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 is now the buzzword in the field of computer science and information technology. Big Data attracted the attention of various sectors, researchers, academia, government and even the media (Géczy, 2014; Kaisler, Armour, Espinosa, & Money, 2013). In the 2011 report of the International Data Corporation (IDC), it is reporting that the amount of the information which will be created and replicated will exceed 1.8 zettabytes which are 1.8 trillion gigabytes in 2011. This amount of information is growing by a factor of 9 in just five years (Gantz & Reinsel, 2011). Big Data and Big Data Analytic are terms that have been used interchangeably (Maltby, 2011). Big Data has unique characteristics that are identified as challenging using traditional technology.
Big Data (BD) has been characterized by what is often referred to as a multi-V model such as variety, velocity, volume, veracity, and value (Assunção, Calheiros, Bianchi, Netto, & Buyya, 2015). While variety represents the data types, the velocity reflects the rate at which the data is produced and processed (Assunção et al., 2015). The volume defines the amount of data, and the veracity reflects how much the data can be trusted given the reliability of its source. The value, on the other hand, represents the monetary worth which organizations can derive from adopting Big Data computing. Figure 1 summarizes these characteristics.
Big Data (BD) has been characterized by what is often referred to as a multi-V model such as variety, velocity, volume, veracity, and value (Assunção, Calheiros, Bianchi, Netto, & Buyya, 2015). While variety represents the data types, the velocity reflects the rate at which the data is produced and processed (Assunção et al., 2015). The volume defines the amount of data, and the veracity reflects how much the data can be trusted given the reliability of its source. The value, on the other hand, represents the monetary worth which organizations can derive from adopting Big Data computing. Figure 1 summarizes these characteristics.
Figure 1. Big Data Multi-V Model (Assunção et al., 2015).
The variety characteristic of the Big Data reflects the data types (Assunção et al., 2015). The data types are further categorized into the structure, unstructured, semi-structured and mixed. The structured data represents the formal schema and data models, while the unstructured reflects no pre-defined data model, and semi-structured lacked strict data model structure and mixed as the term indicates that various types together (Assunção et al., 2015). Figure 2 summarizes these data types in the Big Data.
Figure 2. Variety Characteristic of Big Data (Assunção et al., 2015).
The velocity characteristics of the Big Data represents the speed or arrival and the processing of the data which have been characterized into the batch, near-time, real-time, and streams according to (Assunção et al., 2015). The batch reflects the at time intervals, while near-time refers to at small time intervals. The real-time, on the other hand, represents the continuous input, process, and output, while the streams refer to data flows (Assunção et al., 2015). Figure 3 summarizes these characteristics of the velocity feature of the Big Data.
Figure 3. Velocity Characteristic of Big Data (Assunção et al., 2015).
Big Data Challenges
With these characteristics of Big Data, including the growth rate, challenges and issues have come along (Jagadish et al., 2014; Meeker & Hong, 2014; Misra, Sharma, Gulia, & Bana, 2014; Nasser & Tariq, 2015; Zhou, Chawla, Jin, & Williams, 2014). The growth rate in the amount of data is regarded to be a significant challenge for IT researchers and practitioners to design appropriate systems that handle the data effectively and analyze it to extract relevant meaning for decision-making (Kaisler et al., 2013). Various challenges and issues of the Big Data have been discussed and analyzed in multiple research studies, such as data storage, data management, and data processing (Fernández et al., 2014; Kaisler et al., 2013); Big Data variety, Big Data integration and cleaning, Big Data reduction, Big Data query and indexing, and Bid Data analysis and mining (J. Chen et al., 2013).
Extracting a meaningful value from the Big Data is a significant challenge (Fernández et al., 2014; Sagiroglu & Sinanc, 2013). Three factors must be taken into consideration to create value from Big Data (Chopra & Madan, 2015). These three factors include the user control over the data, the security issues to be taken seriously, and the examination of safety points on a yearly basis. (Chopra & Madan, 2015) suggested that businesses and organizations, which follow those factors, will distinguish themselves by gaining market initiatives. Other research studies such as (Labrinidis & Jagadish, 2012) suggested that the value obtained from the analysis of the data is broadly recognized, but the analysis of the data is regarded to be challenging due to the challenging characteristics of the Big Data. Other research studies such as (Assunção et al., 2015; Chopra & Madan, 2015) have indicated that the complexity of Big Data is preventing organization to realize its benefit and causing a business to step back from the Big Data deployment and implementation.
Big Data Analytics and Cloud Computing Solution
The challenges of BD and BDA such as data storage, data management, data processing, and data-intensive computational requirements required solutions as the traditional technology was found inadequate (Fernández et al., 2014; Hu, Wen, Chua, & Li, 2014). As indicated above, one of the significant challenges is extracting a meaningful value from BD. BD and BDA require advanced and unique data storage, management, analysis, intensive computing, and visualization technologies (H. Chen, Chiang, & Storey, 2012; J. Chen et al., 2013). Cloud computing emerging technology has been meeting these requirements and serving as a solution and platform to BD and BDA challenges.
Cloud computing plays a significant role in Big Data Analytics (Assunção et al., 2015). The massive computation and storage requirement of the BD and BDA brings the critical need for cloud computing (Mehmood, Natgunanathan, Xiang, Hua, & Guo, 2016). Cloud computing is currently the biggest buzz in the information technology, computer science industry, in the computer world, and the distributed computing community (Dhanani, 2014; Saini & Sharma, 2014). It is being positioned as the “next wave of computing” (Mvelase, Dlodlo, Makitla, Sibiya, & Adigun, 2012, p. 214). The use of cloud computing technology in conjunction with data has been the more recent trend for BDA (Wang, Kung, & Byrd, 2018). Organizations have increasingly adopted BD and BDA in the cloud, particularly, the Software-as-a-Service (SaaS) cloud service model, which offers an attractive alternative with lower cost (Wang et al., 2018). Cloud computing technology for BDA systems supporting a real-time analytic capability and cost-effective storage is becoming a preferred information technology solution (Wang et al., 2018). The cloud computing technology is the solution and the answer to the challenges of BD and BDA (Fernández et al., 2014). Organizations and businesses are under pressure to quickly adopt and implement technologies such as cloud computing to address the challenges of Big Data (Hashem et al., 2015).
Big Data Analytics Role in ERP
Big Data Analytics plays a significant role in ERP applications (Carlton, 2014; ERP Solutions, 2018; Woodie, 2016). Enterprise data comprises various departments such as HR, finance, CRM and other essential business functions of a business. This data can be leveraged to make ERP functionality better. When Big Data tools are brought together with the ERP system, it can unfold valuable insights that can businesses make smarter decisions (Carlton, 2014; Cornell University, 2017; Wailgum, 2018). Many ERP systems fail to make use of real-time inventory and supply chains data because these systems lack the intelligence to make predictions about products demands (Carlton, 2014; ERP Solutions, 2018). Big Data tools can predict demand and help determine the needs of the organization to go forward (ERP Solutions, 2018). Infor co-president Duncan Angove established Dynamic Science Labs (DSL) aiming to use data science techniques to solve particular business problems for its customers. Employees with big data, math, and coding skills were hired in Cambridge, Massachusetts-based organization to develop proof of concept (POC) (Woodie, 2016). Big Data systems such as Apache’s Hadoop are creating node-level operating transparencies which affect nearly every current ERP module in real-time (Carlton, 2014). Managers will be able to quickly leverage ERP Big Data capabilities, thereby enhancing information density and speeding up overall decision-making. In brief, Big Data and Big Data Analytics impact business at all levels, and ERP is no exception.
Conclusion
Big Data (BD) and Big Data Analytics (BDA) have been the buzzwords across various industries from academic, research, practitioners, media and government. BD has been characterized by certain features such as volume, variety, and velocity which were the first V-model of BD. The traditional technology and systems were found inadequate to deal with and handle BD. The explosive growth of the data in various forms such as structured, unstructured and semi-structured, and the speed of the growth and the required speed for processing the data demanded technologies that can deal with these unique characteristics. Cloud computing emerging technology was found to provide a solution when applying BD and BDA for storage and computation. Other technologies include Hadoop, MapReduce, Spark and so forth. BD and BDA play a crucial role in Enterprise Resource Planning (ERP). Organizations are under pressure to take advantage of BD and BDA to become competitive and stay competitive in the age of the digital world and the era of Big Data and Big Data Analytics.
References
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