
Hansen, S.
(2017). How Big Data Is Empowering AI and Machine Learning?
Henderson, J.
(2015). Insight: What role does Artificial Intelligence Play in Big Data? What are the links between artificial
intelligence and Big Data?
Marr, B. (2018).
27 Incredible Examples Of AI And Machine Learning In Practice.
Mills, T. (2018).
Eight Ways Big Data And AI Are Changing The Business World.
Rao, S.
(2017). The Five Fundamental Building Blocks for Artificial Intelligence in
Banking.
The purpose of this discussion is to address whether the
combination of Big Data and Artificial
Intelligence is significant to any industry.
The discussion also provides an example where Artificial Intelligence has been used and applied
successfully. The chosen sector for such
the use of AI is the health care.
The
Significance of Big Data and Artificial Intelligence Integration
As discussed in U4-DB2, Big Data
empowers artificial intelligence. Thus, there is no doubt about the benefits and
advantages of utilizing Big Data in artificial intelligence for
businesses. However, in this discussion,
the question is whether the significance of their combination in any industry or specific industries only.
McKinsey Global Institute reported in 2011 that not all industries are created equal when parsing the benefits of Big Data (Brown, Chui, & Manyika, 2011). The report has indicated that although Big Data is changing the game for virtually every sector, it favors some companies and industries over the others, especially in the early stages of the adoption. McKinsey has also reported in (Manyika et al., 2011) five domains that could take advantages of the transformative potential of Big Data. These domains include for U.S. healthcare, retail, and public sector administration, retail for European Union, and personal location data globally. Figure 1 illustrates the value of Big Data significant financial value across sectors.
Figure 1. Big Data Financial Value
Across Sectors (Manyika et al., 2011).
Thus, the value of Big Data Analytics is tremendous already for almost every business, and the value varies from one sector to another. The combination of Big Data and artificial intelligence is good for innovation (Bean, 2018; Seamans, 2017). There is no limit for innovation for any business. Figure 2 shows the 19-year Go player Ke Jie reacts during the second match against Google’s artificial intelligence program AlphaGo in Wuzhen.
Figure 2. 19-year old Ke Jie Reacts
During the Second Match Against Google’s Artificial Intelligence Program
AlphaGo (Seamans, 2017).
If the combination of Big Data and
artificial intelligence is good for
innovation, then, logically every organization and every sector need
innovations to survive the competition.
In the survey conducted by NewVantage Partner, 97.2% of the executive
decision-makers have reported that their companies
are investing in building or launching Big Data and Artificial Intelligence
initiatives (Bean, 2018; Patrizio, 2018). It is also worth noting that 76.5% of the
executives have indicated that the availability
of Big Data is empowering AI and cognitive initiatives
within their organizations (Bean, 2018). The same survey has also shown 93% of the
executives have identified artificial intelligence as the disruptive technology
and their organizations are investing in for the future. This result shows that a common consensus among the executives that
organizations must leverage cognitive technologies to compete in an
increasingly disruptive period (Bean, 2018).
AI
Application Example in the Health Care
Industry
Since healthcare
industry has been identified in various
research studies about its great benefits from Big Data and artificial
intelligence, this sector is chosen as an
example of the application of both BD and AI for this discussion. AI is
becoming a transformational force in healthcare (Bresnick, 2018). The healthcare industry has almost endless
opportunities to apply technologies such as Big Data and AI to deploy more
precise and impactful interventions at the right time in the care of patients (Bresnick, 2018).
Harvard Business Review (HBR) has indicated that 121 health AI and machine learning companies raised $2.7 billion in 206 deals between 2011 and 2017 (Kalis, Collier, & Fu, 2018). HBR has examined ten promising artificial intelligence applications in healthcare (Kalis et al., 2018). The findings have shown that the application of AI could create up to $150 billion in annual savings for U.S. health care by 2026 (Kalis et al., 2018). The investigation has also shown that AI currently creates the most value in assisting the frontline clinicians to be more productive and in making back-end processes more efficient, but not yet in making clinical decisions or improving clinical outcomes (Kalis et al., 2018). Figure 3 shows the ten AI applications that could change health care.
Figure 3. Ten Application of AI That
Could Change Health Care (Kalis et al., 2018).
Conclusion
In
conclusion, the combination of Big Data and Artificial
Intelligence drives innovations for all sectors. Every sector and every
business need to innovate to maintain a competitive edge. Some sectors are leading in taking the advantages of this combination of BD and AI more
than others. Health care is an excellent example of employing artificial
intelligence. However, the application
of the AI has its most value on three main areas only of AI-assisted surgery,
virtual nurse, administrative workflow. The use of AI in other areas in healthcare is
still in infant stages and will take time until it establishes its root and
witness the great benefits of AI application (Kalis et al., 2018).
Manyika, J.,
Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H.
(2011). Big data: The next frontier for innovation, competition, and
productivity.
Patrizio, A.
(2018). Big Data vs. Artificial Intelligence.
Seamans,
R. (2017). Artificial Intelligence And Big Data: Good For Innovation?
Think and Knowledge Tank 