The Evolution of Business Intelligence

Nowadays we live in an ultra-competitive world, where finding ways to emerge from the competition is vital. Business intelligence is the key to gain this advantage and is a set of theories, methodologies, processes, architectures and technologies that can transform raw data into meaningful and useful information for business purposes. BI has become increasingly important to the success of businesses in almost every industry.

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For the first time, the term “Business intelligence” was used in an article by Hans Peter Luhn, an IBM researcher, in 1958. In 1968, only individuals with extremely specialized skills could translate data into usable information. At this time, data from multiple sources was normally stored in silos, and research was typically presented in a fragmented, disjointed report that was open to interpretation.

Prior to Business Intelligence, most of organization analyzed their business operations using decision support applications that are queried and reported directly on data stored in business transaction databases: this approach ended up with several problems like dispersion of data across different systems, poor data quality, lack of historical information, etc. This scenario was one of reason why Data warehousing was introduced (becoming popular in the 1980s), in order to help solve these data and performance issues.

But the concept of BI became widely used only after Howard Dresner has launched the idea that the data in IT systems can be exploited by the business itself in the early 1990’s.

As recent as the early 2000s, data visualization tools were handled almost exclusively by IT. If the management wanted to know the business data, they had to submit a query request to the IT department. As the cost of processing power and storage reduced, companies collected more data and extracted some of the data into data warehouses. On Line Analytical Processing (OLAP) of the data in data warehouses allowed anyone to analyze data without getting permission from IT (OLAP is a system that allows users to analyze data, from a variety of sources, while offering multiple paradigms, or perspectives. Databases configured for OLAP use a multidimensional data model, supporting complex analysis and ad hoc queries).

In today‟s digital economy, every business runs on data. According to a IBM report the world creates 2.5 quintillion bytes of data every day: in other words, this is equivalent of saying that 90% of the data, which exists now, has been created in the last two years. This is because the IT industry continues to rapidly evolve as well as volume of the analytical data is also increasing drastically.

Nowadays, thanks to self-service data visualization tools and software as a service (SAAS), companies can manage their own big data through an easy-to-disseminate dashboard. This enabled laypeople, from the higher-ups down to the entry level staffer, to make use of the data without an intermediary.

Business Intelligence (BI) is being increasingly used to address the challenges of business decisions posed by large amounts of data. Growing penetration of internet across the globe is one of the significant factors that contribute to the growth of the high volume of data that supplements the growth of structured data market. The appearance of the internet of things (IoT) and Big Data are undoubtely driving the growth of the unstructured global data market for the forecast period.