The New Reality of Business Intelligence and Big Data
From business intelligence to operational AI — the evolution of data-driven decision making.
title: "The New Reality of Business Intelligence and Big Data" slug: "new-reality-business-intelligence-and-big-data" description: "From business intelligence to operational AI — the evolution of data-driven decision making." datePublished: "2013-01-26" dateModified: "2026-03-15" category: "Business Intelligence" tags: ["business intelligence", "big data", "analytics", "AI"] tier: 2 originalUrl: "http://www.applieddatalabs.com/content/new-reality-business-intelligence-and-big-data" waybackUrl: "https://web.archive.org/web/20130126111352/http://www.applieddatalabs.com:80/content/new-reality-business-intelligence-and-big-data"
The New Reality of Business Intelligence and Big Data
Back in early 2013, I walked into a Fortune 100 company and declared that business intelligence was going to die. I'd just sold my BI company to Cisco in 2007, and even I could see the writing on the wall. Thirteen years later, I was right about the death, but wrong about the funeral. BI didn't disappear. It was absorbed into something far more powerful.
What We Said in 2013
The original argument was pretty direct. I told this anecdote about installing a "sweet new Web 2.0-y" BI application at a large customer. The next day, a director walked in and asked, "So, what can it tell me?" My answer: "What do you want to know?" And that exchange captured everything wrong with traditional BI. You had to know the question before you could get an answer.
We pointed to Gartner Research, who at the time noted that 4 out of 5 years, CIOs had labeled data strategies as their most important development area. The analytics market was already splitting into two camps: old-school "enterprise BI platforms" (bought by IT, deployed by consultants, focused on reporting) and the new "data discovery platforms" (bought by business users, focused on analysis and visualization). Companies like Tableau were leading that second wave. Gartner called the gap between these two segments "a chasm that continues to deepen."
We were bullish on data discovery. We believed BI's inability to handle Big Data, combined with its complex interfaces that required training, would be its undoing. The new audience for data tools wasn't technical. They were business users who wanted answers without writing SQL.
How It Actually Played Out
We nailed the broad strokes but missed the specifics of how it would unfold. Gartner did split their Business Intelligence Magic Quadrant into two separate reports in 2016, validating exactly what we predicted. Tableau went from a scrappy data discovery tool to a $15.7 billion Salesforce acquisition in 2019. The data discovery category won.
But the story didn't end there. By 2020, the conversation had shifted from "visualize your data" to "let the machine find the patterns." ThoughtSpot introduced natural language search for analytics. Microsoft embedded AI copilots directly into Power BI. And by 2024, products like Databricks' LakehouseIQ and Snowflake's Cortex were letting business users ask questions in plain English and get answers from enterprise data warehouses. No dashboards required.
The question we asked in 2013 was "what do you want to know?" The question AI answers in 2026 is "here's what you should know right now."
Today in 2026, the BI market has consolidated around AI-native platforms. The traditional players who didn't adapt (many of those "megavendors" Gartner identified back in 2012) either acquired their way into relevance or faded. IBM sold off Cognos capabilities. SAP bolted AI onto BusinessObjects. Meanwhile, the real innovation happened at companies that were never in the old BI quadrant at all. Palantir's AIP platform hit $2.8 billion in revenue in 2025 by treating analytics not as reports but as operational decision-making. That's the real shift.
The question I couldn't answer for that director back at Cisco's customer site in 2007 has finally been solved, but not by better dashboards. It's been solved by systems that proactively surface what matters. The user doesn't need to know the question anymore. The AI finds the anomalies, the trends, the risks, and puts them in front of the right person at the right time.
From BI to Operational AI
This evolution from reactive BI to proactive AI is exactly what we mean by Operational AI. It's not enough to visualize data or even to discover patterns. The real value comes when AI systems are embedded directly into business operations, making decisions and surfacing insights without waiting for someone to ask.
The old BI vs. data discovery distinction we drew in 2013 seems quaint now. Both categories assumed a human in the loop, staring at a screen, interpreting charts. The next stage of enterprise data strategy removes that bottleneck entirely. When your supply chain AI automatically adjusts procurement based on demand signals, or your customer success platform identifies churn risk before a human could spot it, that's operational AI at work. It's where the BI market was always heading. We just didn't have the language for it yet.