Why is Business Intelligence so Important?

Business intelligence is used by most large organizations today for the purpose of managing day-to-day operational data as well as to store this operational data to generate insights to support decisions. This article will explore the differences between operational data and decision support data and explain the roles they play within a business environment. We will also consider the business intelligence framework and how its components interact to form a cohesive and integrated system.

Business intelligence (BI) is “a term that describes a comprehensive, cohesive, and integrated set of tools and processes used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information to support business decisions." Daniel Marino explains in a recent article written for Insurance News Net that business intelligence is being used to help reduce healthcare costs for patients. He explains that there are four information strategies that make up an effective BI strategy for the healthcare industry: Population health analytics, risk-based cost analysis, performance analytics, and care management. All of this comes down to database management to capture, store, process, and analyze the data.

The BI framework allows an organization to take raw data and analyze it to be transformed into information. The information is transformed into knowledge and knowledge is then transformed into wisdom. This makes the organization more intelligent as a whole by allowing for the constant monitoring of data to continuously improve the organization through decision support. Business intelligence is a framework for “collecting and storing operational data,” aggregating this data into decision support data, generating information with the data, presenting this information to support decisions, making decisions which generates more data, monitoring results, and predicting future outcomes.

Business intelligence requires the use of a wide range of technologies and applications; the culmination of which represents the architecture required to store, transform, integrate, present, analyze, monitor, and archive data. This architecture is made up of data, people, processes, technology, and management of all of these components. The BI framework begins with external and operational data to be extracted, transformed, and loaded into a data warehouse or data mart. From here we can query the desired data to generate analytics and visual reports for the end-user. Data monitoring and alerts is also used to monitor business activities through the use of metrics revealing specific performance.

People and processes are integrated using technology to add value to the organization through the end-user application of the information. With traditional information systems, the key focus was operational automation and reporting. Business intelligence tools take this one step further by allowing the strategic and tactical use of information to constantly improve the company. Business intelligence has evolved over the years from the traditional mainframe-based OLTP system to managerial information systems (MIS), first-generation departmental decisions support systems (DSS), first generation BI, second generation BI (OLAP), and finally third generation mobile BI and cloud-based systems.

Prior to the business intelligence environment, the first-generation decision support system was widely used. A decision support system (DSS) “is an arrangement of computerized tools used to assist managerial decision making." A DSS is generally narrower in scope than BI, initially only used by a few managers within an organization. Through the advancement of technology the applications of DSS and user access grew; expanding the usefulness of DSS and leading to further evolution into the realm of the more agile BI systems.

It is worth noting that there are key differences between operational and decision support data, each serving a very different purpose and requiring a different data format and structure. Operational data is most often stored in a relational database with highly normalized tables to support high volume transactions that are frequently updated. On the other hand, decision support data is typically stored in a data warehouse or data mart and is characterized by lower volume transactions with less normalized tables than operational data. Operational data represents real time data supporting operations as they happen, whereas decision support data is historical and used to generate time slices of the operational data to inform decisions. While operational data is more often stored in many tables, decision support data has been aggregated to be stored in few tables.

In summary, business intelligence is used by organizations to manage operational data while storing this data to later analyze to generate insights. These insights allow organizations to constantly refine their operations which generates more data for further refinement. The evolution of decision support systems has broadened the application of data as well as the access of end-users. Business intelligence is made up of a variety of components to produce the end applications to include the integration of people, data, processes, technology, and the management of these integrated components. BI makes organizations smarter; offering the ability to see empirical trends to back decisions rather than guessing and hoping for the best.

References

Coronel, C., Morris, S., & Rob, P. (2013). Database Systems: Design, Implementation, and

Management (10th ed.). New Jersey: Cengage Learning, Course Technology, Inc.

Marino, D. (2014, March 27). Using business intelligence to reduce the cost of

care.InsuranceNewsNet. Retrieved April 17, 2014, from http://insurancenewsnet.com/oarticle/2014/03/27/using-business-intelligence-to-reduce-the-cost-of-care-a-480858.html#.U1BKSPldX8k