While the earliest known use of the term “business intelligence” (BI) dates back to 1865, it wasn’t until nearly a century later that computer scientist Hans Peter Luhn — known today as the “Father of Business Intelligence” — released a paper “A Business Intelligence System” that began to really identify and break down technology’s role as an enabler of BI. The period between the 1950s and 1990s is when the term really became widespread as a way to quickly and easily understand massive amounts of information in order to make better decisions.
At the time, datasets were smaller and less complex, alternative data was far less common, and insights were groundbreaking as organizations now actually had data to analyze (versus not having any prior). As the amount of data available continues to grow each year — IDC predicts the world’s data will reach 175 ZB by 2025, which represents a compounded annual growth rate of 61% — it’s no surprise that, just like avenues for data consumption have changed, so has the role of BI.
Aside from the rise in the amount of data available over time, BI teams have surely become more advanced to keep pace with the steady adoption and sophistication of business analytics. More recently, the last several years have paved the way for data science (and therefore, machine learning and AI), as organizations aim to improve their analytics maturity, figure out what it means to truly be “data driven,” and identify tools to help turn this ideation into reality.
Now, we are not saying that data science has wholly replaced BI or that there’s never a reason to approach a problem with a BI-specific mindset. Further, we don’t just believe that data science (including ML and AI) should be done in tandem with BI with both practices isolated in their own disparate platforms.
Rather, we’re at a unique inflection point where organizations are gradually grasping and implementing self-service analytics (with mature ones moving beyond this to operationalize their use cases) and investing in a people, process, technology approach, both of which are critical steps to truly democratizing data within the enterprise.
Across the data science ecosystem (and in our own backyard, such as through our partnership with Tableau), we’re seeing the integration of machine learning in more BI and analytics roles and tools. BI is on track to further move away from traditional look-back analysis and toward even more sophisticated predictive and prescriptive analytics. Further, as more analysts and business stakeholders use data to transform their business efficiency and output, we may see augmented analytics expand beyond the data science pipeline to help data analysts automate various elements of their day-to-day work (and simultaneously pick up new skills from being involved in machine learning projects, from data access and prep to AutoML).