According to ZDNet, Salesforce’s latest State of Data and Analytics Report reveals a massive corporate identity crisis around data. A staggering 63% of business leaders now call their organizations “very data-driven,” up from 53% just last year, yet nearly two-thirds of technical leaders admit their companies struggle to actually drive business priorities with data. The research surveyed over 7,600 leaders worldwide and found that 90% of business leaders believe their careers depend on being data-fluent, while 86% think their careers hinge on being data-driven. Here’s the kicker: data and analytics leaders estimate that 26% of their organizations’ data is “untrustworthy,” and 42% of business leaders say their data strategies don’t align with business objectives at all. Meanwhile, 84% of CIOs believe AI will be as significant to their businesses as the internet, creating enormous pressure to transform into what Salesforce calls “agentic enterprises.”
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<h2 id="data-confidence-crisis”>The data confidence crisis
Here’s the brutal reality: companies are drowning in data but starving for insights. The average enterprise uses 897 applications, but only 29% are actually connected. More than half of leaders aren’t confident they can even access the data they need, and data volumes are growing at 30% annually. Think about that for a second – we’re creating more data than ever, but we can’t find the good stuff. The real problem? Between 80-90% of enterprise data is unstructured, and 70% of data leaders believe their most valuable insights are trapped in that unstructured mess. It’s like having a library where all the best books are written in invisible ink.
AI as the forcing function
AI is basically holding up a giant mirror to companies’ data problems. Ninety-three percent of organizations already have AI in their tech stacks, and 91% of business leaders believe AI makes being data-driven more important than ever. But 84% of data leaders acknowledge the fundamental truth: AI outputs are only as good as the data inputs. That’s why CIOs are spending four times more on data infrastructure than on AI itself. They’re realizing you can’t build a skyscraper on quicksand. The rush to adopt AI agents that can understand and act on natural language is forcing companies to finally confront data quality issues they’ve ignored for years.
The real-time reality check
What’s really fascinating is how priorities are shifting. Real-time data access has suddenly become the top data challenge, surpassing even security threats and data quality concerns. Why? Because AI agents need current information to make relevant decisions. But 49% of data leaders say their companies occasionally or frequently draw wrong conclusions from data that misses business context. So we’re not just talking about bad data – we’re talking about data that’s disconnected from reality. The full report shows that 93% of business leaders agree insights are only relevant if they’re grounded in business context, which explains why so many AI projects deliver technically correct but practically useless results.
The conversational data future
Here’s where things get really interesting. Ninety-three percent of business leaders say they’d perform better if they could just ask data questions in natural language. That’s the promise of agentic analytics – having actual conversations with your data platform instead of wrestling with technical queries. But 63% of data leaders admit that translating business questions into technical queries is prone to error. So we’re stuck in this awkward phase where everyone wants to chat with their data, but the data isn’t ready for polite conversation. The companies that figure this out first will have a massive advantage. Everyone else? They’ll keep drowning in data while wondering why their fancy AI agents keep giving them garbage answers.
