Business Intelligence and Bu

Big Data Analytics and Business Intelligence 大数据分析学和商务智能 Dr Zhaohao Sun, PhD PNG University of Technology [email protected]; [email protected] Federation University Australia [email protected] Hebei University of Science and Technology

© Prof. Dr Zhaohao Sun, PNG Univ of Tech, HUST 30 Dec 2015

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Abstract  This presentation explores big data analytics and business intelligence and their relationships. It proposes an ontology of big data analytics and an service oriented architecture based on big data analytics. The approach proposed in the presentation might facilitate the research and development of big data, intelligent big data analytics, intelligent business analytics and business intelligence with their applications.  这一讲座探讨大数据分析学和商务智能及它们的关系，提出大数据分析学的概念网络和基于大数据分析学的面向服务的架构体系。讲座提到的思想和方法将有利于商务数据分析学、大数据分析学、商务智能和智能代理人的研究与发展。 © Prof. Dr Zhaohao Sun, PNG Univ of Tech, HUST 30 Dec 2015

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Outlines  Motivations  Fundamentals of Big Data Analytics  Business Intelligence and Big Data Analytics  BASOA: Big Data Analytics Services Oriented Architecture  Applying BASOA to BI  Related Work and Discussion  Conclusion  To cite it: Sun Z (2015) Big Data Analytics and Busienss Intelligence, Research Seminar, Hebei Uni of Sci. & Tech., 30 Dec 2015. BAIS No. 15001,


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Motivations  2002 Gartner researcher described 3D->3V of big data in ecommerce  the 3V. Doug (Laney, 2001) used 3V: volume, velocity and variety to represent the 3v characteristics of big data

 2007 Nature published big data as a special issue  2011 McKinsey published big data as The next Frontier for innovation and productivity.  Big data in 2012 (March) http://www.cccblog.org/2012/03/29/obamaadministration-unveils-200m-big-data-rd-initiative

 I forwarded this as important news to my colleagues at UB following day as an issue of IT IS series.  2014 published a book on demand-driven web services (including the research on big data and big data analytics)  Ref: Manyika, James, et al. “Big data: The next Frontier for innovation and productivity”, McKinsey May 2011. © Prof. Dr Zhaohao Sun, PNG Univ of Tech, HUST 30 Dec 2015

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Motivations (Cont’d)  2015 published three conference proceeding papers (2 LNCS, 1 IEEE). Two journal papers (ERA A, SCI) accepted for publication.  See www.researchgate.net or www.researchgate.com

 2015 Founder of Centre of Big Data Analytics and Intelligence  Big data and big data analytics has become one of the important research frontiers [1]  CFPs for books, conferences and journals, market analysis, HR market increased exponentially

 Big data analytics is an emerging science and technology  Business intelligence (BI) has received widespread attention in academia, e-commerce, and business over the past two decades [5] although it was introduced in 1958.  IDC predicts that the business analytics software market will grow at a 9.7% compound annual growth rate over the next five years from 2012 to 2017 [3] © Prof. Dr Zhaohao Sun, PNG Univ of Tech, HUST 30 Dec 2015

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Motivations (Cont’d)  China announced big data as national strategy in Oct 2015  Research Questions  What is the relationship between big data analytics and BI?  How can big data analytics enhance BI?

 To address these questions  Propose an ontology of big data analytics.  Examine big data analytics as a technology for supporting BI through examining the relationship between big data analytics and BI.  Present a big data analytics service oriented architecture (BASOA), and  Explore how to apply big data analytics as a service to enhance BI.


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Fundamentals of Big Data Analytics  Big data analytics (BA) as a process of collecting, organizing and analyzing big data to discover patterns, knowledge, and intelligence as well as other information within the big data [7]*.  a combination of big data management and big data mining

 BA is an integrated form of data analytics and web analytics for big data [2].  BA is an emerging science and technology involving the multidisciplinary state-of-art ICT, mathematics, operations research (OR), machine learning, and decision science for big data [1, 2]  BA = big data descriptive analytics + big data predictive analytics + big data prescriptive analytics [11]  * See the no. of the references of our i3e 2015 paper. © Prof. Dr Zhaohao Sun, PNG Univ of Tech, HUST 30 Dec 2015

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Big Data Descriptive Analytics  Big data descriptive analytics (BDA) is descriptive analytics for big data [12].  discover and explain the characteristics of entities and relationships among entities within the existing big data [13].

 BDA addresses  what happened, and when?  what is happening?

 through analyzing the existing big data using analytical techniques and tools.  E.g, web analytics for pay-per-click or email marketing data

 BDA takes 80% of the tasks of big data analytics


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Big Data Predictive Analytics  Big data predicative analytics (BPA) is predicative analytics for big data,  Forecast trends by addressing the problems such as  what will happen?  what is likely to happen and why it will happen [12, 15].

 Create models to predict future outcomes or events based on the existing big data [13 ].  E.g., BPA can be used to predict where might be the next attack target of terrorists


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Big Data Prescriptive Analytics  Big data prescriptive analytics is prescriptive analytics for big data.  addresses the problems such as  what we should do?  why we should do it?  what should happen with the best outcome under uncertainty?

 through analyzing the existing big data using analytical techniques and tools [12, 13, 15].  E.g., it can be used to provide an optimal marketing strategy for an e-commerce company.


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Ontology  An ontology is a formal naming and definition of a number of concepts and their interrelationships that  really or fundamentally exist for a particular domain of discourse [16].

 Then, an ontology of big data analytics is a network consisting of a number of concepts and their interrelationships for big data analytics.  Historically, ontology is a concept in philosophy, now is used in computer science and AI


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An Ontology of Big Data Analytics Big Data Analytics

Big data descriptive analytics

Big data predictive analytics

Big data prescriptive analytics

Big data & data analytics


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Data Analytics  Data analytics might be the oldest among all types of analytics.  Data analytics is a method or technique that uses data, information, and knowledge to learn, describe and predict something [15].  Data analytics is a new form of data analysis taking into the contemporary information communications technology (ICT) and Web technology in order to meet the human’s sentiment of pursuing new things and more sophisticated technologies  Data analytics is the science and technology about examining, summarizing, and drawing conclusions from data to learn, describe and predict something [2].


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Technical Fundamentals of Data Analytics  Big data analytics = Big data + Data analysis + DW + DM + SM + ML + Visualization + optimization  DW = data warehouse, DM = Data mining, SM = statistical modelling  ML = machine learning  Aims to enhance decision making

 This representation reveals the fundamental relationship between big data, data analysis and big data analytics, i.e,  big data analytics is based on big data and data analytics.  shown that CS and ICT play a dominant role in the development of data analytics through providing sophisticated techniques and tools of DM, DW, ML and visualization.

 SM and optimization still plays a fundamental role in development of big data analytics.


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DW and DM  DW and DM are a part of BI  DW was introduced in 1988  Teradata founded in 1979  Barry Devlin & Paul Murphy of IBM propose “data warehouse” as a new concept in 1988  Bill Inmon (1992) Building the data warehouse, the he as father of DW.  DW is a subject-oriented, integrated, non-volatile, time variant dataset to support management decision making

 DM was introduced in 1973 by a researcher, and officially founded in 1989 through founding of SIGKDD of ACM,  Ref: Xu, Zipei (2015), Turben et al (2012), etcx. © Prof. Dr Zhaohao Sun, PNG Univ of Tech, HUST 30 Dec 2015

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DM and SA  DM = Descriptive DM + Predictive DM  Descriptive DM = 针对过去，揭示规律  Predictive DM =面对未来，预测趋势

 Statistical analysis = descriptive SA + predictive SA  The relationship between DM and SA.  Ref: Xu, Zipei (2015), Turben et al (2012), etc


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Remarks on Big Data Analytics Ontology and Equation  The proposed ontology of big data analytics is a concise representation for big data analytics constitutes at a relatively high level.  Big data analytics equation is a concise representation for the technological components of big data analytics  Consider the big data descriptive, predictive and prescriptive analytics as one dimension, and the technological components of big data analytics as another dimension,  which is a 2-dimension analysis as a future research work.


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Business Intelligence  Luhn, H. P. (1958). A Business Intelligence System. IBM Journal of Research and Development 2(4), 314-319  Dresner, Howard of Gartner uses BI as a marketing brand and strategy in 1989.  BI is a set of techniques and methods that support business DM based on facts (Dresner, 1989).

 Then BI has been increasing attention since then and scientific BI through examining its theoretical foundations with applications.  BI can be defined as a set of theories, methodologies, architectures, systems and technologies that support business decision making with valuable data, information and knowledge. © Prof. Dr Zhaohao Sun, PNG Univ of Tech, HUST 30 Dec 2015

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Technical Perspective on BI  BI = DSS + TPS+ OLAP+ DW + DM+ visualization  Where DSS = Decision support systems, Herbet Simon(19162001) is the father of DSS.  IDSS = Model base + KB + DB+ Inference engine TPS = transaction processing systems OLAP= online analytical processing

 Visualization was first emphasized by F.J. Anscombe in 1973  Diagrams in statistical analysis.

 Edward Tufte of Yale U provide Visualization with theoretical foundation and creates visualization as a discipline  The Visual Display of Quantitative Information, 1983


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Business Intelligence and Big Data Analytics  Big data analytics can be considered a part of BI [5],  because it “supports business decision making with valuable data, information and knowledge” [2]

 Both BI and big data analytics are common in emphasizing either valuable data or information or knowledge, and share some common tools to support business decision making.


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BASOA: Big Data Analytics Services Oriented Architecture


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BASOA (Cont’d)  In this BASOA, big data analytics service provider, big data analytics service requestor, big data analytics service broker are three main players  big data analytics service provider  include analytics developers, analytics vendors, analytics systems or software and other intermediaries

 big data analytics service requestor,  include organizations, governments and all level business decision makers such as CEO, CIO and CFO as well as managers, etc,

 big data analytics service broker  all the entities that facilitate the development of big data analytics services, which include popular presses etc. © Prof. Dr Zhaohao Sun, PNG Univ of Tech, HUST 30 Dec 2015

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Applying BASOA to BI  Analytics as a service (AaaS) is a relatively new concept that has emerged as a rapidly growing business sector of web analytics industry [17]  BAaaS (Big data analytics as a service) = services provided by an individual or organization or information system using a wide range of analytic tools or apps [12].  BAaaS has the ability to turn a general analytic platform into a shared utility for an enterprise with visualized analytic services [12].  Big data analytics services include e-analytics services or web analytics services (WAS) [2]


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Applying BASOA to BI (Cont’d)  BAaaS model has been adopted by many famous web companies such as Amazon, Microsoft, and eBay [12]  BASOA is an architecture for supporting business decision making with big data analytics services.  The theory of big data analytics providers, brokers and requestors of the BASOA can facilitate the understanding and development of BI and business decision making. E.g.,  from a deep analysis of the BASOA, an enterprise and its CEO can know who are the best big data analytics providers and brokers in order to improve his business, market performance, and competition


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Enterprise Acceptability of BASOA  We surveyed 71 IT managers at the Association for Education in Journalism and Mass Communication (AEJMC) in Montreal during August 6-9, 2014 [2],  to collect data concerning the enterprise-level acceptability of the BASOA concept.

 These results indicate some preliminary support for the BASOA similar to the way private mortgage and loans work in the USA.  Based on this preliminary enterprise acceptability of this BASOA, we propose that more research be done to investigate how it could be used.


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Related Work and Discussion-I  Why does big data analytics really matter for modern business organizations?  Davis considers that the current big data analytics has embodied the state of art current development of modern computing [27]

 Big data analytics and BI have drawn an increasing attention in the computing, business, and e-commerce community.  E.g., Lim et al [5] consider business intelligence and analytics (BIA) as a current form replacing the traditional BI,  whereas we still consider BI and big data analytics are two different concepts.  Fan et al [6] provide a marketing mix framework for big data management .


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Related Work and Discussion-II  Ontology has been important in CS and AI [16].  We explored “ontology of big data analytics” and put it as a part of this research through updating our early work on data analytics, business analytics and big data analytics [1].  We explored the interrelationship among big data analytics, big data descriptive analytics, big data predictive analytics, and big data prescriptive analytics using the proposed ontology.

 The result reported here is only a beginning for providing a relatively comprehensive ontology of big data analytics.  In this direction, we will develop an ontology of big data analytics with three levels for each related analytics  big data, methods and applications .


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Related Work and Discussion-III  SAP has introduced its enterprise service-oriented architecture [23] (p. 383).  SAP’ architecture specializes general services to enterprise services whereas our BABES specializes general services to analytics services.

 Big data analytics services should be a part of enterprise services  SAP’s enterprise systems focus on key applications in finance, logistics, procurement and human resources management as an ERP system.  Our BASOA will be incorporated into the next generation EIS integrating SCM, CRM and KM systems, in particular the cloudbased version of EIS.


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Conclusion  Proposed an ontology of big data analytics, and looked at the relationship between big data analytics and BI.  Presented a big data analytics service oriented architecture (BASOA) and discussed how to use BASOA to enhance BI.  The preliminary analysis on the collected data shows that this proposed BASOA is viable for facilitating the development of BI.


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Future Work  Besides mentioning in the previous section, we will  Explore big data analytics and its applications in e-commerce and cloud services, and realize BASOA using intelligent agents technology,  Look at some implementation related issues for developing BASOA based e-commerce systems such as how to collect, store, and process big data –  by whom, for what, access rights, and many more.


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References (see the references of the paper)  Sun Z, Zou, H, & Strang K (2015) Big data analytics as a service for business intelligence, The I3E 2015, 13-15 Oct, Delft, Netherlands. LNCS 9373, Springer, DOI:10.1007/978-3-319-25013-7_16.  C. P. Chen & C.-Y. Zhang, Data-intensive applications, challenges, techniques and technologies: A survey on Big Data, Information Sciences, (275): 314–347, 2014.  Z. Sun, K. Strang & J. Yearwood, Analytics service oriented architecture for enterprise information systems, in CONFENIS 2014, 4 - 6 Dec 14, Hanoi, 2014.  E. Lim, H. Chen & G. Chen, Business Intelligence and Analytics: Research Directions," ACM Trans. on Management Inform Syst, 3(4):1-10, 2013.  S. Fan, R. Y. Lau & J. L. Zhao, Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix, Big Data Research, 2: 28–32, 2015.  A. Gandomi & M. Haider, Beyond the hype: Big data concepts, methods, and analytics, Int’l J of Information Management 35:137–144, 2015.  C. Holsapplea, A. Lee-Postb & R. Pakath, A unified foundation for business analytics, Decision Support Systems, 64:130–141, 2014.  R. van der Meulen & J. Rivera, Gartner Says Worldwide Business Intelligence and Analytics Software Market Grew 8 Percent in 2013, 2014. Available: http://www.gartner.com/newsroom/id/2723717. [Accessed 28 6 2014]. © Prof. Dr Zhaohao Sun, PNG Univ of Tech, HUST 30 Dec 2015

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References (Cont)  M. Minelli, M. Chambers & A. Dhiraj, Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses, Wiley & Sons, 2013.  D. Delena & H. Demirkanb, Data, information and analytics as services," Decision Support Systems, 55 (1): 359–363, 2013.  Z. Sun & J. Yearwood, A theoretical foundation of demand-driven web services, in Demand-Driven Web Services: Theory, Technologies, and Applications, IGIGlobal, 20014, pp. 1-25.  H. P. Luhn, A Business Intelligence System, IBM J of Research and Development 2(4): 314-319, 1958.  A. Brust, Gartner releases 2013 BI Magic Quadrant," 2013. Available: http://www.zdnet.com/gartner-releases-2013-bi-magic-quadrant7000011264/. [Accessed 14 2 2014].  R. J. Kauffman, J. Srivastava & J. Vayghan, Business and data analytics: New innovations for the management of e-commerce, Electronic Commerce Research and Applications, 11:85–88, 2012.  C. K. Davis, Viewpoint Beyond Data and Analytics- Why business analytics and big data really matter for modern business organizations," CACM, 57(8):39-41, 2014.  Z. Sun & G. Finnie, Intelligent Techniques in E-Commerce: A Case-based Reasoning Perspective, Heidelberg Berlin: Springer-Verlag, 2004; 2010. © Prof. Dr Zhaohao Sun, PNG Univ of Tech, HUST 30 Dec 2015

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