Leveraging Big Data to Better Business Intelligence: A Literature Review Paper

Abstract

In this digitised age, vast amounts of information are readily available to be autonomously rationalised/analysed to gain valuable knowledge and insights to support decision makers. This information referred to as ‘Big Data’ is quantified from large datasets, hence the name. It is also derived from the information being high in variety and velocity. Because of this data’s characteristics, they are beyond the ability of commonly used software tools and storage systems to capture, store, manage, as well as process the data within a tolerable elapsed time. Due to the progression of this Big Data, innovative solutions are needed to be implemented to uncover valuable insights from these datasets. Moreover, decision makers must be able to retain valuable insights from this erratic data. Such value can be provided using big data analytics. This paper aims to review the literature covered on Big Data analytics considering; what it is, its characteristics and its implication in decision making.

Introduction

“Big Data” is a term used to specify information. Information that due to its characteristics is unable to be processed by conventional database methods and tools[1] [2]. They are data sets whose size is beyond the ability of commonly used software tools and storage systems to capture, store, manage, as well as process the data within a tolerable elapsed time [4]. [12] Today, enterprises are exploring large volumes of highly detailed data to better Business Intelligence[5]. Business intelligence (BI) is the ability of a company to make meaningful use of data it collects during its day-to-day business operations (Kimble & Milolidakis, 2015).[6]

The BI could play an important role in improving organizational performance by identifying new opportunities, highlighting potential threats, revealing new business insights and enhancing decision making processes among many other benefits [7] In addition, ‘big data’ has the capability of transforming the decision making process by allowing enhanced visibility of firm operations and improved performance measurement mechanisms [10] (Literature bookmark 2) Literature paper states that McKinsey and Company found that “collecting, storing, and mining big data for insights can create significant value for the world economy, enhancing productivity and competitiveness of companies and the public sector and creating a substantial economic surplus for consumers” [9] It is these insights that retailers can achieve up to 15–20% increase in ROI (Return on Investment) by bettering insight from analytics [8]

With any innovation, big data presents multiple challenges to adopting firms. For example [14], notes that enterprises will face challenges in processing speed, data interpretation, data quality, visualization, and exception handling of big data. I highlight 4 technical and managerial challenges: data quality, data security, privacy and investment justification.

Currently, BI solutions mainly focus on structured and internal data of enterprise. As a result, a lot of valuable information embedded in unstructured and external data remains hidden, which could potentially lead to incomplete view of the reality and resultantly biased business decision making. However, there are great advantages in using BI with the advent of computing and internet technologies facilitatating the collection of a large volume of heterogeneous data from multiple sources on an ongoing basis thus posing new challenges and opportunities for business intelligence.

The subsequent report, aims to discuss data security, privacy and investment justification in order to investigate how this may be beneficial for businesses to thrive. The literature procured was selected based on its adherence to the above topics. BI research is still a relatively novel subject with research only beginning to be conducted around 2008. However, this report aims to use research literature from the years 2011-2013 due to the crux of research being conducted during this time.  Leading corporations in the industry have investigated BI and discussed their findings in the form of journals, conferences and white papers. This report aims to discuss these resources by highlighting the information gathered and critiquing where appropriate. The business market is both rapidly changing and growing, and the adoption of BI can lead to great financial gain, it is therefore of great importance that BI literature is investigated and understood.

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[1] S. Kaisler, J. A. Espinosa, F. Armour, W. Money, Advanced Analytics for Big Data Encyclopedia of Information Science and Technology, IGI-Global. (Literature bookmark 5)

[2] 3. J. Manyika, M. Chui, B. Brown, et al, McKinsey Global Institute(2011) (Literature bookmark 5)

[3] 6. M. Schroeck, R. Shockley, J. Smart, D. Romero-Morales, P. Tufano, IBM(2012) (Literature bookmark 5)

[4] Kubick, W.R.: Big Data, Information and Meaning. In: Clinical Trial Insights, pp. 26–28 (2012)  (Literature bookmark 2)

[5] Russom, P.: Big Data Analytics. In: TDWI Best Practices Report, pp. 1–40 (2011) (Literature bookmark 2)

[6] C. Kimble, Milolidakis G. Big Data and Business Intelligence: Debunking the Myths Global Business and Organizational Excellence, 35 (2015), pp. 23-34 (Literature bookmark 2)

[7] (Xia & Gong, 2014; Kowalczyk & Buxmann, 2014). (Literature bookmark 2)

[8] (Perrey et al., 2013). (Literature bookmark 2)

[9] (p. 1) (Manyika et al., 2011). (Literature bookmark 2)

[10] (McAfee and Brynjolfsson, 2012). (Literature bookmark 2)

[11] (Sharma et al., 2014).(Literature bookmark 2)

[12] (Sharma et al., 2014). (Literature bookmark 2)

[13] (Marín-Ortega et al., 2014).

[14] SAS (2013) (literature review 10)

Big Data

Despite the term “Big Data” being firmly grounded in the lexicon of technological, industrial, business and academic literature [2]. They do not share a unified understanding of what it is and how it is applied [1]. For instance, it was stated by [3] “data quality that is consistently meeting knowledge worker and end-customer expectations” but also as “data that fit for their intended uses in operations, decision making, and planning” [4].

Mayer-Schönberger and Cukier (2013)[5] suggests big data is based on “predictions, connections, and relationships amongst vast data sets. Beyond bigger, better, networked information, big data is chiefly defined by its novel applications”. This is supported by [7] stating that android big data is utilised to determine highway traffic alerts and supply chain management for corporate chains. This is further backed up by [6] which suggests sentiment tracking of the social media platform Twitter is used to formulate predictions on stock market indicators.

Although “Big Data“ is densely referenced in business literature,  it still remains in infancy as a concept, it remains an ambiguous and uncertain definition amongst academic journals. However, a definition that resonates amongst the mass of literature studied, refers to “Big Data” as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyse” [21] within a tolerable elapsed time [12].

Due to the current evolution of the IoT (internet of things)  and dependency on technology, big data has seen a surge in its volume. Social media platforms such as Facebook, Twitter and Youtube have contributed to almost 93% of all big data revenue. (Sharma et al., 2014).(Literature bookmark 2). Enterprises are showing a 200% growth per year in this revenue stream and this is only set to grow exponentially. With predictions of an 800% increase within the next five years, 90% of which is accounted for as being totally unstructured.(Literature Bookmark 18). This unstructured data that isn’t being processed means that insights are biased due to a lack of a holistic view of the information provided, this can result in an inefficient or detrimental business strategy. [17] also supports the idea that even when the volume of data is at efficient level of insight extraction, that there is still the human error of miss reading data, which would further cripple business intelligence.

However this unstructured data is not lost, big data analytics is able to structure this data and then exploit its algorithmic methods to retain pertinent information that leverages positive business change.

The following section will be highlighting, big data, the characteristics of big data, business intelligence, how big data is optimising business intelligence, customer intelligence,

In this section, we will start by discussing the characteristics of big data, as well as its importance. Naturally, business benefit can commonly be derived from analyzinglarger and more complex data sets that require real time or near-real time capabilities; however, this leads to a need for new data architectures, analytical methods, and tools. Therefore the successive section will elaborate the big data analytics tools and methods, in particular, starting with the big data storage and management, then moving on to the big data analytic processing. It then concludes with some of the various big data analyses which have grown in usage with big data.

(From other lit review)

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[1] . P.M. Hartmann, M. Zaki, N. Feldmann, A. Neely, Cambridge Service Alliance, March 27(2014) (Literature bookmark 5)

[2] M. Chen, S. Mao, Y. Liu, “Big Data: A Survey”, Mobile Networks and Applications, vol. 19, no. 2, pp. 171-209, 2014.  (Literature bookmark 1)

[3] L. P. English, Improving Data Warehouse and Business Information Quality, New York, USA:Wiley Computer Publishing, 1999. (Literature bookmark 1)

[4] R. Y. Wang, D. M. Strong, L. M. Guarascio, “Data Consumers’ Perspective on Data Quality”, Beyond Accuracy: What Data Quality Means to Data Consumers TDQM-94-01 MIT, 1994. (Literature bookmark 1)

[5] Mayer-Schönberger and Cukier (2013) (Literature bookmark 4)

[6] (Bollen, Mao, & Zeng 2011). (Literature bookmark 4)

[7] (Demirkan & Delen, 2013). (Literature bookmark 4)

[8] (Laney, 2001). Laney, D., 2001. 3D Data management: controlling data volume, velocity, and variety. (available at http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf) (accessed 10.10.15).(Literature bookmark 4) (FROM GARTNER)

[9] Russom P. Big data analytics. (2011). (Literature bookmark 2)

[10] EMC: Data Science and Big Data Analytics. In: EMC Education Services, pp. 1–508 (2012) (Literature bookmark 3)

[11] M.E. Prescott Big data and competitive advantage at Nielsen Management Decision., 52 (2014), pp. 573-601(Literature bookmark 2)

[12](Schomm, Stahl & Vossen, 2013; Kambatla et al., 2014).F. Schomm, F. Stahl, G. Vossen Marketplaces for data: an initial surveySIGMOD Record., 42 (2013), pp. 15-26 (Literature bookmark 2)

[13] 5. P.M. Hartmann, M. Zaki, N. Feldmann, A. Neely, Cambridge Service Alliance, March 27(2014)

[14] (Wamba et al..,2015). S.F. Wamba, S. Akter, A. Edwards, G. Chopin, D. GnanzouHow ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case studyInternational Journal of Production Economics., 165 (2015), pp. 234-246 (Literature bookmark 2)

[15] M. Schroeck, R. Shockley, J. Smart, D. Romero-Morales, P. Tufano, IBM(2012) (Literature bookmark 5)

[16]. G.J. Li, X.Q. Cheng, Bulletin of Chinese Academy of Sciences, 27(2012)[In Chinese] (Literature bookmark 5)

[17]  Z.B. Xu, Z.Y. Feng, X.H. Guo et al, Management world, 11,158-163 (2014)[In Chinese] (Literature bookmark 5)

[18]. H. Chen, R.H.L. Chiang, V.C. Store, MIS Quarterly, 36,1165-1188(2012) (Literature bookmark 5)

[19] R.M. Chang, R.J. Kauffman, Y.O. Kwon, DSS, 63,67-80( 2014) 4. P.B. Goes, MIS Quarterly, 38(2014) (Literature bookmark 5)

[20]. P.B. Goes, MIS Quarterly, 38(2014) (Literature bookmark 5)

[21] (Manyika et al., 2011, p. 1)[

[22] (Davenport, 2014). (Literature bookmark 10) T.H. Davenport Big data at work: Dispelling the myths, uncovering the opportunities Harvard Business Review Press, Boston (2014)

Characteristics of Big Data

In the literature reviewed; “Big data is typically characterised by three important attributes, namely volume, variety and velocity” and is referred to as the three V’s.[8]

The three V’s are defined as high-volume, high-variety and high-velocity [9].

High-volume is the primary feature of big data. It describes the high density of a data subject. In terms of data volume, for example, Nielsen can generate around 300,000 rows of real-time data per second from live viewing and yield more than one billion records per month to do big data analysis [11]

High-velocity as implied by the name is suggestive of the movement of data. It is defined in the literature as rate at which big data is being created, or the rate at which data is changing. [9]

High-variety is defined as the format and type of data. The formats varying from qualitative to quantitative, audio to visual, social media and clickstream data. Whilst types of data being described as prescriptive, descriptive and predictive.

Due to the infancy of big data, it is constantly evolving. The literature from 2016 is now presenting diagnostic as a new type of data that assesses

are used for discovery or to determine why something happened. For example, for a social media marketing campaign, you can use descriptive analytics to assess the number of posts, mentions, followers, fans, page views, reviews, pins, etc. There can be thousands of online mentions that can be distilled into a single view to see what worked in your past campaigns and what didn’t.

Finally, variety includes the different formats and types of data, as well as the different kinds of uses and ways of analysing the data [10]. including logs, clickstreams, and social media.

There’s also data, which is hard to categorize since it comes from audio, video, and other devices. Furthermore, multi-dimensional data can be drawn from a data warehouse to add historic context to big data. Thus, with big data, variety is just as big as volume. In terms of data variety, big data analytics of, both, structured and unstructured data can help companies generate insights from various sources, including consumer transactions, inventory monitoring, store-based video, advertisement and consumer relations, consumer preferences, sales management and financial data [12] While variety refers to its various forms, it can be classified into structural data, unstructured data and semi-structured data [13]

However, the study of “big data” has evolved and expanded a lot based on its application and implementation processes in specific industries to create value (Value-adding – V5) – “Big Data cloud computing perspective/Internet of Things (IoT)”. Hence, the four Vs of “big data” is now expanded into five Vs.

Moreover, big data can be described by its velocity or speed. This is basically the frequency of data generation or the frequency of data delivery. The leading edge of big data is streaming data, which is collected in real-time from the websites  [17]. For data velocity, big data analytics can enable real-time access and information sharing through local to national governments for improved decision making [14]

Some researchers and organizations have discussed the addition of a fourth V, or veracity. Veracity focuses on the quality of the data. This characterizes big data quali- ty as good, bad, or undefined due to data inconsistency, incompleteness, ambiguity, latency, deception, and approximations [22]. There are a few researchers to enrich the “3v” features to “4v” after Gartner’s definition. (Literature Bookmark 5) states that Schroeck et al [15] added veracity into above three characteristics to emphasize on the uncertainty of certain types data such as weather, economy, the consuming intention of consumers and so on, while Li et al (Literature Bookmark 5)[16] added value into “3v” in order to explain the feature of huge value but low-density.

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[1] . P.M. Hartmann, M. Zaki, N. Feldmann, A. Neely, Cambridge Service Alliance, March 27(2014) (Literature bookmark 5)

[2] M. Chen, S. Mao, Y. Liu, “Big Data: A Survey”, Mobile Networks and Applications, vol. 19, no. 2, pp. 171-209, 2014.  (Literature bookmark 1)

[3] L. P. English, Improving Data Warehouse and Business Information Quality, New York, USA:Wiley Computer Publishing, 1999. (Literature bookmark 1)

[4] R. Y. Wang, D. M. Strong, L. M. Guarascio, “Data Consumers’ Perspective on Data Quality”, Beyond Accuracy: What Data Quality Means to Data Consumers TDQM-94-01 MIT, 1994. (Literature bookmark 1)

[5] Mayer-Schönberger and Cukier (2013) (Literature bookmark 4)

[6] (Bollen, Mao, & Zeng 2011). (Literature bookmark 4)

[7] (Demirkan & Delen, 2013). (Literature bookmark 4)

[8] (Laney, 2001). Laney, D., 2001. 3D Data management: controlling data volume, velocity, and variety. (available at http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf) (accessed 10.10.15).(Literature bookmark 4) (FROM GARTNER)

[9] Russom P. Big data analytics. (2011). (Literature bookmark 2)

[10] EMC: Data Science and Big Data Analytics. In: EMC Education Services, pp. 1–508 (2012) (Literature bookmark 3)

[11] M.E. Prescott Big data and competitive advantage at Nielsen Management Decision., 52 (2014), pp. 573-601(Literature bookmark 2)

[12](Schomm, Stahl & Vossen, 2013; Kambatla et al., 2014).F. Schomm, F. Stahl, G. Vossen Marketplaces for data: an initial survey SIGMOD Record., 42 (2013), pp. 15-26 (Literature bookmark 2)

[13] 5. P.M. Hartmann, M. Zaki, N. Feldmann, A. Neely, Cambridge Service Alliance, March 27(2014)

[14] (Wamba et al..,2015). S.F. Wamba, S. Akter, A. Edwards, G. Chopin, D. Gnanzou How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study International Journal of Production Economics., 165 (2015), pp. 234-246 (Literature bookmark 2)

[15] M. Schroeck, R. Shockley, J. Smart, D. Romero-Morales, P. Tufano, IBM(2012) (Literature bookmark 5)

[16]. G.J. Li, X.Q. Cheng, Bulletin of Chinese Academy of Sciences, 27(2012)[In Chinese] (Literature bookmark 5)

[17]  Z.B. Xu, Z.Y. Feng, X.H. Guo et al, Management world, 11,158-163 (2014)[In Chinese] (Literature bookmark 5)

[18]. H. Chen, R.H.L. Chiang, V.C. Store, MIS Quarterly, 36,1165-1188(2012) (Literature bookmark 5)

[19] R.M. Chang, R.J. Kauffman, Y.O. Kwon, DSS, 63,67-80( 2014) 4. P.B. Goes, MIS Quarterly, 38(2014) (Literature bookmark 5)

[20]. P.B. Goes, MIS Quarterly, 38(2014) (Literature bookmark 5)

[21] (Manyika et al., 2011, p. 1)[

[22] (Davenport, 2014). (Literature bookmark 10) T.H. Davenport Big data at work: Dispelling the myths, uncovering the opportunities Harvard Business Review Press, Boston (2014)

Business Intelligence

Business intelligence (BI) is the ability of a company to make meaningful use of available data[1]. BI is typically used as an ‘umbrella’ term to describe a process[2] , or concepts and methods [3], that improve decision making by using fact-based support systems (Big data) Business intelligence includes a range of areas such as competitor intelligence, customer intelligence, market intelligence, product intelligence, strategic intelligence, technological intelligence and business counterintelligence[1]. Xia and Gong (2014)[4] cited a survey conducted by Thomson in 2004 suggesting that the major benefits of BI are generating faster and more accurate reporting, improved business decision making, improved customer service and increasing company revenue. These formal information-based routines and procedures are used by managers for improving or altering the course of a firm’s operations, otherwise known as management control systems [5].

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[1] (Kimble & Milolidakis, 2015).

[2] A. Shollo, K. Kautz ‘Towards an Understanding of Business Intelligence, In the Proceedings of the 21st Australian Conference in Information Systems (ACIS), Brisbane (2010) (Literature bookmark 12)

[3] R. Sabherwal, I. Becerra-Fernandez Business Intelligence: Practices, Technologies, and Management John Wiley & Sons, NJ (2011)

[4] Xia and Gong (2014)

[5] Simons, R. 1995. Levers of Control: How Managers Use Innovative Control Systems to Drive Strategic Renewal. Boston, MA: Harvard Business School Press. (Literature bookmark 13)

4.1  Overview of big data in improving business intelligence

An umbrella term used to define

Businesses have historically turned to large amounts of data and analytics to inform strategy and decision making, so why is big data making such an impact right now, and what are some of the top benefits? Firstly, data itself has become easier to collect, through web clicks, RFID tags, sensors, loyalty cards and barcodes. Secondly, data has become cheaper to store and analyze through some of the big data tools outlined above, and the volume capacity and performance quality of these tools is steadily increasing. This means more businesses than ever have the opportunity to tap into new insights.

At a foundational level, one of the most powerful benefits of big data is simply the greater availability, visibility, and transparency of information. As businesses collect and analyze both structured and unstructured data, decision-makers can gain a clearer picture of employee performance, product supply chain, service quality, customer satisfaction, and the competitive landscape (Fanning and Grant, 2013; Milliken, 2014 ;  Tirunillai and Tellis, 2014). When the inner workings of an organization are brought to light, there is the potential for problem-solving and optimization. Data analysts and data-mining techniques can find new patterns and connections at a more granular level than was previously possible by virtue of the fact that this type of data is collected and made available for analysis.

Predictive and prescriptive analytics play a vital role in helping companies make effective decisions on the strategic direction of the organization (Demirkan and Delen, 2013). They can be applied to address problems related to the changes in organizational culture, sourcing decisions, supply chain configuration, and design and development of products or services. Descriptive analytics answer questions related to “what happened and/or what is happening”. These decisions may also involve performance analysis by employing models, techniques, and tools to help companies make quick, efficient, and effective decisions.

Another one of the most resounding benefits we found across the population, is big data’s unprecedented potential to help businesses measure and manage predictively. This is made possible by sophisticated, real-time analytics that can increase the speed and accuracy of decision-making. For instance, banks can manage delinquency levels more effectively, auto manufacturers can forecast which parts of vehicles may be most prone to break down and offer preventative maintenance, and retailers can predict which customers will stop shopping and offer them incentives to return ( Melo, 2014 ;  Shu, 2014).

Big data also enables businesses to market products and services in a new way. Marketers can map consumer purchase paths through social media monitoring, campaign-tracking data, clickstream analysis, and product review forums, to ascertain which methods are the most effective (Spenner & Freeman, 2012). Crowdsourcing through big data can spark improved product development or identify customer needs to create new businesses. This type of data allows firms to better understand customer heterogeneity, or personal preference, and to spend marketing budgets in more targeted ways that increase return on investment

4.2 Big Data Analytics

Nowadays, people don’t just want to collect data, they want to understand the mean- ing and importance of the data, and use it to aid them in making decisions. Data ana- lytics is the process of applying algorithms in order to analyze sets of data and extract useful and unknown patterns, relationships, and information [1]. Furthermore, data analytics are used to extract previously unknown, useful, valid, and hidden patterns and information from large data sets, as well as to detect important relationships among the  stored  variables.  Therefore,  analytics  have had  a  significant  impact on research and technologies, since decision makers have become more and more inter- ested in learning from previous data, thus gaining competitive advantage [21].

Along with some of the most common advanced data analytics methods, such as association rules, clustering, classification and decision trees, and regression some additional analyses have become common with big data.

For example, social media has recently become important for social networking and content sharing. Yet, the content that is generated from social media websites is enormous and remains largely unexploited. However, social media analytics can be used to analyze such data and extract useful information and predictions [2]. Social media analytics is based on developing and evaluating informatics frameworks and tools in order to collect, monitor, summarize, analyze, as well as visualize social me- dia data. Furthermore, social media analytics facilitates understanding the reactions and conversations between people in online communities, as well as extracting useful patterns and intelligence from their interactions, in addition to what they share on social media websites [24].

On the other hand, Social Network Analysis (SNA) focuses on the relationships among social entities, as well as the patterns and implications of such relationships [23]. An SNA maps and measures both formal and informal relationships in order to comprehend what facilitates the flow of knowledge between interacting parties, such as who knows who, and who shares what knowledge or information with who and using what [19].

However, SNA differs from social media analysis, in that SNA tries to capture the social relationships and patterns between networks of people. On the other hand, so- cial media analysis aims to analyze what social media users are saying in order to uncover useful patterns, information about the users, and sentiments. This is tradition- ally done using text mining or sentiment analysis, which are discussed below.

On the other hand, text mining is used to analyze a document or set of documents in order to understand the content within and the meaning of the information contained. Text mining has become very important nowadays since most of the in- formation stored, not including audio, video, and images, consists of text. While data mining deals with structured data, text presents special characteristics which basically follow a non-relational form [18].

Moreover, sentiment analysis, or opinion mining, is becoming more and more im- portant as online opinion data, such as blogs, product reviews, forums, and social data from social media sites like Twitter and Facebook, grow tremendously. Sentiment analysis focuses on analyzing and understanding emotions from subjective text pat- terns, and is enabled through text mining. It identifies opinions and attitudes of indi- viduals towards certain topics, and is useful in classifying viewpoints as positive or negative. Sentiment analysis uses natural language processing and text analytics in order to identify and extract information by finding words that are indicative of a sentiment, as well as relationships between words, so that sentiments can be accurate- ly identified [15].

Finally, from the strongest potential growths among big data analytics options is Advanced Data Visualization (ADV) and visual discovery [17]. Presenting informa- tion so that people can consume it effectively is a key challenge that needs to be met, in order for decision makers to be able to properly analyze data in a way to lead to concrete actions [14].

Big Data Analytics and Decision Making

From the decision maker’s perspective, the significance of big data lies in its ability to provide information and knowledge of value, upon which to base decisions. The managerial decision making process has been an important and thoroughly covered topic in research throughout the years.

Big data is becoming an increasingly important asset for decision makers. Large volumes of highly detailed data from various sources such as scanners, mobile phones, loyalty cards, the web, and social media platforms provide the opportunity to deliver significant benefits to organizations. This is possible only if the data is properly analyzed to reveal valuable insights, allowing for decision makers to capitalize upon the resulting opportunities from the wealth of historic and real-time data generated through supply chains, production processes, customer behaviors, etc. [4].

Moreover, organizations are currently accustomed to analyzing internal data, such as sales, shipments, and inventory. However, the need for analyzing external data, such as customer markets and supply chains, has arisen, and the use of big data can provide cumulative value and knowledge. With the increasing sizes and types of un- structured data on hand, it becomes necessary to make more informed decisions based on drawing meaningful inferences from the data [7].

Accordingly, [8] developed the B-DAD framework which maps big data tools and techniques, into the decision making process [8]. Such a framework is intended to enhance the quality of the decision making process in regards to dealing with big data. The first phase of the decision making process is the intelligence phase, where data which can be used to identify problems and opportunities is collected from internal and external data sources. In this phase, the sources of big data need to be identified, and the data needs to be gathered from different sources, processed, stored, and mi- grated to the end user. Such big data needs to be treated accordingly, so after the data sources and types of data required for the analysis are defined, the chosen data is ac- quired and stored in any of the big data storage and management tools previously discussed After the big data is acquired and stored, it is then organized, prepared, and processed, This is achieved across a high-speed network using ETL/ELT or big data processing tools, which have been covered in the previous sections.

The next phase in the decision making process is the design phase, where possible courses of action are developed and analyzed through a conceptualization, or a repre- sentative model of the problem. The framework divides this phase into three steps, model planning, data analytics, and analyzing. Here, a model for data analytics, such as those previously discussed, is selected and planned, and then applied, and finally analyzed.

Consequently, the following phase in the decision making process is the choice phase, where methods are used to evaluate the impacts of the proposed solutions, or courses of action, from the design phase. Finally, the last phase in the decision mak- ing process is the implementation phase, where the proposed solution from the pre- vious phase is implemented [8].

As the amount of big data continues to exponentially grow, organizations through- out the different sectors are becoming more interested in how to manage and analyze such data. Thus, they are rushing to seize the opportunities offered by big data, and gain the most benefit and insight possible, consequently adopting big data analytics in order to unlock economic value and make better and faster decisions. Therefore, or- ganizations are turning towards big data analytics in order to analyze huge amounts of data faster, and reveal previously unseen patterns, sentiments, and customer intelli- gence. This section focuses on some of the different applications, both proposed and implemented, of big data analytics, and how these applications can aid organizations across different sectors to gain valuable insights and enhance decision making.

According to Manyika et al.’s research, big data can enable companies to createnew products and services, enhance existing ones, as well as invent entirely new busi- ness models. Such benefits can be gained by applying big data analytics in different areas, such as customer intelligence, supply chain intelligence, performance, quality and risk management and fraud detection [14]. Furthermore, Cebr’s study highlighted the main industries that can benefit from big data analytics, such as the manufacturing, retail, central government, healthcare, telecom, and banking industries [4].

Customer Intelligence

As stated from the literature, customer intelligence is a subcategory of business intelligence.

Big data analytics holds much potential for customer intelligence, and can highly benefit industries such as retail, banking, and telecommunications. Big data can create transparency, and make relevant data more easily accessible to stakeholders in a timely manner [14]. Big data analytics can provide organizations with the ability to profile and segment customers based on different socioeconomic characteristics, as well as increase levels of customer satisfaction and retention [4]. This can allow them to  make more informed marketing decisions, and market to different segments based on their preferences along with the recognition of sales and marketing opportunities [17]. Moreover, social media can be used to inform companies what their customers like, as well as what they don’t like. By performing sentiment analysis on this data, firms can be alerted beforehand when customers are turning against them or shifting to different products, and accordingly take action [7].

Additionally, using SNAs to monitor customer sentiments towards brands, and identify influential individuals, can help organizations react to trends and perform direct marketing. Big data analytics can also enable the construction of predictive models for customer behavior and purchase patterns, therefore raising overall profita-bility [4]. Even organizations which have used segmentation for many years are beginning to deploy more sophisticated big data techniques, such as real-time micro- segmentation of customers, in order to target promotions and advertising [14]. Consequently, big data analytics can benefit organizations by enabling better targeted social influencer marketing, defining and predicting trends from market sentiments, as well as analyzing and understanding churn and other customer behaviors [17].

Supply Chain and Performance Management

Big data facilitates better informed decisions within supply chain management. Gantz and Reinsel (2011) predicted that the return-on-investment for the big data market be $16.1 billion in 2014. Therefore, it has become essential for

Predictive and prescriptive analytics play a vital role in helping companies make effective decisions on the strategic direction of the organization (Demirkan and Delen, 2013)[1]. They can be applied to address problems related to the changes in organizational culture, sourcing decisions, supply chain configuration, and design and development of products or services. Descriptive analytics answer questions related to “what happened and/or what is happening”. These decisions may also involve performance analysis by employing models, techniques, and tools to help companies make quick, efficient, and effective decisions. (lit review bookmark 20)

H. Demirkan, D. Delen Leveraging the capabilities of service-oriented decision support systems: putting analytics and big data in cloud Decis. Support Syst., 55 (1) (2013), pp. 412–421 [1]

Many have argued that the market focus of competition has evolved from that of competition between individual firms to competition between entire supply chains (Craighead et al., 2009; Ketchen and Hult, 2007; Slone, 2004 ;  Whipple and Frankel, 2000). The resulting focus on supply chain management (SCM) has forced managers to rethink their competitive strategies (Zacharia et al., 2011), with many now seeking to “win with data” (Hopkins et al., 2010). Supply chain managers are increasingly reliant upon data to gain visibility into expenditures, identify trends in costs and performance, and support process control, inventory monitoring, production optimization, and process improvement efforts. In fact, many businesses are awash in data, with many seeking to capitalize on data analysis as a means for gaining a competitive advantage (Davenport, 2006). Data science, predictive analytics, and “big data” are each thought to be part of an emerging competitive area that will transform the way in which supply chains are managed and designed (Waller and Fawcett, 2013).

Although there is as yet no agreed definition of big data, many enterprise industrial SCM stakeholders and experts predict that big data will have a positive impact on their operations and activities, enabling them to make more strategic data-oriented and informed decisions. Furthermore, one of the reports from International Data Corporation (IDC), Gantz and Reinsel (2011) predicted that the return-on-investment (ROI) for the big data market would reach $16.1 billion in 2014, thus representing a growth about six-times faster than Information Technology (IT) businesses overall. Therefore, it has become imperative that more effort is put into arriving at a common consensus definition for big data in an operations or supply-chain management perspective to obtain more informed and data-oriented strategic decision making. (Literature bookmark 15)

Big data reduces operational costs for many firms. According to(Literature Bookmark 10) Accenture (2016), firms states that use data analytics in their operations have faster and more effective reaction time to supply chain issues than those that use data analytics on an ad-hoc basis (47% vs. 18%). (Literature Bookmark 10)

As for supply chain management, big data analytics can be used to forecast demand changes, and accordingly match their supply. This can increasingly benefit the manu- facturing, retail, as well as transport and logistics industries. By analyzing stock utili- zation and geospatial data on deliveries, organizations can automate replenishment decisions, which will reduce lead times and minimize costs and delays, as well as process interruptions. Additionally, decisions on changing suppliers, based on quality or price competitiveness, can be taken by analyzing supplier data to monitor perfor- mance. Furthermore, alternate pricing scenarios can be run instantly, which can ena- ble a reduction in inventories and an increase in profit margins [4]. Accordingly, big data can lead to the identification of the root causes of cost, and provide for better planning and forecasting [17].

Another area where big data analytics can be of value is performance management, where the governmental and healthcare industries can easily benefit. With the increas- ing need to improve productivity, staff performance information can be monitored  and forecasted by using predictive analytics tools. This can allow departments to linktheir strategic objectives with the service or user outcomes, thus leading to increased efficiencies. Additionally, with the availability of big data and performance informa- tion, as well as its accessibility to operations managers, the use of predictive KPIs, balanced scorecards, and dashboards within the organization can introduce operation- al benefits by enabling the monitoring of performance, as well as improving transpa- rency, objectives setting, and planning and management functions [4].

According to Milan (2015), big data provides ample opportunities in SCM as an invaluable instrument for spending analysis in terms of supply-chain risks or measuring supplier performance for senior stakeholders with an accuracy never seen before. Furthermore, Milan stated that big data comes with huge possibilities as well as the ability to drill down and identify credible areas for optimization. Big data has been making huge strides in enterprise industrial circles recently as a prospective and feasible solution to almost every organizational operations challenge facing industrial decision makers today. The research question (RQ) here is how operations can or supply-chain management take advantage by adding value (Value-adding) efficiently and effectively from the perceived enormous benefits of big data application analytics and implementation processes?

Big data analytics can improve the management of supply chain from various aspects, including supply chain efficiency, supply chain planning, inventory control and risk management, market intelligence and real-time personalized service (Wang & Alexander, 2015; Vera-Baquero et al., 2015). (Literature bookmark 2)

Meanwhile, big data can also support the supply chain to innovate new product and service development ideas and also understand how diverse sub-firms can collaborate together to optimize the operation process in a cost effective way (Tan et al., 2015). (Literature bookmark 2)

Quality Management and Improvement

Especially for the manufacturing, energy and utilities, and telecommunications indus- tries, big data can be used for quality management, in order to increase profitability and reduce costs by improving the quality of goods and services provided. For exam- ple, in the manufacturing process, predictive analytics on big data can be used to mi- nimize the performance variability, as well as prevent quality issues by providing early warning alerts. This can reduce scrap rates, and decrease the time to market, since identifying any disruptions to the production process before they occur can save significant expenditures [4]. Additionally, big data analytics can result in manufactur- ing lead improvements [17]. Furthermore, real-time data analyses and monitoring of machine logs can enable managers to make swifter decisions for quality management. Also, big data analytics can allow for the real-time monitoring of network demand, in addition to the forecasting of bandwidth in response to customer behavior.

Moreover, healthcare IT systems can improve the efficiency and quality of care, by communicating and integrating patient data across different departments and institu- tions, while retaining privacy controls [4]. Analyzing electronic health records can improve the continuity of care for individuals, as well as creating a massive dataset through which treatments and outcomes can be predicted and compared. Therefore, with the increasing use of electronic health records, along with the advancements in analytics tools, there arises an opportunity to mine the available de-identified patient information for assessing the quality of healthcare, as well as managing diseases and health services [22].

Additionally, the quality of citizens’ lives can be improved through the utilization of big data. For healthcare, sensors can be used in hospitals and homes to provide the continuous monitoring of patients, and perform real-time analyses on the patient data streaming in. This can be used to alert individuals and their health care providers if any health anomalies are detected in the analysis, requiring the patient to seek medical help [22]. Patients can also be monitored remotely to analyze their adherence to their prescriptions, and improve drug and treatment options [14].

Moreover, by analyzing information from distributed sensors on handheld devices, roads, and vehicles, which provide real-time traffic information, transportation can be transformed and improved. Traffic jams can be predicted and prevented, and drivers can operate more safely and with less disruption to the traffic flow. Such a new type of traffic ecosystem, with “intelligent” connected cars, can potentially renovate trans- portation and how roadways are used [22]. Accordingly, big data applications can provide smart routing, according to real-time traffic information based on personal location data. Furthermore, such applications can automatically call for help when trouble is detected by the sensors, and inform users about accidents, scheduled road- work, and congested areas in real-time [14].

Furthermore, big data can be used for better understanding changes in the location, frequency, and intensity of weather and climate. This can benefit citizens and busi- nesses that rely upon weather, such as farmers, as well as tourism and transportation companies. Also, with new sensors and analysis techniques for developing long term climate models and nearer weather forecasts, weather related natural disasters can be predicted, and preventive or adaptive measures can be taken beforehand [22].

3.10. RQ10: What are some of the top challenges of big data for business?

As with any disruptive technology, big data technologies introduce new challenges and problems to overcome, alongside the benefits they produce. The two most prominently discussed in our sample population are the lack of skillsets or tools required to carry out big data strategies, and concerns about privacy and surveillance, followed by various disruptions of conventional methods, labour, and legality.

The most prevalent challenges reflected in the literature center on exactly how to take advantage of the unprecedented scale of available data. This includes the new need for data scientists and programmers versed in big data applications, as well as the integration of new technical tools required to collect, store, analyze and use big data (Dobre & Xhafa, 2014). For instance, since tools and techniques for extracting data have proliferated more quickly than analytical tools, many companies are overwhelmed by the sheer volume of data they’re able to collect, but unable to identify the truly valuable pieces of information from the flood, which in turn slows down the very processes they were intended to sharpen (Gobble, 2013). Beyond talent and technology issues, the desire to incorporate big data into business challenges organizational structures themselves, as new workflows and incentives must be designed to prioritize data-driven decision-making (Fanning & Grant, 2013).

The second most prevalent challenge of big data relates to the risk of data privacy and ethical infringements, which cut across the whole big data lifecycle from collection and combination to analysis and use (Cumbley & Church, 2013). As data becomes more valuable and more prevalent, illegal markets for lucrative personal data also emerge (Pasquale, 2015). Increases in computing power and data linkages fuel fears of identity exploitation, massive online data breaches and automated identification technologies (Knight & Saxby, 2014). As consumers become more aware of these potential risks, they are less inclined to embrace products and services involving tracking technologies such as cookies or GPS trackers to collect personal data. In legal terms, big data innovation has raced ahead of existing policy, so organizations will need to address risks related to security, intellectual property and liability, before they become public relations crises.

Other concerns reported in the literature include the cost versus benefit of using big data for decision-making, the validation and integrity of collected data, and the complexities of dealing with highly distributed data sources (McNeely & Hahm, 2014). The excitement around big data has sparked the urgent notion to get on board with the latest technologies, or risk getting left behind the competition. But with a wide variety of concerns and obstacles to overcome in order to use big data in an effective, lucrative, and ethical way, recent scholarship on big data challenges is perhaps some of the most valuable information in our population.

2.4. Challenges of Big Data analytics (GOOD)

Although big data can help companies achieve competitive advantage over its rivals through many aspects, big data analytics still face a variety of challenges (Assunção et al., 2015). The main challenge of big data analytics include lack of intelligent big data sources, lack of scalable real-time analytics capabilities, the availability of sufficient network resources for running applications, the demand in necessary expansion for peer-to-peer networks, the concerns about data privacy and information security regulations, the problems with data integration and fragmented data and lack of availability of cost effective storage subsystem of high performance (Ahmad & Quadri, 2015; Wang & Alexander, 2015). Also, the requirements of expensive software and huge computational infrastructure to do the analysis cause issues in the implementation of Big Data analytics for BI (Assunção et al., 2015). Particularly, as Bog Data involve storage of massive volumes of aggregated heterogeneous data from a wide range of sources, it remains the target of hackers. The compliance to regulatory requirements, especially the data protection laws becomes an important issue (Tankard, 2012). Additionally, since the big data analytics is still in its infancy, there are no clear regulations for safeguarding and protecting the privacy, and which may harm the public trust on big data storage and its analytics.

The challenge is to establish protocols to set contractual restrictions on exposing the data to unauthorised people 224 Jiwat Ram et al. / Procedia Computer Science 87 ( 2016 ) 221 – 226 and revealing of the data, restricting the copy of the data, establishing personnel background check for those who are able to access to the data, and setting contractual restriction of the use of specific projects data. The establishment of privacy regulations is the most crucial areas for developments in Big Data for the next five years (Leonard, 2014). Apart from the security and privacy issues of big data, the hardware-technology that supports big data analytics poses challenges (computation, networking and storage technology). First, the technology is unable to provide a single computing configuration to apply on both real-time and scalable analysis. Second, the networking technology cultivates growing void between bandwidth, which limits the network capability to support real-time applications. Third, there is no well-established rule to predict the growth in storage capacity of magnetic drives (Ahmad & Quadri, 2015).

Assunção M.D., Calheiros R.N., Bianchi S., Netto M.A.S. and Buyya R. Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing. 79–80 (2015), 3 – 15

Ahmad W. and Quadri B.S.M.K. Big Data promises value: Is hardware technology taken onboard?. Industrial Management & Data Systems. 115(9), 2015.

Risk Management and Fraud Detection

Industries such as investment or retail banking, as well as insurance, can benefit from big data analytics in the area of risk management. Since the evaluation and bearing of risk is a critical aspect for the financial services sector, big data analytics can help in selecting investments by analyzing the likelihood of gains against the likelihood of losses. Additionally, internal and external big data can be analyzed for the full and dynamic appraisal of risk exposures [4]. Accordingly, big data can benefit organiza- tions by enabling the quantification of risks [17]. High-performance analytics can also be used to integrate the risk profiles managed in isolation across separate departments, into enterprise wide risk profiles. This can aid in risk mitigation, since a comprehensive view of the different risk types and their interrelations is provided to decision makers [4].

Furthermore, new big data tools and technologies can provide for managing the exponential growth in network produced data, as well reduce database performance problems by increasing the ability to scale and capture the required data. Along with the enhancement in cyber analytics and data intensive computing solutions, organiza- tions can incorporate multiple streams of data and automated analyses to protect themselves against cyber and network attacks [22].

As for fraud detection, especially in the government, banking, and insurance indus- tries, big data analytics can be used to detect and prevent fraud [17]. Analytics are already commonly used in automated fraud detection, but organizations and sectors are looking towards harnessing the potentials of big data in order to improve their systems. Big data can allow them to match electronic data across several sources, between both public and private sectors, and perform faster analytics [4].

In addition, customer intelligence can be used to model normal customer behavior, and detect suspicious or divergent activities through the accurate flagging of outlier occurrences. Furthermore, providing systems with big data about prevailing fraud patterns can allow these systems to learn the new types of frauds and act accordingly, as the fraudsters adapt to the old systems designed to detect them. Also, SNAs can beused to identify the networks of collaborating fraudsters, as well as discover evidence of fraudulent insurance or benefits claims, which will lead to less fraudulent activity going undiscovered [4]. Thus, big data tools, techniques, and governance processes can increase the prevention and recovery of fraudulent transactions by dramatically increasing the speed of identification and detection of compliance patterns within all available data sets [22].

Conclusion

In this research, we have examined the innovative topic of big data, which has recently gained lots of interest due to its perceived unprecedented opportunities and benefits in business. In the information era we are currently living in, voluminous varieties of high velocity data are being produced daily, and within them lay intrinsic details and patterns of hidden knowledge which should be extracted and utilized. Hence, big data analytics can be applied to leverage business change and enhance decision making, by applying advanced analytic techniques on big data, and revealing hidden insights and valuable knowledge.

Accordingly, the literature was reviewed in order to provide an analysis of the big data analytics concepts which are being researched, as well as their importance to decision making. Consequently, big data was discussed, as well as its characteristics and importance. Moreover, some of the big data analytics tools and methods in particular were examined. Thus, big data storage and management, as well as big data analytics processing were detailed. In addition, some of the different advanced data analytics techniques were further discussed.

By applying such analytics to big data, valuable information can be extracted and exploited to enhance decision making and support informed decisions. Consequently, some of the different areas where big data analytics can support and aid in decision making were examined. It was found that big data analytics can provide vast horizons of opportunities in various applications and areas, such as customer intelligence, fraud detection, and supply chain management. Additionally, its benefits can serve different sectors and industries, such as healthcare, retail, telecom, manufacturing, etc.

Accordingly, this research has provided the people and the organizations with ex- amples of the various big data tools, methods, and technologies which can be applied. This gives users an idea of the necessary technologies required, as well as developers an idea of what they can do to provide more enhanced solutions for big data analytics in support of decision making. Thus, the support of big data analytics to decision making was depicted.

Finally, any new technology, if applied correctly can bring with it several potential benefits and innovations, let alone big data, which is a remarkable field with a bright future, if approached correctly. However, big data is very difficult to deal with. It requires proper storage, management, integration, federation, cleansing, processing, analyzing, etc. With all the problems faced with traditional data management, big data exponentially increases these difficulties due to additional volumes, velocities, and varieties of data and sources which have to be dealt with. Therefore, future researchcan focus on providing a roadmap or framework for big data management which can encompass the previously stated difficulties.

We believe that big data analytics is of great significance in this era of data over- flow, and can provide unforeseen insights and benefits to decision makers in various areas. If properly exploited and applied, big data analytics has the potential to provide businesses with an exponential impact, an impact on business that will shape the business industry; if correctly utilised in the most powerful way known.

References

Adams, M.N.: Perspectives on Data Mining. International Journal of Market Research 52(1), 11–19(2010)

Asur, S., Huberman, B.A.: Predicting the Future with Social Media. In: ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 492–499 (2010)

Bakshi, K.: Considerations for Big Data: Architecture and Approaches. In: Proceedings of the IEEE Aerospace Conference, pp. 1–7 (2012)

Cebr: Data equity, Unlocking the value of big data. in: SAS Reports, pp. 1–44 (2012)

Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J.M., Welton, C.: MAD Skills: New Analy- sis Practices for Big Data. Proceedings of the ACM VLDB Endowment 2(2), 1481–1492 (2009)

Cuzzocrea, A., Song, I., Davis, K.C.: Analytics over Large-Scale Multidimensional Data: The Big Data Revolution! In: Proceedings of the ACM International Workshop on Data Warehousing and OLAP, pp. 101–104(2011)

Economist Intelligence Unit: The Deciding Factor: Big Data & Decision Making. In: Capgemini Reports, pp. 1–24 (2012)

Elgendy, N.: Big Data Analytics in Support of the Decision Making Process. MSc Thesis, German University in Cairo, p. 164(2013)

EMC: Data Science and Big Data Analytics. In: EMC Education Services, pp. 1–508 (2012)

He, Y., Lee, R., Huai, Y., Shao, Z., Jain, N., Zhang, X., Xu, Z.: RCFile: A Fast and Space- efficient Data Placement Structure in MapReduce-based Warehouse Systems. In: IEEE International Conference on Data Engineering (ICDE), pp. 1199–1208 (2011)

Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F.B., Babu, S.: Starfish: A Self-tuning System for Big Data Analytics. In: Proceedings of the Conference on Innova- tive Data Systems Research, pp. 261–272(2011)

Kubick, W.R.: Big Data, Information and Meaning. In: Clinical Trial Insights, pp. 26–28 (2012)

Lee, R., Luo, T., Huai, Y., Wang, F., He, Y., Zhang, X.: Ysmart: Yet Another SQL-to- MapReduce Translator. In: IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 25–36(2011)

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. In: McKinsey Global Institute Reports, pp. 1–156(2011)

Mouthami, K., Devi, K.N., Bhaskaran, V.M.: Sentiment Analysis and Classification Based on Textual Reviews. In: International Conference on Information Communication and Embedded Systems (ICICES), pp. 271–276(2013)

Plattner, H., Zeier, A.: In-Memory Data Management: An Inflection Point for Enterprise Applications. Springer, Heidelberg(2011)

Russom, P.: Big Data Analytics. In: TDWI Best Practices Report, pp. 1–40 (2011)

Sanchez, D., Martin-Bautista, M.J., Blanco, I., Torre, C.: Text Knowledge Mining: An Al- ternative to Text Data Mining. In: IEEE International Conference on Data Mining Work- shops, pp. 664–672(2008)

Serrat,O.:SocialNetworkAnalysis.KnowledgeNetworkSolutions28,1–4(2009)

Shen, Z., Wei, J., Sundaresan, N., Ma, K.L.: Visual Analysis of Massive Web Session Da- ta. In: Large Data Analysis and Visualization (LDAV), pp. 65–72(2012)

Song, Z., Kusiak, A.: Optimizing Product Configurations with a Data Mining Approach. InternationalJournalofProductionResearch47(7),1733–1751(2009)

TechAmerica: Demystifying Big Data: A Practical Guide to Transforming the Business of Government. In: TechAmerica Reports, pp. 1–40 (2012)

Van der Valk, T., Gijsbers, G.: The Use of Social Network Analysis in Innovation Studies: Mapping Actors and Technologies. Innovation: Management, Policy & Practice 12(1), 5–17 (2010)

Zeng, D., Hsinchun, C., Lusch, R., Li, S.H.: Social Media Analytics  and  Intelligence. IEEE Intelligent Systems 25(6), 13–16 (2010)

Zhang, L., Stoffel, A., Behrisch, M., Mittelstadt, S., Schreck, T., Pompl, R., Weber, S., Last, H., Keim, D.: Visual Analytics for the Big Data Era—A Comparative Review of State-of-the-Art Commercial Systems. In: IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 173–182(2012)

Adams,M.N.:Perspectives on Data Mining.International Journal of Market Research 52(1),11–19 (2010)

Asur,S.,Huberman, B.A.:Predicting the Future with Social Media.In: ACM International Conference on Web Intelligence and Intelligent Agent Technology,vol. 1, pp. 492–499 (2010)

Bakshi,K.:Considerations for Big Data:Architecture and Approaches. In: Proceedings of the IEEE Aerospace Conference, pp. 1–7 (2012)

Cebr:Data equity, Unlocking the value of big data.in: SAS Reports, pp. 1–44 (2012)

Cohen, J., Dolan, B., Dunlap, M.,Hellerstein, J.M.,Welton, C.: MAD Skills:New Analysis Practices for Big Data.Proceedings of the ACM VLDB Endowment 2(2), 1481–1492 (2009)

Cuzzocrea,A., Song, I., Davis, K.C.:Analytics over Large-Scale Multidimensional Data:The Big Data Revolution! In:Proceedings of the ACM International Workshop on Data Warehousing and OLAP, pp. 101–104 (2011)

Economist Intelligence Unit:The Deciding Factor:Big Data & Decision Making. In:Capgemini Reports,pp. 1–24(2012)

Elgendy,N.:Big Data Analytics in Support of the Decision Making Process. MSc Thesis, German University in Cairo, p. 164(2013)

EMC:Data Science and Big Data Analytics. In: EMC Education Services, pp. 1–508 (2012)

He, Y., Lee, R., Huai, Y., Shao, Z., Jain, N., Zhang, X., Xu, Z.: RCFile:A Fast and Space- efficient Data Placement Structure in MapReduce-based Warehouse Systems. In:IEEE International Conference on Data Engineering (ICDE), pp. 1199–1208 (2011)

Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F.B., Babu, S.: Starfish: A Self-tuning System for Big Data Analytics. In: Proceedings of the Conference on Innovative Data Systems Research, pp. 261–272 (2011)

Kubick,W.R.:Big Data, Information and Meaning.In:Clinical Trial Insights, pp. 26–28 (2012)

Lee, R., Luo, T.,Huai Y.,Wang, F., He, Y., Zhang, X.: Ysmart: Yet Another SQL-to- MapReduce Translator. In: IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 25–36 (2011)

Manyika,J.,Chui,M., Brown, B., Bughin, J., Dobbs,R., Roxburgh, C., Byers, A.H.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. In: McKinsey Global Institute Reports, pp. 1–156 (2011)

Mouthami, K.,Devi, K.N., Bhaskaran, V.M.:Sentiment Analysis and Classification Based on Textual Reviews. In: International Conference on Information Communication and Embedded Systems (ICICES), pp. 271–276 (2013)

Plattner, H.,Zeier, A.:In-Memory Data Management: An Inflection Point for Enterprise Applications. Springer, Heidelberg (2011)

Russom,P.: Big Data Analytics. In:TDWI Best Practices Report, pp. 1–40 (2011)

Sanchez, D.,Martin-Bautista, M.J., Blanco,I., Torre, C.: Text Knowledge Mining: An Al- ternative to Text Data Mining.In: IEEEInternational Conference on Data Mining Work- shops,pp. 664–672(2008)

Serrat, O.: Social Network Analysis. Knowledge Network Solutions 28, 1–4 (2009)

Shen, Z., Wei, J., Sundaresan, N., Ma, K.L.: Visual Analysis of Massive Web Session Da- ta. In: Large Data Analysis and Visualization (LDAV), pp. 65–72 (2012)

Song, Z., Kusiak, A.: Optimizing Product Configurations with a Data Mining Approach. International Journal of Production Research 47(7), 1733–1751 (2009)

TechAmerica: Demystifying Big Data: A Practical Guide to Transforming the Business of Government. In: TechAmerica Reports, pp. 1–40 (2012)

Van der Valk, T., Gijsbers, G.: The Use of Social Network Analysis in Innovation Studies: Mapping Actors and Technologies. Innovation: Management, Policy & Practice 12(1), 5–17 (2010)

Zeng, D., Hsinchun, C., Lusch, R., Li, S.H.: Social Media Analytics  and  Intelligence. IEEE Intelligent Systems 25(6), 13–16 (2010)

Zhang, L., Stoffel, A., Behrisch, M., Mittelstadt, S., Schreck, T., Pompl, R., Weber, S., Last, H., Keim, D.: Visual Analytics for the Big Data Era—A Comparative Review of State-of-the-Art Commercial Systems. In: IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 173–182 (2012)

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This literature review has been written by a student and is published as an example. See our guide on How to Write a Dissertation Literature Review for guidance on writing your own.