If you’re a knowledge worker who frequently makes informed strategic decisions leveraging data and analytics, you know the plight of finding the right data or dashboards, running SQL queries, and finding patterns and trends. You turn to your friendly analysts, who are already slogged and overworked. They have no more room for additional analysis in their pipeline. You're left frustrated, waiting for weeks, or still proceed without adequate insights. Now imagine the AI-powered analyst comes around, who not only sifts through loads of data and queries, but also shares powerful trends, insights, and actionable recommendations, all while you brew your coffee. A new AI-led era in business intelligence (BI) is transforming the landscape, offering a glimpse into a future where data accessibility and analysis are not just streamlined and democratized but are also enriched with deeper insights that provide a broader understanding of available information sources.
Early beginnings to Cloud-based innovation
We’ve come a long way in business intelligence. The birth of on-premises giants like Microstrategy and Business Objects in the 1980s moved us from raw data to data mining and centrally generated reporting–a revolution at the time. Yet, these tools empowered only a select few, leaving the promise of data-driven decision-making unfulfilled for the broader enterprise. Then came Tableau in the 2000’s to query relational databases, online analytical processing cubes, and spreadsheets to generate graph-type data visualizations. And while these generated visually beautiful reports and dashboards, IT Admins still needed to manage and administer servers and scale infrastructure with typical updates and maintenance overheads. Then came PowerBI (2011) and Data Studio (2016) leveraging a serverless, browser-based IDE, with drag and drop canvas for data visualization, so anyone could self-serve dashboards building on data from spreadsheets to enterprise systems, CRMs, or data warehouses. No admin overhead meant analysts could spend time actually extracting insights, and less time interacting with the IT Admin.
The direct access to the database, and light weight data modeling, meant every team could have a front-end, a data-app for each use case.Teams with prolific dashboards made data-informed decisions, rapidly spreading beautiful data stories, but a data model in every front-end application led to the challenge of a lack of commonly shared glossary of business metrics. Every department has its own model, and new calculated fields and formula on top of the data warehouse or data lakes. Departments now have more than five definitions of ARR (annual recurring revenue), and when producing the QBR (quarterly business revenue) reports for C-suite, no one quite knows the single source of truth.
