The Evolution of BI with AI

With more and more being written about AI — and with more and more applications being developed for its use — we couldn’t help wondering about the role of AI in what we’ve come to consider business intelligence (BI). Obviously enough, BI is synonymous with data analytics and has been for a while. But we really wanted to know about BI’s origins and its evolution, which would help us to understand and to keep pace with its evolution with the introduction of AI.

One of the first things we came across was a post from DATAVERSITY, a producer of educational resources for business and Information Technology (IT) professionals on the uses and management of data. By way of historical perspective, DATAVERSITY offered this:

In 1865, Richard Millar Devens presented the phrase “Business Intelligence” (BI) in the “Cyclopædia of Commercial and Business Anecdotes.” He used it to describe how Sir Henry Furnese, a banker, profited from information by gathering and acting on it before his competition … in 1958, an article was written by an IBM computer scientist named Hans Peter Luhn, describing the potential of gathering business intelligence (BI) through the use of technology … 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. Edgar Codd recognized this as a problem, and published a paper in 1970, altering how people thought about databases. His proposal of developing a “relational database model” gained tremendous popularity and was adopted worldwid … The number of BI vendors grew in the 1980s, as business people discovered the value of business intelligence. An assortment of tools was developed during this time, to access and organize data in simpler ways. OLAP [online analytical processing], executive information systems, and data warehouses were some of the tools developed.

Given what we know about the analytical and predictive abilities of AI, it’s fairly easy to generalize, then, about the ways in which it will contribute to the evolution of AI.

Here’s What We Think

While we can’t be sure how much of this has actually come to fruition, it seems safe to assume AI helps or will help BI to:

1. Automatically analyze large datasets, identify patterns, and generate insights.
2. Forecast future trends (given #1), optimize operations, and allow even better data-driven decisions.
3. Interact with BI systems using natural language processing (NLP), making it more accessible to and usable for non-technical people.
4. Use machine-learning algorithms to understand user behavior, adapt to changing business needs, and continuously improve performance.
5. Enable real-time decision-making, making BI dynamically operational, as opposed to reporting-centric.
6. Integrate with other technologies, such as big data, the cloud, and IoT to leverage a wide range of data sources and create unified views of businesses.

Are we correct about all of that? We don’t know. We’d have to combine AI with a crystal ball and a Ouija board to be sure. But we do know we’ve come a long way since 1865. And the evolution of BI will continue with the further adaptation and inclusion of AI.

That’s why we built the Finys Suite to be ready for it.