Business Intelligence Vs. Analytics: Understanding The Role Of Each In Making Informed Decisions

Information impacting business operations is diverse, complex and growing at staggering rates. Due to unrelenting competition, changing markets, and accelerating rates of adoption for new technology, there is a tremendous strain on IT and business infrastructures. Accessibility to actionable knowledge continually sparks the debate between business intelligence (BI) and analytics, questioning the roles each of them plays in making informed decisions.

In the past, organisations have struggled to find people willing to sift through mountains of data in order to properly analyse the information needed to make smart decisions. BI made this process easier by introducing analytics as part of the company’s strategic decision making process. Unfortunately, many companies striving to run their entire organisation based on BI alone have fallen short for a number of reasons, including:

1. The same people who were sifting through all of the data are now trying to manage the surplus of data required to create an all-encompassing warehouse; 

2. BI infrastructure and design are faced with a dilemma: as soon as they are completed, they are out of date due to the massive proliferation of data in the business ecosystem. It is almost impossible for organisations to keep up with the veritable explosion of data from new sources; 

3. The needs of an organisation are constantly shifting. In order to respond to these changes, it is necessary (but virtually impossible) to anticipate today what will happen tomorrow.

My guess is that this debate of BI and analytics has been in progress since the inception and branding of BI as a standalone discipline for organisations. BI, as I see it, is a complete end-to-end platform consisting of tools, processes and business models that allow for the retrieval of relevant information in the best format for your business.

At this level, analytics is a key part of the BI process. It’s about the predictability of the business – to the extent in which you can predict it – based on potential variances of business norms. The question of what data is being retrieved becomes static in the bigger picture. At this point, it is necessary to move from “static analytics” to “dynamic analytics,” allowing the dynamic concept of searching and obtaining relevant information to come to fruition.

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