Encyclopedia of Big Data

Living Edition
| Editors: Laurie A. Schintler, Connie L. McNeely

Business Intelligence Analytics

  • Feras A. BatarsehEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32001-4_253-1



Business Intelligence Analytics is a wide set of solutions that could directly and indirectly influence the decision making process of a business organization. Many vendors build Business Intelligence (BI) platforms that aim to plan, organize, share, and present data at a company, hospital, bank, airport, federal agency, university, or any other type of organization. BI is the business umbrella that has analytics and big data under it.


Nowadays, business organizations need to carefully gauge markets and take key decisions, quicker than ever before. Certain decision can steer the direction of an organization, and halt its progress, while other decisions can improve its place in the market and even increase profits. If BI is broken into categories, three organizational areas would emerge: technological intelligence (understanding the data, advancing the technologies used, and the technical footprint), market intelligence (studying the market, predicting where it is heading and how to react to its many variables), and strategic intelligence (dictates how to organize, employ, and structure an organization from the inside; and how strategies affect the direction of an organization in general).

Main BI Features and Vendors

The capabilities of BI include decision support, statistical analysis, forecasting, and data mining. Such capabilities are achieved through a wide array of features that a BI vendor should inject into their software offering. Most BI vendors provide such features, however, the leading global BI vendors are: IBM (Watson Analytics), Tibco (Spotfire), Tableau, MicroStrategy, SAS, SAP (Lumira), and Oracle and Microsoft (PowerBI). Figure 1 below illustrates BI market leaders based on the power of execution and performance and the clarity of vision.
Fig. 1

Leading BI vendors (Forrester 2015)

BI vendors provide a wide array of software, data, and technical features for their customers; the most commonplace features include database management, data organization and augmentation, data cleaning and filtering, data normalization and ordering, data mining and statistical analysis, data visualization, and interactive dashboards (Batarseh et al. 2017).

Many industries (such as healthcare, finance, athletics, government, education, and the media) adopted analytical models within their organizations. Although data mining research has been of interest to many academic researchers around the world for a long time, data analytics (a form of BI) did not see much light until it was adopted by the industry. Many software vendors (SAS, SPSS, Tableau, Microsoft, and Pentaho) shifted the focus of their software development to include a form of BI analytics, big data, statistical modeling, and data visualization.

BI Applications

As it is mentioned previously, BI has been deployed at many domains, some famous and successful BI applications include: (1) healthcare records collection and analysis, (2) predictive analytics for the stock market, (3) airport passengers flow and management analytics, (4) federal government decision and policy making, (5) geography, remote sensing, and weather forecasting, and (6) defense and army operations, among many other successful applications.

However, to achieve such decision-making support functions, BI relies heavily on structured data. Obtaining structured data is quite challenging in many cases, and data are usually raw, unstructured, and unorganized. Business organizations have data in forms of emails, documents, surveys, sheets, tables, and even meeting notes; furthermore, they have data for customers that can be aggregated at many different levels (such as weekly, monthly, or yearly), but to achieve successful applications, most organizations need to have a well-defined BI lifecycle. The BI lifecycle is introduced in the next section.

The BI Development Lifecycle

Based on multiple long and challenging deployments in many fields, trials, and errors, and many consulting exchanges with customers from a variety of domains, BI vendors coined a data management lifecycle model for BI. SAS provided that model (illustrated in Fig. 2).
Fig. 2

BI lifecycle model (SAS 2017)

The model includes the following steps: identify and formulate the problem; prepare the data (pivoting and data cleansing), data exploration (through summary statistics charts), data transformation and selection (select ranges, and create subsets), statistical model development (data mining), validation, verification and deployment; evaluate and monitor results of models; deliver the best model; and observe the results and refine (Batarseh et al. 2017). The main goal of the BI lifecycle is to allow BI engineers to transform the big data into useful reports, graphs, tables, and dashboards. Dashboards and interactive visualizations are the main outputs of most BI tools. Figure 3 shows an example output – a Tableau Dashboard (Tableau 2017).
Fig. 3

A dashboard (Tableau 2017)

BI outputs are usually presented on top of a data warehouse. The data warehouse is the main repository of all data that are created, collected, generated, organized, and owned by the organization. Data warehouses can host databases (such as Oracle databases), or big data that is unstructured (but organized through tools such as Hadoop). Each of the mentioned technologies has become essential in the lifecycle of BI and its outputs.

Different vendors have different weaknesses and strengths, most of which are presented in many market analysis studies presented in publications from Mckinsey & Company, Gartner, and Forrester (Forrester 2015).


Business Intelligence (analytics built on top of data, in many cases big data) is a rapidly growing field of research and development and has attracted interest from academia and government but mostly from industry. BI analytics depend on many other software technologies and research areas of study such as data mining, machine learning, statistical analysis, art, user interfaces, market intelligence, artificial intelligence, and big data. BI has been used in many domains, and it is still witnessing a growing demand with many new applications. BI is a highly relevant and a very interesting area of study that is worth investing in at all venues and exploring at all levels.

Further Readings

  1. Batarseh, F., Yang, R., & Deng, L. (2017). A comprehensive model for management and validation of federal big data analytical systems. Published at Springer’s journal of Big Data Analytics.Google Scholar
  2. Evelson, B. (2015). The Forrester wave: Agile business intelligence platforms. A report published by Forrester Research, Inc.Google Scholar
  3. SAS website and reports. (2017). Available at: http://www.sas.com/en_us/home.html.
  4. Tableau website and dashboards. (2017). Available at: http://www.tableau.com/.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.College of ScienceGeorge Mason UniversityFarifaxUSA