Business intelligence has come a long way since Richard Millar Devens used it in the Cyclopædia of Commercial and Business Anecdotes in 1865. Devens referenced business intelligence in describing how a banker benefited from gathering and acting on information before the competition.
That definition isn’t far off from where things stand now. Though business intelligence has undergone several incarnations over the past few decades, it’s still about gathering and analyzing data to generate meaningful information and actionable conclusions.
Where is business intelligence headed? Let’s examine the trajectory below by going back to BI’s roots.
Business Intelligence of Recent, and Still-Current Memory
While BI was a well-known concept before the advent of computing, technology-enabled business intelligence to become science instead of an art. People with the know-how could suddenly turn data into business value.
The methods for storing and interacting with data changed over the years, as Dataversity notes, Decision Support Systems (DDS) was the first database management system. Some historians even say modern BI evolved from DSS.
To enhance DSS technology, online analytical processing (OLAP) was created to analyze data from several sources. Executive information systems (EIS) came next. EIS was a software focused on helping high-level executives and other management make decisions by providing streamlined access to up-to-date information. Yet it was less helpful in reality than theory and its usage declined.
Data warehouses came about in the 80s, allowing businesses to store their information in one place instead of silos for the first time. At the time, data warehousing was a significant improvement in terms of redundancy as it reduced the time it took to access data. Around the same time, the concept of BI as a way of producing regular data reports in a visually coherent way began gaining steam. The tools developed reflected this goal.
Around the turn of the century, business intelligence services introduced simpler tools with the intention of making each user more self-sufficient when looking for insights. This is more or less where things stand in the present day, except for that whole big data transition.
Business Intelligence of Today and Tomorrow
The passage into big data happened so seamlessly that it’s surreal to think about how much data is being generated every second around the world from a growing number of sources, by an increasing number of users.
This pace of creation has rendered a lot of BI technologies obsolete, particularly ones light on cutting-edge analytics solutions and next-generation product software architecture.
Cutting-edge analytics methods include predictive, prescriptive, descriptive, diagnostic, and streaming analytics, but more importantly, it leverages artificial intelligence and machine learning to automate analytics tasks, make insights more accurate, personalize them to the user, and serve them faster.
On the architecture side, companies like ThoughtSpot are reshaping how BI architecture is built, incorporating search, automated insights, ease of use, scale and enterprise governance in one tool.
Other aspects of tomorrow’s BI technology are the secure sharing of insights to partners and suppliers, multi-cloud tools, enhanced data quality management, and features that facilitate BI collaboration.
These things have already come to fruition in some capacity, but as we’ve seen with BI’s own trajectory and evolution, a lot of room for growth exists.