Information Technology Requirements in Healthcare

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 Big Data Benefits and Challenges

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.

Healthcare Data Sources for Data Analytics

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).

Security Requirement

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

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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Critical Information Technology Solutions Used to Gain Competitive Advantages

Dr. O. Aly
Computer Science

Abstract

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.

Introduction

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 and Big Data Analytics

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.

Hadoop Ecosystem

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.

Figure 1. Hadoop Architecture Overview (Alguliyev & Imamverdiyev, 2014).

NoSQL Databases

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.

Spark Integration for Real-Time Data Processing

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.

Figure 3. Spark Core Engines (Scott, 2015).

Big Data Visualization

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.

Cloud Computing Technology

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).

Cloud Computing Essential Characteristics

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 Three Essential Service Models

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 Four Essential Deployment Models

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.

Enterprise Resource Planning (ERP)

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.

Critical Factors for Successful ERP Implementation

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 and 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, 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)

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.

CRM Challenges and Costs

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).

The Building Blocks of CRM Systems and Their Integration

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.

Best Practices in Implementing These CRM Systems

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).

Conclusion

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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Two Good Quality Research Papers On Customer Relationship Management (CRM)

Dr. O. Aly
Computer Science

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.

The Trade-off Between Cost, Time and Quality

Dr. O. Aly
Computer Science

Abstract

The purpose of this project is to discuss the trade-off between cost, time and quality of projects. Various essential topics related to projects and project management are discussed. The discussion begins with the distinct characteristics of the projects and operations, among which projects are temporary while operations are repetitive. The project addressed the project cycle plan and project development tools. Various tools for project management include project evaluation and review techniques (PERT), critical path method (CPM) and Gantt Chart. The project management with the trade-off between time, cost and quality are addressed. A balance of these three critical elements is required. This project discusses the project trade-off and the correlation between time and cost. Some argue that most businesses are cost-time bias at the expense of quality. Various projects success factors are also discussed in this project. Various factors cause projects to fail. These factors include misunderstanding of the project requirement, organizational influences, and risk management. Failed projects take a long time to be abandoned or corrected due to logistical problems, political thinking and lack of planning for uncertainty, and risk management.

Keywords: Project Management, Cost, Time, Quality.

Introduction

Enterprises achieve their strategic goals using various project management techniques. Business requires good performance assessment tools for project management to make sound decision to gain and maintain a competitive edge in the market (Anuar & Ng, 2011). Management and executives are under pressure to complete projects within a specific time, a specific budget while maintaining the quality, which is considered to be the success factors for project implementation. This project discusses these factors for project management. It begins with the discussion of projects vs. operations, followed by the project cycle plan and project development tools.

Projects vs. Operations

A project is defined as a temporary venture to implement a unique service or product. The temporary indicates a period that has a beginning and ending, while unique indicates the service or product will be distinguished from the ones in the market (Pearlson & Saunders, 2001; PMI, 2000). It is also defined as an organization of people dedicated to a specific purpose or object (Pinto & Slevin, 2015). Projects consist of a set of one-time actions to shift the present event into a new one based on the strategic plan of the enterprise (Pearlson & Saunders, 2001; PMI, 2000). Projects involve substantial, expensive, unique or high risk and must be completed within a time frame using a certain amount of investment (Pinto & Slevin, 2015). Projects need to have well-identified objectives and sufficient resources to implement all the required tasks and activities (Pearlson & Saunders, 2001; PMI, 2000). The successful strategy of the enterprise requires two types of decisions; one for the daily operatives, and another for the strategic objectives. Since IT plays a significant role in all projects of the enterprise, IT project management plays a critical role in the success of the business.

Projects and operations utilize the resources of the business to transform them into profits. Human resources and the flow of resources are required for projects and operations of the business. A project can be divided into sub-projects to implement particular activities such as quality control testing (Pearlson & Saunders, 2001). During this sub-division of a project, sourcing decisions are made to limit costs. Various projects are organized at a high level, and elements of a more extensive program which provide a framework from which competing resource requirements are managed, and priorities among a set of projects are shifted.

Projects and operations have the same elements such as labor skills, training time, worker autonomy, compensation system, material input requirements, supplier ties, raw materials inventory, scheduling complexity, quality control, information flows, worker-management communication, duration and product or service (Pearlson & Saunders, 2001). However, each element has a different characteristic of the project and the operation. For instance, operations require low labor skills, training time, worker autonomy, while projects require them high. Compensation is a lump sum for projects, while hourly or weekly wage for operations. Material input requirements for operations require a high degree of certainty, while projects are uncertain. Information flows, and worker-management communication is essential in projects, while less critical in operations. The duration is on-going for operations, while temporary for projects. The product or service is repetitive in operation, while unique in projects. Table 1 shows the characteristics of operations and projects (Pearlson & Saunders, 2001).

Table 1. Projects vs. Operations (Pearlson & Saunders, 2001).

Project Cycle Plan and Project Development Tools

Enterprises develop the operations of the business based on a strategic plan that has goals and objectives (Wilson, 2015). Resources get acquired and managed to implement the plan. The project plan is comprised of sequential steps for organizing and tracking the work of the team which implements the project, while the project management contains a set of tools to balance the competing demands for resources and ensure the completion of the work at every step and evolves throughout the project plan (Pearlson & Saunders, 2001).
The project cycle plan organizes the activities of the project and sequences them in steps along a timeline so that the project delivers based on the requirements of the stakeholders and customers. The plan is bounded by a critical beginning and end dates and breaks the work into phases (Pearlson & Saunders, 2001). The plan identifies the resources and time required to complete the work based on the scope of the project. The tasks are identified and assigned to team members. The management tracks the progress and the phases of the project and coordinates the eventual transition from the project to operational status, a project that leads to the milestone of the project by delivering it. The project progress is monitored to ensure it meets the requirements of the cost, time, and quality. If the project does not meet the requirements, some corrections must be made, and the cycle gets adjusted as required (Copertari, 2002; Pearlson & Saunders, 2001).
Various approaches and software tools exist for the development of the project. Three main approaches include project evaluation and review techniques (PERT), critical path method (CPM), and Gantt Chart (Pearlson & Saunders, 2001). PERT method identifies the tasks of the project, orders the tasks in a time sequence, identifies the interdependencies of the tasks, and estimates the time which is required to complete each task. Tasks are divided into critical and non-critical. The critical tasks must be performed individually and together impact the total elapsed time of the project, while the non-critical tasks include slack time without impacting the duration of the entire project. Figure 1 shows an example of a PERT chart for a project plan.

Figure 1. PERT Chart (Pearlson & Saunders, 2001).

The CPM is another project planning and scheduling tools. CPM is similar to PERT. However, unlike PERT, CPM can identify relationships between costs and completion date of a project, the amount and value of resources which can be applied as alternatives (Pearlson & Saunders, 2001). CPM and PERT are different in term of time estimates. PERT develops broad estimates about the time needed to complete the tasks of the project, calculating the optimistic, most probable and pessimistic time estimates for each task. CPC, in contrast, assumes that all time requirements for completion of each task are relatively predictable. CPM tends to be used on projects for which direct relationships can be established between time and costs.

Gantt charts are used mostly for displaying time relationships of the tasks of the project and for monitoring the progress toward project completion. Gantt charts list project task with a bar for each task indicating the relative amount of time expected to complete the task (Pearlson & Saunders, 2001). The due date for completion is regarded as a milestone and noted with diamonds. Gantt charts are useful for planning purpose at the beginning of the project. When the project progresses, the chart is altered to reflect the extent to which each task is completed at the time the project is monitored. Figure 2 illustrates an example of a Gantt chart for a project.

Figure 2. Gantt Chart (Pearlson & Saunders, 2001).

Project Management

Project management is defined as the application of skills, knowledge, techniques, and tools to implement activities to meet or exceed the needs of the stakeholders and the expectation from a project (Pearlson & Saunders, 2001). Project management involves a continuous trade-off between cost, quality and time. Managers and executives are confronted with a serious decision among these triangle constraints for projects implementation, involving the scope of the project. The scope can be divided into product scope and project scope. The product scope includes a detailed description of the quality of the product, features, and functions, while the project scope involves the work required to deliver a product or service with the intended product scope. Time refers to the period that is required to complete a project, while cost involves all the required resources to implement the project. Figure 3 shows the triangle of project management.

Figure 3. Project Triangle (Pearlson & Saunders, 2001).

Any modification in any of these three sides of the project triangle can have an impact on either side or both of the other sides. For instance, if the scope of the project increases, more time and more cost will be required to implement the additional work. The increase in the scope after the project started is known as scope creep. One or two of these project triangle elements can be optimized, modifying the third to maintain the balance. For instance, a project with a fixed time and a fixed budget can restrict the scope, while a project with a short time and a broad scope need budget flexibility. The trade-off among these project elements plays a crucial role in business, as it can lead to a disastrous event such as Titanic. The use of substandard low-grade rivets makes ships sink when hitting an iceberg. The history showed that the quality trade-off to using these low-grade reverts to lower the cost of some parts of Titanic causes a disastrous event. Managers and executives are under pressure to balance among these project elements to ensure the success of the project and eventually the success of the business.

Project Trade-off and the Correlation Between Time and Cost

The nature of the underlying tradeoffs can be illustrated using a systematic approach (Copertari, 2002). The systematic relationship between time and cost is illustrated in Figure 4 (a). If the project is delayed, it costs more money which is supported by studies such as (Anuar & Ng, 2011; Atkinson, 1999; Bowen, Cattel, Hall, Edwards, & Pearl, 2012). This relationship is a positive correlation between time and cost. Additional resources are required to deliver on time which can be directed to critical activities. Limited resources should be directed to non-critical activities, which is called crashing and it has a negative correlation between cost and time. The nature of the activities as critical and non-critical and the existing of both positive and negative correlation implies the existence of an equilibrium where an optimal project completion time is achieved at a minimum cost. Figure 4 (b) illustrates how the time/cost tradeoff is influenced by performance. The quality can be improved by using more resources, which increases the financial cost and will increase the time if such resources are limited. However, if more resources are invested and the project is taken more time to complete, the cost increases, the Internal Rate of Return (IRR) of the project measuring the profitability is reduced. Thus, enterprises must maintain an optimal time/cost tradeoff that can yield optimal project performance as measured by its IRR (Copertari, 2002).

Figure 4. Time, Cost and Performance Tradeoffs (Copertari, 2002).

Project Success Factors

Various studies discussed various factors affecting the success of projects. (Thamhain, 2004) examined the influences of the project environment on team performance. The result showed that a general agreement existed on the factors that drive team performance, and a large number of performance factors derived from the human side is the most significant findings. Project success is based on the effectiveness of multi-disciplinary efforts across various teams (Thamhain, 2004). (Hong, 2011) suggested that the initiation and planning phases of capital projects impact the outcome of completed cost, time and profitability. (Bonner, Ruekert, & Walker Jr, 2002) examined formal and interactive control mechanisms available to upper-managers in controlling new product development (NDP) projects, and the relationship between these techniques and the NDP project performance. The findings indicated that the degree to which upper-management intervened in project-level during the project was negatively related to project performance. The results also showed support for the notion that early and interactive decision-making on control mechanisms is critical for effective projects.

Other studies discussed cost, time and quality as success factors for project implementation and management. (Atkinson, 1999) indicated that the Iron Triangle of time, cost, and quality is still preferred success criteria for projects. Time is an intangible resource binding the period of the project from the start to the completion (Anuar & Ng, 2011; Pearlson & Saunders, 2001). Time plays a significant role in the success of the project as it is regarded as a significant criterion for project success (Anuar & Ng, 2011; Bowen et al., 2012). The longer the project takes, the potential damage is expected, the more complex and costly the corrective measures will be to the project. Some argue that the projects with a short time frame for completion have advantages cost and performance wise, while others argue that when the projects are under time and cost pressure, the quality is profoundly affected (Anuar & Ng, 2011; Pollack-Johnson & Liberatore, 2006). (Bowen et al., 2012) suggested that time-cost bias exist, indicating quality is last to consider.

Every project requires financial resources reflecting the costs. The cost of the project plays another significant role in the success of projects implementation (Westland, 2018; Wilson, 2015). Some suggest that when the cost increases when the duration is shortened, and vice versa. However, most large and complex project development require substantial financial resources and schedule overrun (Anuar & Ng, 2011). The delayed and more time projects require more financial resources (Bowen et al., 2012; Shankar, Raju, Srikanth, & Bindu, 2014).

Products or service without quality can bring a business down. Quality is defined as one of the components that contribute to value for money (Bowen et al., 2012). Enterprises must pay attention to the quality of products and services. The high failure rates of quality suggest that the knowledge of the transformation process whereby ideas are turned into successful quality products and services is far from perfect (Anuar & Ng, 2011). Organizations are under pressure to introduce new products and adopt new processes to gain and maintain competitive advantages.

(Anuar & Ng, 2011) analyzed three different scenarios and modeling using Microsoft Office Project tool. The first scenario is about project fixed time with limited resources. The second scenario is about project time reduced with minimus cost imposed. The last scenario is about maintaining quality while reducing the project duration. The findings of the first scenario showed that cost was controlled very tightly even though the time of the project was not required to be reduced. These findings are similar to the findings of (Olson, Walker Jr, Ruekerf, & Bonnerd, 2001). The findings of the second scenario showed that the reduced time of the project could reduce the cost of the project. The findings of the last scenario showed that a shorter duration was not considered due to the risks of having quality issues (Nidumolu, 1996) argued that the tight control of the process could result in strict adherence to time and cost estimates. Such control impacts the functionality of the product, thereby the long-term flexibility of technology is jeopardized with the short-term user needs.

Project Failures

Various studies discussed reasons for projects management failure. (Atkinson, 1999) identified two types of errors for project failure; Type I and Type II. Type I errors occur when something is done wrong, while Type II errors occur when something has not been done as well as it could have been or something was missed. (Gardiner & Stewart, 2000) examined the relationship between project budgets, cash flow cost control and schedule. Each element plays a significant role in the net present value (NPV) of a project. The NPV can be used as a technique to monitor the health of the project, and whether it is meeting the objectives within the time and cost identified. The failure of a project is measured by the net present value (Gardiner & Stewart, 2000).

When a project absorbs a delay to a deliverable on the critical path, five options are available (Gardiner & Stewart, 2000). The first option is to move the milestone date. The second option is to reduce the scope of the deliverable. The third option is to reduce the quality of the deliverable. The fourth option is to apply additional resources generally workforce or money. The last option is to rearrange the workload. However, another investment appraisal is not carried in most cases to assist in determining what the most appropriate action is. The point is that the logistical problems and political thinking play a role within a project and the project managers should not ignore these facts. These logistical problems and political thinkings play a role in taking a long time in abandoning a project or correcting a project (Gardiner & Stewart, 2000).

Understanding the requirement of the project play a significant role in the success of the project. Thus, the lack of understanding of the requirements of the project can lead to a different outcome, delayed project, or failed project (Forsberg, Mooz, & Cotterman, 2000). The requirement of a project begins with the customer’s needs, and not with the perception of the organization to the customer’s needs. There is an ongoing danger of misunderstanding and ambiguity in the end-to-end chain of technical, business and project development. This misunderstanding leads to non-essential, overspecified, unclear or missing requirements as illustrated in Figure 5, which is a cartoon. Such projects are subject to failure.

Figure 5. Misunderstanding Project Requirements Leads to Project Failure (Forsberg et al., 2000).

Moreover, project managers are confronted with various influencing factors including technical, organizational, and socioeconomic influences, which are relatively unique to IT projects (Pearlson & Saunders, 2001). Technical issues are related to business and budget issues. Management which does not feel comfortable with technology often take one of these actions; either ignore the IT issues or delegate them to information system organization or focus inappropriate attention on managing the technology to counter their fear. The managerial and socioeconomic influence involves the control systems used for non-project-based operations which do not efficiently support the project management. The organizational culture has an impact on the leadership style of the project management, and communication between team members. The socioeconomic impact on projects includes government and industry standards, globalization, and cultural issue.

The IT projects have a higher risk than non-IT projects (Pearlson & Saunders, 2001). The term risk is not well understood among various project management. The risk is defined as the possibility of the additional cost or loss due to the alternatives are chosen. Some alternative has a lower risk than others. Risk can be measured and quantified by assigning a probability of occurrence and a financial consequence to each alternative. Risk involves complexity, clarity, and size (Pearlson & Saunders, 2001). The more complexity of the project, the higher is the risk associated with the project. The more ambiguous the project, the higher the risk, and the bigger the size or scope of the project, the higher is the risk. There is a positive correlation between risk and these three risk elements.

The management of these risks can aid in turning the troubled projects into a successful one. (Pearlson & Saunders, 2001) argued that trouble projects persist long before they get abandoned. The amount of money invested on the trouble project biases management toward continuing to fund the project even if the success of the project is questionable. Other factors include the penalties for failure within the organization that can be high; project management is willing to go for a more extended period even if it means more resources including cost. Emotional attachment to the project can cause prolonged projects that are subject to failure.

Conclusion

This project discussed various essential topics related to projects and project management. It began with the unique characteristics of the projects and operations, among which projects are temporary while operations are repetitive. The project cycle plan and project development tools are also discussed. Various tools for project management were also discussed. These tools include project evaluation and review techniques (PERT), critical path method (CPM) and Gantt Chart. Project management involves various elements including cost, time and quality. The project also discussed project trade-off and the correlation between time and cost. Some argue that most businesses are cost-time bias at the expense of quality. Various projects success factors were also discussed in this project, such as the balance between cost, time and quality. Various factors cause projects to fail. These factors include misunderstanding of the project requirement, organizational influences, and risk management. Failed projects take a long time to be abandoned or corrected due to logistical problems, political thinking and lack of planning for uncertainty. Although the success of a project is questionable, the management persists in implementing, and it takes a long time before it gets abandoned or to put under control. Various factors contribute to this phenomenon including the penalty for failing projects, lack of understanding to risk management, and the emotional attachment to the project.

References

Anuar, N. I., & Ng, P. K. (2011). The role of time, cost and quality in project management. Paper presented at the Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on.

Atkinson, R. (1999). Project management: cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria. International journal of project management, 17(6), 337-342.

Bonner, J. M., Ruekert, R. W., & Walker Jr, O. C. (2002). Upper management control of new product development projects and project performance. Journal of Product Innovation Management: AN INTERNATIONAL PUBLICATION OF THE PRODUCT DEVELOPMENT & MANAGEMENT ASSOCIATION, 19(3), 233-245.

Bowen, P., Cattel, K., Hall, K., Edwards, P., & Pearl, R. (2012). Perceptions of time, cost and quality management on building projects. Construction Economics and Building, 2(2), 48-56.

Copertari, L. F. (2002). Time, cost and performance tradeoffs in project management.

Forsberg, K., Mooz, H., & Cotterman, H. (2000). Visualizing project management: a model for business and professional sucess: John Wiley and Sons.

Gardiner, P. D., & Stewart, K. (2000). Revisiting the golden triangle of cost, time and quality: the role of NPV in project control, success and failure. International journal of project management, 18(4), 251-256.

Hong, L. C. (2011). Predictors of project performance and the likelihood of project success.

Nidumolu, S. R. (1996). Standardization, requirements uncertainty and software project performance. Information & Management, 31(3), 135-150.

Olson, E. M., Walker Jr, O. C., Ruekerf, R. W., & Bonnerd, J. M. (2001). Patterns of cooperation during new product development among marketing, operations, and R&D: Implications for project performance. Journal of Product Innovation Management: An International Publication of the Product Development & Management Association, 18(4), 258-271.

Pearlson, K., & Saunders, C. (2001). Managing and Using Information Systems: A Strategic Approach. 2001: USA: John Wiley & Sons.

Pinto, J. K., & Slevin, D. P. (2015). 20. Critical Success Factors in Effective Project implementation*.

PMI. (2000). Project management body of knowledge (PMBOK).

Pollack-Johnson, B., & Liberatore, M. J. (2006). Incorporating quality considerations into project time/cost tradeoff analysis and decision making. IEEE Transactions on engineering management, 53(4), 534-542.

Shankar, N. R., Raju, M., Srikanth, G., & Bindu, P. H. (2014). Time, cost and quality trade-off analysis in the construction of projects.

Thamhain, H. J. (2004). Linkages of the project environment to performance: lessons for team leadership. International journal of project management, 22(7), 533-544.

Westland, J. (2018). The Triple Constraint in Project Management: Time, Scope & Cost.

Wilson, R. (2015). Mastering Project Time Management, Cost Control, and Quality Management: Proven Methods for Controlling the Three Elements that Define Project Deliverables: FT Press.

The importance of Information Control for Ethical Reasons

Dr. O. Aly
Computer Science

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 property represents 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).

Kuzu, A. (2009). Problems Related to Computer Ethics: Origins of the Problems and Suggested Solutions. Online Submission, 8(2).

Mason, R. O. (2015). Four ethical issues of the information age Computer Ethics (pp. 41-48): Routledge.

Pearlson, K., & Saunders, C. (2001). Managing and Using Information Systems: A Strategic Approach. 2001: USA: John Wiley & Sons.

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.

Significant Sourcing Topics

Dr. O. Aly
Computer Science

Abstract

The purpose of this project is to discuss significant sourcing topics such as insourcing, outsourcing, offshoring, near-shoring, and captive center. Executives and leaders are confronted with critical decisions to either insource or outsource and between make or buy. Various factors affect their decisions to go for insourcing such as design secrecy, unreliable vendors, maintaining core competencies. Other supporting elements that can aid Enterprises in their decisions to go for outsourcing instead of insourcing such as cost reduction for small projects, limited numbers of employees, the requirement for highly educated with specific skills professionals. When organizations decide to go for outsourcing another critical decision to be made is to go for offshoring or near-shoring or captive centers. Offshoring has various challenges such as cultural differences, language barriers, and distance, while near-shoring can be an appropriate option which does not have these challenges that off-shoring has. Organizations decide to go for captive centers as an alternative for offshoring by developing an overseas subsidiary. In summary, Enterprises are confronted with various sourcing options, and they must weigh the benefits and the drawbacks of each option based on the strategic goal and objectives of the business and the requirements to achieve these objectives.

Keywords: Sourcing, Insourcing, Outsourcing, Offshoring, Near-Shoring, Captive Centers

Introduction

Enterprises are confronted with various decisions challenges regarding insourcing or outsource which means to make in-house or to buy from external sources (Baldwin, Irani, & Love, 2001; Kumari & Kumar, 2013; Pearlson & Saunders, 2001). The decisions are challenges as they involve considerable complexity and business risks. Each approach has its benefits and drawbacks. Organizations must carefully examine each method and determine the best fit for their business based on its strategic goal and objectives. This project discusses the decision factors for each approach, and the discussion will expand to other related topics when organizations decide to outsource such as off-shore, near-shore and captive centers. The discussion begins with the decision cycle framework for business sources, followed by the insourcing approach decision factors.

Business Decision Cycle Framework for Sourcing

In the mid-1990s, Cognizant Technology Solutions proliferated to become a $1.4 billion revenue company providing information technology (IT) outsourcing services (Pearlson & Saunders, 2001). Outsourcing and insourcing are challenging decision-making process with which executives and leaders typically are confronted. Careful examination and analysis are required before making the decision for insourcing or outsourcing.

For Enterprises, sourcing involves many decisions evolving around the sourcing decision cycle framework (Pearlson & Saunders, 2001). The sourcing decision cycle framework begins with the decision to make or buy, followed by outsourcing or insourcing. When the decision is to outsource, then the next decision is to offshore or nearshore. The offshore decision is usually associated with cheap labor, or the required skills are more readily available. The next decision will be to go to a close country or in a country that is quite far. Organizations then are confronted with other decision to go captive center, far-shoring or near-shoring. When organizations settle on the outsourcing provider, and after a while, it is confronted with another decision, as periodically the organization must evaluate the arrangement and determine whether a modification or shift is in place. When the in-house work is not satisfactory, or other opportunities become available, the organization may decide to outsource. When outsourcing arrangement is not satisfactory, organizations have several options to consider, either to correct the existing problems, or to continue outsourcing with its current provider, or to outsource with another provider or to back-source. Figure 1 illustrates the sourcing decision cycle framework which organizations go through when making decisions to outsource or insource.

Figure 1. Sourcing Decision Cycle Framework (Pearlson & Saunders, 2001).

Insourcing Approach and Decision Factors

When organizations delegate jobs to another entity within the organization, it is defined as insourcing. The in-house entity possesses a dedicated panel who will be expert in providing the requisite services. Insourcing is also described as transferring work from one organization to another organization which is located within a similar country and not outside the country (Kumari & Kumar, 2013). Insourcing can also mean that an organization develops an innovative business center with a set of skills focusing on a particular service or product. Insourcing is defined in (Lejeck, 2016) as the return of functionality to the company, increasing the number of resources and personnel in organizations.

Insourcing is the most traditional approach where enterprises provide information system (IS) services or develop IS on-premise or in-house (Pearlson & Saunders, 2001). Factors which support insourcing, among which the most common factor is to keep the competencies inside the organization. Some argue that if an organization outsource the core competency, it can lose control over that competency or lose contact with suppliers who can assist in staying innovative about competency (Kumari & Kumar, 2013; Pearlson & Saunders, 2001). There is an increasing trend in the insourcing trend in insourcing approach to reducing the cost of labor and taxes amongst others (Kumari & Kumar, 2013).

(Hirschheim & Lacity, 2000) argued that insourcing is the assessment process of the outsourcing option with the confirmation to continue the use of the internal IT resources to achieve the same objective of outsourcing. Fourteen insourcing case studies were conducted to develop an in-depth understanding of IT insourcing decisions and outcomes. The study indicated four archetypes for insourcing. The archetype plays an essential role in conveying the fundamental differences that exist in alternative ways organization approach IT insourcing (Hirschheim & Lacity, 2000). Archetype 1 is about senior executive to enable internal IT managers to cut costs. Archetype 2 is about IT managers terminate failing outsourcing contracts. Archetype 3 is about IT managers defend insourcing. Archetype 4 is about senior executives confirm the value of IT. The authors indicated that outsourcing evaluations often result from the frustrations caused by different stakeholder expectation and perceptions of IT performance. Different perspectives of stakeholder set unrealistic performance expectations for IT managers, leading to frustration, loss of faith in internal IT management, and hopes that outsourcing vendors will provide solutions.

Insourcing Decision Factors and Challenges

There are factors which support insourcing (Kumari & Kumar, 2013; Pearlson & Saunders, 2001). The cost analysis between buying or making favors the choice of make. The process and the operations are integrated part of the system and cannot be isolated, insourcing is preferred. The use of available capacity to absorb fixed overhead is another factor supporting insourcing. The operation must be in-house for better quality and production control. Organizations prefer insourcing when the design secrecy is required; the insourcing is a better option (Kumari & Kumar, 2013; Pearlson & Saunders, 2001). When there is a lack of reliable suppliers, insourcing is a wise choice (Kumari & Kumar, 2013). When organizations have to develop long-term Core Competencies for the organization, it is better to go insourcing (Kumari & Kumar, 2013; Pearlson & Saunders, 2001). When the decision for a process or product depends on the strategic decision for the organization, insourcing is the appropriate option (Kumari & Kumar, 2013). Insourcing approach is preferred when the time is available in-house to complete software development projects, and the IT professionals are adequately trained, experienced and skilled to provide service or develop software (Pearlson & Saunders, 2001).

Insourcing has various challenges, some of which are dealing with inadequate support from top management to acquire needed resources (Pearlson & Saunders, 2001). Another insourcing challenge is finding a reliable and competent outsourcing provider who is likely to stay in business.

Advantages and Disadvantages of Insourcing

Insourcing approach has advantages and disadvantages (Pearlson & Saunders, 2001). The advantages of the insourcing approach include the high degree of control that organizations have over inputs, the increase in the visibility over the process, and the economies of scale and scope. The disadvantages of insourcing approach include the requirement for high volumes, high investment, dedicated equipment with limited use, and problems with supply chain integration (Pearlson & Saunders, 2001).

Outsourcing Approach and Decision Factors

Outsourcing is the opposite of insourcing. The outsourcing option is the process of buying goods or services that can be performed in-house or internally (Kumari & Kumar, 2013; Pearlson & Saunders, 2001). In the 1970s, some IT managers took the outsourcing approach as an essential technique to control the cost (Pearlson & Saunders, 2001). IT outsourcing involves the outside vendor providing IT services traditionally provided by the internal information system department. The motives for outsourcing have been broadened since then. The guiding principle of outsourcing approach has been the transfer of a process and function which is typically not a core competence of an enterprise to an entity that has expertise in that area, allowing the enterprise to effectively utilize its resources in its core areas of business (Kumari & Kumar, 2013). The objectives of the outsourcing include saving on the cost of operation by acquiring services from a team more productive than the internal resources, and the improvement of quality and value of operation by acquiring services from an entity with best practices in management that business activity. Since these objectives require significant investments in time and resources, over the last two years, many well-structured outsourcing deals have failed to meet expectations (Kumari & Kumar, 2013). Outsourcing approach is increasingly perceived as a strategy that can be used by businesses to leverage the skills and competencies of a definable preeminence.

In a recent report by (Deloitte, 2018), a survey result showed that outsourcing is enabling competitive advantages. The same report indicates that organizations are embracing disruptive solutions such as cloud computing and robotic process automation (RPA) 93% are considering adopting cloud solution, and 72% are considering or adopting RPA solution, and 70% believe their service providers have a reasonable or advanced ability to implement disruptive solutions. The disruptive solutions are transforming the traditional outsourcing approach, as the cloud creates capabilities but cost counts. Figure 2 shows 93% of organizations are considering adopting the cloud, and 64% shows the objective to catalyze IT innovation when adopting the cloud.

Figure 2. Cloud Adoption One of the Disruptive Solutions Transforming Traditional Outsourcing (Deloitte, 2018).

The same report also showed that organizations are addressing cyber risks when making decisions to outsource. Figure 3 shows how organizations are addressing cyber risks when making decisions to outsource.

Figure 3. Cyber Risk Consideration When Outsourcing (Deloitte, 2018).

Outsourcing Decision Factors and Challenges

Some factors support outsourcing approaches. When expert vendors are available with specialized know-how generating better output with less cost, outsourcing approach can be a better choice (Kumari & Kumar, 2013; Pearlson & Saunders, 2001). When the cost of developing the required competencies is less with outsourcing than with insourcing, outsourcing is the preferred option (Kumari & Kumar, 2013). When a business does not have the capabilities or competencies to develop items or products, the outsourcing approach is the better choice. When a small volume is required, it will not be wise to choose to outsource for cost-effectiveness. When organizations have limited numbers of employees, the outsourcing option is preferred. When the volume of a product is unknown and not certain, an organization can go with outsourcing. Organizations can use outsourcing for better strategic focus, better management of IS staff, cash infusion, data center consolidation, and smooth transition to new technologies (Pearlson & Saunders, 2001).

Outsourcing approach has various challenges. Maintaining an adequate level of control is one of the challenges that organizations have to face. Maintaining the ability to respond to technological innovation is another challenge for outsourcing approach. The avoid of a loss of strategic advantage, and overreliance on outsourcing provider are challenges for outsourcing adoption. The mitigation of the risks associated with outsourcing is another challenge. Ensuring the cost savings while protecting the quality, and work effectively with suppliers are additional challenges when adopting the outsourcing approach (Pearlson & Saunders, 2001).

Advantages and Disadvantages of Outsourcing

Outsourcing approach has advantages and disadvantages (Kumari & Kumar, 2013). Outsourcing approach has greater flexibility, lower investment risk, improved cash flow, and lower potential labor cost. Additional benefits of the outsourcing approach include the reduced costs, better service and access to new technology and enabling employees to focus their effort on higher-value work that improving output (Baldwin et al., 2001). The disadvantages of the outsourcing approach include the greater possibility of choosing wrong suppliers and distributors, loss of control over processes, the potential for losing core supportive activities, long lead-times, and hollowing out (Kumari & Kumar, 2013).

Outsourcing Types and Decision Factors

The sourcing decision cycle framework suggested by (Pearlson & Saunders, 2001) include shows that outsourcing decision includes various types such as off-shore, near-shore, or captive centers (Figure 1). This section discusses these types of outsourcing approach.

Outsourcing Offshore Approach

The offshore outsourcing approach occurs when the management information system (IS) uses services of contractors or develop a data center in a distant location (Pearlson & Saunders, 2001). The functions sent offshore range from the traditional IT transactions to high-end, knowledge-based business processes. Outsourcing offshore can save from 40% to 70% in labor. However, these labor savings come with other costs associated with additional technology, telecommunications, travel process changes, and management overhead which are required to relocate and supervise operations overseas. Organizations adopt the offshore outsourcing approach for other reasons than the cost reduction. One of these reasons is that the employees of these companies are highly educated (Kumari & Kumar, 2013; Pearlson & Saunders, 2001). The offshore outsourcing providers are often profit centers, which have established Six Sigma, ISO 9001, or another certification program (Pearlson & Saunders, 2001).

Outsourcing Near-Shoring Approach

The outsourcing near-shoring approach is a type of sourcing service work to a foreign, lower-wage country which is relatively near or closes in distance or time zone or both (Pearlson & Saunders, 2001). It was introduced as an alternative to far-shoring. Organizations can benefit from outsourcing near-shoring geographically, culturally, linguistically, economically and politically. Some argue that by being close or nearby, the organization will face fewer challenges regarding communications, control, supervision, coordination or bonding socially (Pearlson & Saunders, 2001).

Captive Centers

The concept of captive center is an alternative to outsourcing offshore and near-short approaches. The captive center is an overseas subsidiary which is set up to serve the parent company (Pearlson & Saunders, 2001). Organizations develop captive centers as an alternative to offshoring approach. In the 1990s, many companies developed captive centers to do software maintenance and customer services. The captive center approach has four important strategies; hybrid captive shared captive, divest captive, and terminated captive.

Off-Shoring, Near-Shoring, and Captive Centers Decision Factors

Although offshoring approach has the lowest cost, Enterprises should carefully examine the offshoring option due to the challenges that come along with offshoring such as cultural difference, language barriers, communication gap, time zone differences, and regulations and policies (Corredor, 2018; Pearlson & Saunders, 2001).

Near-shoring is an alternative option which also offers significant cost efficiencies. Near-shoring has more advantages over the offshoring as it has time and geographic proximity. The timezone and physical proximity of near-shoring approach provide a significant advantage over more distant locations (Corredor, 2018; Pearlson & Saunders, 2001). The culture similarity is another benefit of the outsourcing near-shoring approach. The labor pool and cost attractiveness are additional benefits of the near-shoring for decision consideration.

Summary and Conclusion

This project discussed major sourcing topics such as insourcing, outsourcing, offshoring, near-shoring, and captive center. Executives and leaders are confronted with a critical decision to either insource or outsource and between make or buy. Various factors affect their decisions to go for insourcing such as design secrecy, unreliable vendors, maintaining core competencies. Other supporting factors that can aid Enterprises in their decisions to go for outsourcing instead of insourcing such as cost reduction for small projects, limited numbers of employees, the requirement for highly educated with specific skills professionals. When organizations decide to go for outsourcing another critical decision to be made is to go for offshoring or near-shoring or captive centers. Offshoring has various challenges such as cultural differences, language barriers, and distance, while near-shoring can be an appropriate option which does not have these challenges that off-shoring has. Organizations decide to go for captive centers as an alternative for offshoring by developing an overseas subsidiary. In summary, executives are confronted with various sourcing options, and they must weigh the benefits and the drawbacks of each option based on the strategic goal and objectives of the business and the requirements to achieve these objectives.

References

Baldwin, L. P., Irani, Z., & Love, P. E. (2001). Outsourcing information systems: drawing lessons from a banking case study. European Journal of Information Systems, 10(1), 15-24.

Corredor, F. (2018). 4 Reasons Why You Should Consider Nearshoring Vs. Offshoring.

Deloitte. (2018). 2018 Global Outsourcing Survey. Retrieved from https://www2.deloitte.com/us/en/pages/operations/articles/global-outsourcing-survey.html.

Hirschheim, R., & Lacity, M. (2000). The myths and realities of information technology insourcing. Communications of the ACM, 43(2), 99-107.

Kumari, K., & Kumar, Y. V. (2013). Outsourcing vs. Insourcing: Best for Your Organization? International Journal of Management, 4(3), 08-13.

Lejeck, D. W. (2016). How to decide between insourcing and outsourcing.

Pearlson, K., & Saunders, C. (2001). Managing and Using Information Systems: A Strategic Approach. 2001: USA: John Wiley & Sons.

Business Analytics: Big Data Challenges

Dr. O. Aly
Computer Science

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 (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

Carlton, R. (2014). 5 Ways Big Data is Changing ERP Software. Retrieved from https://www.erpfocus.com/five-ways-big-data-is-changing-erp-software-2733.html.

Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.

Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., & Zhou, X. (2013). Big Data Challenge: a Data Management Perspective. Frontiers of Computer Science, 7(2), 157-164. doi:10.1007/s11704-013-3903-7

Chopra, A., & Madan, S. (2015). Big Data: A Trouble or A Real Solution? International Journal of Computer Science Issues, 12(2), 221.

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Fernández, A., Del Río, S., López, V., Bawakid, A., del Jesus, M. J., Benítez, J. M., & Herrera, F. (2014). Big Data with Cloud Computing: An Insight on the Computing Environment, MapReduce, and Programming Frameworks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(5), 380-409. doi:10.1002/widm.1134

Gantz, J., & Reinsel, D. (2011). Extracting Value From Chaos. International Data Corporation, 1142, 1-12.

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Hu, H., Wen, Y., Chua, T., & Li, X. (2014). Toward Scalable Systems for Big Data Analytics: A Technology Tutorial. Practical Innovation, Open Solution, 2, 652-687. doi:10.1109/ACCESS.2014.2332453

Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Big Data and Its Technical Challenges. Communications of the Association for Computing Machinery, 57(7), 86-94. doi:10.1145/2611567

Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013). Big Data: Issues and Challenges Moving Forward. Paper presented at the Hawaii International Conference on System Sciences

Labrinidis, A., & Jagadish, H. V. (2012). Challenges and Opportunities with Big Data. International Conference on Very Large Data Bases, 5(12), 2032-2033.

Maltby, D. (2011). Big Data Analytics. Paper presented at the Annual Meeting of the Association for Information Science and Technology.

Meeker, W., & Hong, Y. (2014). Reliability Meets Big Data: Opportunities and Challenges. Quality Engineering, 26(1), 102-116. doi:10.1080/08982112.2014.846119

Mehmood, A., Natgunanathan, I., Xiang, Y., Hua, G., & Guo, S. (2016). Protection of Big Data Privacy. Institute of Electrical and Electronic Engineers, 4, 1821-1834. doi:10.1109/ACCESS.2016.2558446

Misra, A., Sharma, A., Gulia, P., & Bana, A. (2014). Big Data: Challenges and Opportunities. International Journal of Innovative Technology and Exploring Engineering, 4(2).

Mvelase, P., Dlodlo, N., Makitla, I., Sibiya, G., & Adigun, M. (2012). An Architecture Based on SOA and Virtual Enterprise Principles: OpenNebula for Cloud Deployment, Reading.

Nasser, T., & Tariq, R. S. (2015). Big Data Challenges. Journal of Computer Engineering & Information Technology, 9307, 1-10. doi:10.4172/2324

Sagiroglu, S., & Sinanc, D. (2013). Big Data: A Review. Paper presented at the International Conference: Collaboration Technologies and Systems.

Saini, G., & Sharma, N. (2014). Triple Security of Data in Cloud Computing. International Journal of Computer Science and Information Technologies, 5(4), 5825-5827.

Woodie, A. (2016). Making ERP Better with Big Data. Retrieved from https://www.datanami.com/2016/07/08/making-erp-better-big-data/.

Zhou, Z., Chawla, N., Jin, Y., & Williams, G. (2014). Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives. Institute of Electrical and Electronic Engineers: Computational Intelligence Magazine, 9(4), 62-74.

Sarbanes-Oxley (SOX) Act of 2002: The Role of COBIT

Dr. O. Aly
Computer Science

The purpose of this discussion is to address a high-level understanding of the Sarbanes-Oxley (SOX) Act of 2002. The focus of the discussion will be on the role of IT such as COBIT in the implementation of SOX. The discussion also addresses some of the other frameworks that can be used to implement SOX, with a focus on one selected framework and rationale for such a choice. The discussion begins with the Controls Frameworks Background, followed by the Sarbanes-Oxley (SOX) Act.

Controls Frameworks Background

In 1929, Wall Street crashed (Shofner & Adams, n.d.). In 1934, the US Security and Exchange Commission (SEC) was formed, and public companies required to perform annual audits. In 1987, the Treadway Commission was formed in response to corrupt mid-1970s accounting practices, retains Coopers & Lybrand to perform a project to create an accounting control framework. In 1992, Internal Control – Integrated Framework, a four-volume report was released by the Committee of Sponsoring Organizations (COSO). A survey result shows that 82% of the respondents used COSO (Shofner & Adams, n.d.). In 1996, the Information Technology Governance Institute (ITGI) released the Control Objectives for Information and Related Technology (COBIT) Framework. In 2002, Sarbanes-Oxley (SOX) Act passed, requiring companies to adopt and declare a framework used to define and assess the internal controls (Pearlson & Saunders, 2001; Shofner & Adams, n.d.).

Sarbanes-Oxley (SOX) Act

Sarbanes-Oxley (SOX) Act is a Public Company Accounting Reform and Investor Protection Act (PCAOB) of 2002. The SOX Act affects organizations that are publicly traded in the United States (Abernathy & McMillan, 2016; Pearlson & Saunders, 2001). It controls the accounting methods and financial reporting for the organizations and stipulates penalties and even jail time for executive officers. SOX introduced new limitations on auditors including mandatory partner rotation and limits on services (Bannister, 2006). It requires new disclosure controls that inform corporate officers of material information during the reporting period. The purpose of SOX is to reduce the possibilities of corporate fraud by increasing the stringency of procedures and requirements for financial reporting (Sarbanes-Oxley, 2002). It is worth to mention two significant sections of SOX, section 302 for financial reporting and section 404 for internal control (Bannister, 2006).

Section 302 of the SOX Act directed the Security and Exchange Commission (SEC) to adopt rules to require the principal executive and financial officers of a public company to certify in their company’s annual and quarterly reports that such reports are accurate and complete and that they have established and maintained adequate internal controls for public disclosure. The purpose of this section is to ensure that the CEO and CFO take a proactive role in their company’s public disclosure and to give investors more confidence in the accuracy, quality, and reliability of the company’s SEC periodic reports (Sarbanes-Oxley, 2018a).

Section 404 of the SOX Act requires the publicly-held company’s auditor to attest to, and report on, management’s assessment of its internal control. It mandates that all publicly-traded companies must establish internal controls and procedures for financial reporting and must document, test and maintain those controls and procedures to ensure their effectiveness (Sarbanes-Oxley, 2018b).

Business Audit and Strategic Security Risk Assessment

Security Information and Event Management (SIEM or SIM/SEM) solutions play a significant role in monitoring operational security and supporting organizations in decision making (Zhu, Hill, & Trovati, 2016). SIEM provides a standardized approach to collect information and events, store and query and provide degrees of correlations, usually driven by rules. The leading SIEM solutions in the market include HP ArcSight, IBM Security QRadar, LogRhythm, and EMC Corp. However, SIEM does not cover the business audit and strategic security risk assessment but instead provide inputs that need to be adequately analyzed and translated into a suitable format to be used by senior risk assessors and strategic policymakers.

The risk assessment standards such as ISO2700x, NIST and so forth operate at a macro level and usually do not fully use the information coming from the logging and auditing activities carried bout by IT operations. Some frameworks for auditing the companies IT controls, most notably COSO and COBIT (Bannister, 2006; Zhu et al., 2016). COBiT and COSO frameworks become more critical in documenting and testing the effectiveness of the internal controls. However, COSO is not sufficient alone (Bannister, 2006; Shofner & Adams, n.d.).

Other types of detective techniques are concerned with the cloud computing services adoption rather than the security and information monitoring. Examples of these techniques include Sumo Logic, Amazon Web Services (AWS) CloudTrail, and Logentries.

COBIT IT Control Framework for Sarbanes-Oxley (SOX)

COBIT stands for Control Objectives for Information and Related Technology. It is a documented set of best IT security practices designed by the Information Systems and Control Association (ISACA) (Stewart, Chapple, & Gibson, 2015). COBIT is used to plan the IT security of an organization and also serve as a guideline for auditors. It is a security concept infrastructure used to organize the complex security solution of companies. COBIT has a series of COBIT from COBIT 1, COBIT 2, COBIT 3, COBIT 4 and 4.1, and COBIT 5. Figure 1 illustrates the history of COBIT.

Figure 1. COBIT History (Shofner & Adams, n.d.).

COBIT prescribes goals and requirements for security controls and encourages the mapping of IT security ideals to business objectives. COBIT five is based on five fundamental principles for governance and management of enterprise IT (itgovernanceusa.com, 2018; Shofner & Adams, n.d.). Principle One is about meeting the stakeholder’s needs. Principle Two is about covering the Enterprise end-to-end. Principle Three is about applying a Single, Integrated Framework. Principle Four is about Enabling a Holistic Approach. Principle Five is about Separating Governance From Management (Bannister, 2006).

Other standards and guidelines for IT security include the Open Source Security Testing Methodology Manual (OSSTMM), ISO/IEC27002 which replaced ISO17799, and the Information Technology Infrastructure Library (ITIL) (Pearlson & Saunders, 2001; Shofner & Adams, n.d.).

COBIT Advantages and Rationale

COBIT is well suited to organizations focused on risk management and mitigation, and it is detailed (Pearlson & Saunders, 2001). COBIT is chosen for Sarbanes Oxley (SOX) because it is the most widely-recognized internal control framework used to achieve IT SOX compliance (itgovernanceusa.com, 2018). It has more breadth of IT control coverage. COSO, ISO 17799 and ITIL provide medium control coverage, while COBIT provides a high level of control coverage (Bannister, 2006). Figure 2 illustrates the breadth of IT control coverage and the position of COBIT.

Figure 2. COBIT Control Coverage (Bannister, 2006).

COBIT is internationally accepted good practices, a de facto standard. It is management oriented and supported by tools and training. It is freely available and shares knowledge and leverage expert volunteers. COBIT continually evolves and maintained by reputable, not for profit organization. It maps 100% onto COSO, and strongly onto all primarily related standards.

It is used for audit planning and audit program development. It is also used to validate current IT controls and assess and reduce IT risks. It complements the COSO framework. It is used as a framework for improving IT, and to benchmark IT. It is also used as a foundation for IT Governance (Bannister, 2006).

Summary and Conclusion

This discussion addressed various essential topics related to Sarbanes-Oxley. The discussion provides a brief history of the Controls Frameworks since 1929. COSO is not recommended to be used alone, as it is not detailed enough for IT. ISO 17799 is not enough as it does not cover sound data management, third-party processes, IT delivery and support operations, audit and governance issues, software and hardware development control and segregation of duties. Organizations should consult and agree on the framework with external auditors before implementing the program. Businesses should not select too many COBIT control objectives and control practices. Simplification is highly recommended. The focus should be on Key IT Control deficiencies that are high or are a critical risk such as change management issues, access control and segregation of duties, and some data management issues like backups and storage. Organizations should include IT applications such as SAP with the business process document because most of the business controls are defined by the systems and applications (Bannister, 2006).

Organizations are recommended not to test too many applications and processes, but instead take a Risk & Business Impact Approach. Businesses should also use external Pricewaterhouse-Coopers five-step process of inventory spreadsheets, evaluation of the use and complexity, the determination of the level of controls, the evaluation of existing controls, and the development remediation. COSO and COBIT frameworks should be used as benchmarks, as they do not provide answers or specific controls, and should be tailored to meet the needs of the business. Organizations should analyze the compliance tools and software as some of them are not matured yet. Accountabilities for each business and IT process should be assigned as Segregation of Duties is business accountability but facilitated by IT (Bannister, 2006).

References

Abernathy, R., & McMillan, T. (2016). CISSP Cert Guide: Pearson IT Certification.

Bannister, G. (2006). Using COBiT for Sarbanes Oxley. Retrieved from http://itgi.jp/conf200611/garybannister.pdf.

itgovernanceusa.com. (2018). COBIT (Control Objectives for Information and Related Technology). Retrieved from https://www.itgovernanceusa.com/cobit.

Pearlson, K., & Saunders, C. (2001). Managing and Using Information Systems: A Strategic Approach. 2001: USA: John Wiley & Sons.

Sarbanes-Oxley. (2002). Sarbanes-Oxley Act Guideline. Retrieved from http://www.sarbanes-oxley.com/.

Sarbanes-Oxley. (2018a). SOX Section 302: Corporate Responsibility for Financial Reports. Retrieved from http://sarbanes-oxley-101.com/SOX-302.htm.

Sarbanes-Oxley. (2018b). SOX Section 404: Management Assessment of Internal Controls. Retrieved from http://sarbanes-oxley-101.com/SOX-404.htm.

Shofner, S., & Adams, M. (n.d.). Introduction to COSO & COBIT. Retrieved from http://www.sfisaca.org/images/FC12Presentations/C31.pdf.

Stewart, J., Chapple, M., & Gibson, D. (2015). ISC Official Study Guide. CISSP Security Professional Official Study Guide (7th ed.): Wiley.

Zhu, S. Y., Hill, R., & Trovati, M. (2016). Guide to Security Assurance for Cloud Computing: Springer.

The Impact of Cloud Computing Technology on Information Security Governance Decisions

Dr. O. Aly
Computer Science

Information security plays a significant role in the context of information technology (IT) governance. The critical decisions as part of governance for the information security needs are in the areas of information security strategy, policies, infrastructure, training, and investments for tools. Cloud computing emerging technology provides a new business model for accessing computing infrastructure on a virtualized, scalable, and lower-cost basis. The purpose of this discussion is to address the impact of cloud computing on changing decisions related to information security governance.

Cloud Computing Technology

“Cloud computing and big data are conjoined” (Hashem et al., 2015). This statement can raise the question about the reason for such a relationship. Big Data 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. The characteristics of Big Data including the explosive growth rate, challenges and issues came along (Jagadish et al., 2014; Meeker & Hong, 2014; Misra, Sharma, Gulia, & Bana, 2014; Nasser & Tariq, 2015; Zhou, Chawla, Jin, & Williams, 2014). The growth rate 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, Armour, Espinosa, & Money, 2013). Other challenges include 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 (Chen et al., 2013).

Traditional systems could not face all these challenges of BD. Cloud computing technology emerged to address these challenges of BD. Cloud computing is regarded as the solution and the answer to BD challenges and issues (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 the Big Data storage, and processing demands (Hashem et al., 2015). Besides, the increasing demand of the Big Data on networks, storage, and servers outsourcing the data to the cloud may seem to be a practical and useful option and approach when dealing with Big Data (Katal, Wazid, & Goudar, 2013). During the last two decades, this increasing demand for data storage and data security has been growing at a fast pace (Gupta, 2015). Such a demand lead to the emerging cloud computing technology (Gupta, 2015). Issues such as scalability of the Big Data has also pointed towards the cloud computing technology, which can aggregate multiple disparate workloads with varying performance goals into significant clusters in the cloud (Katal et al., 2013).

Various studies provided a different definition to cloud computing. However, the National Institute of Standards and Technology (NIST) proposed an official definition of cloud computing. NIST defined cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., network, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” (page 2) (Mell & Grance, 2011).

Cloud computing technology offers various deployment models of public cloud, private cloud, hybrid cloud, and community cloud. The public cloud is the least secure cloud model (Puthal, Sahoo, Mishra, & Swain, 2015). The private cloud has also been referred by (Armbrust et al., 2009) as internal datacenters, which are not available to the general public. Community cloud supports the specific community with particular concerns such as security requirements, policy and compliance consideration, and mission (Yang & Tate, 2012; Zissis & Lekkas, 2012). It also offers three major service models such as Infrastructure-as-a-Service (IaaS), Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) (Mell & Grance, 2011).

Cloud computing offers various benefits from technological benefits such as data and storage, APIs, metering and tools, to economic benefits such as pay per use, cost reduction and return on investment, to non-functional benefits such as elasticity, reliability, and availability (Chang, 2015). Despite these benefits, and the increasing trend in the adoption of cloud computing is still not widely used. Security concerns related to virtualization, hardware, network, data, and service providers act as significant obstacles in adopting cloud computing in IT industry (Balasubramanian & Mala, 2015; Kazim & Zhu, 2015). The security and privacy concern has been one of the major obstacle preventing the full adoption of the technology (Shahzad, 2014). (Purcell, 2014) have stated that “The advantages of cloud computing are tempered by two major concerns – security and loss of control.” The uncertainty about security has lead executives to state that security is their number one concern for deploying cloud computing (Hashizume, Rosado, Fernández-medina, & Fernandez, 2013).

Cloud Computing Governance and Data Governance

The enforcement of regulatory laws such as Health and Human Services Health Insurance Portability and Accountability Act (HIPAA) and Sarbanes-Oxley becomes an issue especially when adopting cloud computing (Ali, Khan, & Vasilakos, 2015). Cloud computing fosters security concerns that hamper the fast rate adoption of the cloud computing. Thus, cloud governance and data governance are highly recommended when adopting cloud computing.

Cloud governance is defined as the control and processes that make sure policies are enforced (Saidah & Abdelbaki, 2014). It is a framework applied to all related parties and the business process securely to ensure that the cloud supports the goal of the organization and comply with all required regulations and rules. Cloud governance model should be aligned with the corporate governance and IT governance. It has to comply with the strategy of the organization to accomplish the business goals. Various studies proposed various cloud governance models.

(Saidah & Abdelbaki, 2014) proposed a cloud governance model that provides three models; policy model, operational model, and management model. The policy model invovle data policy, service policy, business process management policy and exit policy. The operational model include authentication, authorization, audit, monitoring, adaptations, medata repository, and asset management. The management model includes policy management, security management, and service management. Figure 1 illustrates the proposed cloud governance model.

Figure 1. The Proposed Cloud Governance Model (Saidah & Abdelbaki, 2014).

(Rebollo, Mellado, & Fernández-Medina, 2013) proposed a security governance framework for cloud computing environment (ISGcloud). The proposed governance framework is founded upon two main standards. It implements the core governance principles of the ISO/IEC 38500 governance standard. The framework proposed a cloud service lifecycle based on the ISO/IEC 27036 outsourcing security draft.

When organizations decide to adopt the cloud computing technology, careful considerations must be made toward the deployment model as well as to the service model to understand the security requirements and the governance strategies (Al-Ruithe, Benkhelifa, & Hameed, 2016). Data governance for cloud computing is not nice to have but is required by rules and regulations to protect the privacy of the users and employees.

The loss of control on the data is the most significant issue when adopting cloud computing because the data is stored on a computer belonging to the cloud provider. This loss of governance and control could have a potentially severe impact on the strategy of the organization, and the capacity to meet its mission and goals (Al-Ruithe et al., 2016). The loss of control and governance of the data can lead to the impossibility of complying with security requirements, a lack of confidentiality, integrity, and availability of data, and a deterioration of performance and quality of services, not to mention the introduction of compliance challenges. Thus, organizations must be aware of the best practice for safeguarding, governing and operating data when adopting cloud computing technology. NIST offers many recommendations when adopting cloud computing technology (Al-Ruithe et al., 2016). The organization should consider data governance strategy before adopting cloud computing. This recommendation demonstrates the importance of data governance for organizations which intend to move their data and services to cloud computing environment as policies, rules, and distribution of responsibilities between cloud actors will have to be set. The development of policies and data governance will assist organizations in monitoring compliance with the current regulations and rules. The primary benefit of data governance when using cloud environment is to ensure security measures, privacy protection and quality of data.

The implementation of data governance for cloud computing changes based on the roles and responsibilities in the internal process of the organization (Al-Ruithe et al., 2016). Thus, organizations are expected to face many issues. The lack of understanding of data governance is one of the major issues. The lack of training and lack of communication plan are additional issues which organizations will face. The lack of support is another obstacle which includes lack of top management support, lack of compliance enforcement and lack of cloud regulation. Lack of policies, process and defined roles in the organization are one of the main obstacles to implement data governance in the cloud. The lack of resources including lack of funding, technology, people, and skills is considered another data governance obstacle.

Conclusion

This discussion addressed cloud computing technology and its relationship with BD and BDA. Cloud computing technology emerged as a solution to the challenges that BD and BDA faced. However, cloud computing is confronted with security and privacy challenges. Executives expressed security as the number one concern for cloud computing adoption. The governance of cloud computing will provide a secure environment to protect data from loss or malicious attacks. Organizations are required to comply with the various security and privacy regulations and rules. Organizations under pressure for data protection especially when using cloud computing technology. Thus, they are required to implement the data governance and cloud computing governance framework to ensure such compliance.

References

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Zachman Enterprise Architecture

Dr. O. Aly
Computer Science

Abstract

The purpose of this project is to discuss Zachman Enterprise Architecture which is also known as the Zachman Framework. Zachman introduced the concept of architecture in 1987 and compared the framework to the construction architecture which requires components, builders, time frame and so forth. The framework is not a methodological but a logical framework. It is a two-dimensional framework. The framework is not a security-based framework. However, it allows analysis of enterprise to be presented to different groups in the enterprise in ways that relate to the responsibilities of the groups. A few architectures expanded since the inception of Zachman’s Framework. This project discussed a few of them such as TOGAF, DoDAF, MODAF, SABSA, and CobiT. In brief, the concept of architecture did not exist until the Zachman’s initiative. The architecture concept was limited to building and construction in the Industrial Age. However, in the Information Age, Zachman was inspired to develop an information system architecture and framework for Enterprise. The application of the architecture concept to the enterprise information system was an innovative idea from Zachman that deserves recognition.

Keywords: Zachman Framework; Zachman Enterprise Architecture.

Introduction

This project discusses the enterprise framework which is developed by Zachman. As indicated in (Zachman, 1987), information system architecture was not significant thirty years ago from the time Zachman started to develop the framework in 1987. Zachman was inspired to develop such a framework and architecture for the enterprise information system. This project begins with Zachman Enterprise Architecture, followed by additional frameworks and architectures that appeared and expanded since the inception of Zachman Framework.

Zachman Enterprise Architecture or Zachman’s Framework

In 1987, John A. Zachman published a unique approach to the elements of the information system. Zachman is often mentioned in the literature as the primary contributor to enterprise architecture. Zachman (1987) presented a comparison between project design and the implementation using the classical engineering and building constructions, roads, and bridges (Zachman, 1987). The construction begins with requirements and then the structure to implement these requirements is designed. Before starting the implementation of a project that is based on stakeholders’ requirement, the design to implement such a project must be developed (Zachman, 1987). Zachman provided a comparison using a generic set of architectural representations produced during the process of constructing a building, including concepts, work breakdown structure, engineering design, manufacturing engineering, and assembly and fabrication drawings.

The framework for enterprise architecture or as it is called Zachman Framework is a logical structure for classifying and organizing the descriptive representations of the enterprise which are significant to the management and the development of the systems of the enterprise including manual and automated systems (Zachman, 1997). The generic classification structure of the design artifacts involves questions of what, how, where, who, when and why with various players planner, owner, design, builder, implementer, and operator. The artifacts involve scope, concepts, logic, physics, technology, and product. It also includes material, process, geometry, instructions, timing, and objectives. Figure 1 shows the generic classification structure of Zachman’s Design Artifacts. Figure 2 shows the populated framework.

Figure 1. Generic Classification Structure of Design Artifacts (Zachman, 1997).

Figure 2. The Populated Zachman’s Framework for Enterprise Architecture (Zachman, 1997).

Zachman’s Framework is a generic classification scheme for design artifacts, which is detailed representations of a complex object. The utility of such a scheme is to enable focused concentration on selected aspects of an object without losing a sense of the contextual or holistic perspective (Zachman, 1997). The framework is logical with five perspectives Owner, Designer, Builder, bounded by Scope or Strategist and Detail or Implementer in addition to the instantiation. Six abstractions of What for things, How for the process, Where for location, Who for responsibility, When for timing, and Why for motivation. The framework is comprehensive as it addresses the enterprise as a whole. The framework does not require technical professionals. The framework serves as a planning tool to make better choices by positioning issues in the context of the enterprise and view various alternatives. It also serves as a problem-solving tool to enable enterprise work with abstractions to simplify, isolate simple and single variables without losing the sense of the complexity of the enterprise as a whole. Zachman’s framework is described as a neutral as it is defined independently of tools or methodologies and therefore any tool or any methodology can be mapped against it. It is also described as a raw material for enterprise engineering (Zachman, 1997).

Zachman’s Framework is the basis for architecture (Zachman, 2008). During the industrial age, the industrial products were increasing in complexity and products that are changing. However, in the age of information system, the enterprise is increasing in complexity and is changing continuously. Zachman suggested that enterprise architecture is the determinant factor of survival in the Information Age (Zachman, 2008). Thus, the Framework for Enterprise Architecture which is also called the Zachman Framework has profound significance in placing definitions around Enterprise Architecture, the survival issue of the century. It is not a methodology but rather an ontology and a theory of the existence of a structured set of essential components of an object for which explicit expressions is required and probably mandatory for creating, operating and changing the object (Zachman, 2008).

Zachman’s Framework is also described as a two-dimensional classification system based on six communication questions of What, Where, When, Why, Who and How as discussed above, which intersect with different views of Planner, Owner, Designer, Builder, Subcontractor, and Actual System (Abernathy & McMillan, 2016). The system allows the analysis of an organization to be presented to different groups in the organization in ways that relate to the groups’ responsibilities. The enterprise architecture framework is not security oriented. However, it helps organizations relay information for personnel in a language and format that is most useful to them. Since the inception of Zachman’s enterprise architecture, a few architectures were developed. The next section will address some of these architectures.

Architectures Expansion

Since the inception of Zachman’s Enterprise Architecture, a few architectures have been expanded along with the technology growth. Organizations should choose the enterprise architecture framework that represents the organization in the most useful manner, based on the needs of the stakeholders. This section discusses some of these architectures that expanded since Zachman’s Framework.

The Open Group Architecture Framework (TOGAF) is another enterprise architecture framework that aids organization design, plan, implement and govern enterprise information architecture (Abernathy & McMillan, 2016). TOGAF is based on four inter-related domains: technology, applications, data, and business.

The Department of Defense Architecture Framework (DoDAF) is another architecture framework that organizes a set of products under eight views starting with “All viewpoint” (AV) for required, capability viewpoint (CV), data and information viewpoint (DIV), operation viewpoint (OV), project viewpoint (PV), services viewpoint (SvcV), standards viewpoint (STDV), and system viewpoint (SV). This framework is used to ensure that new Department of Defense (DoD) techn9ologies integrate correctly with the current infrastructures (Abernathy & McMillan, 2016).

The British Ministry of Defence Architecture Framework (MODAF) is another architecture framework that divides information into seven viewpoints starting with strategic viewpoint (StV), operational viewpoint (OV), service-oriented viewpoint (SOV), systems viewpoints (SV), acquisition viewpoint (AcV), technical viewpoint (TV), and all viewpoint (AV) (Abernathy & McMillan, 2016).

Sherwood Applied Business Security Architecture (SABSA) is an enterprise security architecture framework which is similar to Zachman’s Framework (Abernathy & McMillan, 2016). It uses six communication questions of What, Where, When, Why, Who and How which intersect with six players of Operational, Component, Physical, Logical, Conceptual and Contextual. It is described to be a risk-driven architecture (Abernathy & McMillan, 2016). Table 1 shows the SABSA Framework Matrix.

Table 1. SABSA Framework Matrix (Abernathy & McMillan, 2016).

Control Objectives for Information and Related Technology (CobiT) is a security control development framework that documents five principles. The first principle is about meeting stakeholder needs while covering the enterprise end-to-end is the second principle. The application of a single integrated framework is the third principle, following by enabling a holistic approach and separating governance from management principle. These five principles drive control objectives categorized into seven enablers starting with principles, policies, and framework, followed by processes, organizational structures. Culture, ethics, and behavior is the fourth enabler, followed by the information. Services, infrastructure, and application is another enabler, followed by the last enabler of people, skills and competencies.

Conclusion

This project discussed Zachman Enterprise Architecture which is also known as the Zachman Framework. Zachman introduced the concept of architecture in 1987 and compared the framework to the construction architecture which requires components, builders, time frame and so forth. The framework is not a methodological but a logical framework. It is a two-dimensional framework. The framework is not a security-based framework. However, it allows analysis of enterprise to be presented to different groups in the enterprise in ways that relate to the responsibilities of the groups. A few architectures expanded since the inception of Zachman’s Framework. This project discussed a few of them such as TOGAF, DoDAF, MODAF, SABSA, and CobiT. In brief, the concept of architecture did not exist until the Zachman’s initiative. The architecture concept was limited to the construction in the Industrial Age. However, in the Information Age, Zachman was inspired to develop an information system architecture and framework for Enterprise. The application of architecture concept to the enterprise information system was an innovative idea from Zachman that deserves recognition.

References

Abernathy, R., & McMillan, T. (2016). CISSP Cert Guide: Pearson IT Certification.

Zachman, J. A. (1987). A framework for information systems architecture. IBM Systems Journal

Retrieved from https://www.zachman.com/images/ZI_PIcs/ibmsj2603e.pdf, 26(3), 276.

Zachman, J. A. (1997). The Framework for Enterprise Architecture: Background, Description, and Utility by John A. Zachman. Retrieved from https://www.zachman.com/resources/ea-articles-reference/327-the-framework-for-enterprise-architecture-background-description-and-utility-by-john-a-zachman.

Zachman, J. A. (2008). The Concise Definition of The Zachman Framework by John A. Zachman. Retrieved from https://www.zachman.com/about-the-zachman-framework.