The following article cleans up with all the buzzwords, often used interchangeably in practice. In particular, the interrelationships between Business Intelligence, Business Analytics, Big Data Analytics, Data Science and Data Mining are presented in the following.
Business Intelligence vs. Business Analytics
While the term “Intelligence” has been used by AI researchers since the 1950s, “Business Intelligence” (BI) only became a popular term in the 1990s. BI is defined as the ability of an organization or business to reason, plan, predict, solve problems, think abstractly, comprehend, innovate, and learn in ways that increase organizational knowledge, inform decision processes, enable effective actions, and help to establish and achieve business goals. It comprises both technical and organizational elements that presents historical information to its users for analysis, the overall purpose of increasing organizational performance.
In contrast, Business Analytics (BA) is a much younger term, coined by Davenport(2006), referring to the key analytical component in BI, and emphasizing the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. So, while BI is focused on past data, BA tries to predict future outcomes. Both are jointly referred to as Business Intelligence and Analytics (BI&A).
Big Data Analytics
The basic idea behind BI&A, the generation of explanatory and predictive insights for supporting decision-making, as well as the underlying mathematical modeling techniques is, in fact, not new at all. What is new and different is the characteristics of the generated data, the respective technology for collecting, storing and analyzing it, and also the accelerating need for large-scale analytics in order to sustain business competitiveness and to assist day-to-day decision-making. This fundamentally different type of data is called “Big Data”. One of the key characteristics of Big Data is its unstructured nature. To analyze such data, you need sophisticated technologies, and the term Big Data Analytics (BDA) emphasizes the extension of BI&A by these specialized methods. For example, one could state five different methodical fields of BDA: text, audio, video, social media and predictive analytics. From the latter predictive analytics, which deals mostly with structured data, overshadows other forms of analytics applied to unstructured data, which constitutes about 95% of big data. Various authors classify the historic evolution of analytics in different phases. BI&A 1.0, which mainly handles structured, DBMS-based (Database-Management-System-based) content, can be differentiated from BI&A 2.0, which aims at unstructured, web-based content. BI&A 3.0 is the defined as the analysis of mobile and sensor-based content. Furthermore, Analytics 1.0 (the “era of BI”) and Analytics 2.0 (the “era of Big Data”) can be distinquished in a similar way like BI&A 1.0 and 2.0. Additionally, Analytics 1.0 only deals with internal data, whereas Analytics 2.0 comprises external data sources as well. Analytics 3.0 however is defined as the analytics for supporting customer-facing products, services and features in the “era of data-enriched offering”. Note that the understanding of which business needs can be addressed by analyzing Big Data and the necessary expertise to do so is still evolving. Current techniques in analyzing complex data formats like images, audio, and video, are still in their infancy, and better methods for BDA, like semantic merging of different types of Big Data streams, are yet to be developed.
Data Science vs. Data Mining
At a high level, Data Science (DS) is a set of fundamental principles that support and guide the principled extraction of information and knowledge from data. These principles and techniques are broadly applicable across various fields in science and business. In contrast, Data Mining (DM) is the actual extraction of knowledge from data, via technologies that incorporate these principles. In other words, DM is a set of methods and algorithms (including Machine Learning) applying the general principles within DS. Note that in contrast to BI&A, neither DM nor DS are intended to be useful for business purposes only or ultimately aim to increase organizational performance. Even if they are widely used in business, their nature is purely technical and their purpose often more scientific. In summary, DM is closely related to DS. In particular, ML, as a set of specific statistical methods, is heavily applied in both DM and Artificial Intelligence (AI) for generating knowledge. This knowledge either can be used for informing humans directly (especially for BDA purposes) or induced into knowledge databases to build an intelligent entity.
Chen, H., Chiang, R. H. L., & Storey, V. (2012): Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188.
Davenport, T. H. (2006): Competing on Analytics. Harvard Business Review. (January 2006), 98–107.
Davenport, T. H. (2013): Analytics 3.0. Harvard Business Review. (December 2013), 64–72.
Davenport, T. H., & Harris, J. G. (2007): Competing on Analytics: The New Science of Winning. Boston, Massachusetts: Harvard Business School Press.
Davis, C. K. (2014): Beyond Data and Analysis: Why business analytics and big data really matter for modern business organizations. Communications of the ACM, 57(6), 39–41.
Gandomi, A., & Haider, M. (2015): Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
Isik, O., Jones, M. C., & Sidorova, A. (2011): Business Intelligence (BI) Success and the Role of BI Capabilities. Intelligent Systems in Accounting, Finance and Management, 18(4), 161–176.
Phillips-Wren, G., Iyer, L. S., Kulkarni, U., & Ariyachandra, T. (2015): Business Analytics in the Context of Big Data: A Roadmap for Research. Communications of the Association for Information Systems, 37(23).
Popovič, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012): Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729–739.
Provost, F., & Fawcett, T. (2013): Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59.
About the author