Business Intelligence Analytics
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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
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.
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
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.
- 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
- Evelson, B. (2015). The Forrester wave: Agile business intelligence platforms. A report published by Forrester Research, Inc.Google Scholar
- SAS website and reports. (2017). Available at: http://www.sas.com/en_us/home.html.
- Tableau website and dashboards. (2017). Available at: http://www.tableau.com/.