The numbers are hard to ignore. Eighty-seven percent of organizations are investing in data and analytics. Seventy-four percent are struggling to achieve and scale real value from it. The average Chief Data Officer lasts just 2.5 years in the role before moving on.
Something is structurally wrong — and according to Nysa Zaran Evagelinos, Research Director at Info-Tech Research Group, the problem starts with how organizations think about data products.
"Why, given that we have so much data, given that we all do things with this data, is it always so challenging?" she asked, opening the final day keynote at Info-Tech LIVE 2026 in Las Vegas. "It's not a niche discipline. There's so much advice out there. Where do we keep falling short?"
The Data Product Problem
The current industry answer to that question is the data product — owned, trusted, reusable, discoverable. Vendors and analysts have been promoting the concept as the path to data-driven value for several years now.
But the results tell a different story. According to a 2026 study of data engineering organizations, 70% of data product initiatives stall after 12 to 18 months.
The reason, Zaran argued, is that most data products solve the wrong problem. They deliver real value — but only within the CIO and CDO portfolio. Faster time-to-incident-detection. Better time-to-resolution. Reduced data downtime. Quicker analytics deployment. These are efficiency gains for the data team, not enterprise value drivers.
"The results are limited to running a more efficient shop," she said. "But the mission is to drive broader transformation — shift the culture, make a difference to the organization. Not just run a more efficient pipeline."
The Data Value Chain
The framework Zaran introduced reframes the problem entirely. Instead of starting with data, she starts with value — and works backward.
The Data Value Chain runs from left to right: Data → Insight → Decision → Action → Value. AI maps directly onto this chain, perceiving and learning on the left side, reasoning and planning in the middle, creating and acting on the right.
Most organizations have invested heavily in the left side of that chain. They've built data infrastructure, established pipelines, created dashboards, and produced insights. What they've largely failed to do is own the right side — the leap from Decision to Action, and from Action to measurable Value. That leap, Zaran noted, is where data culture either exists or it doesn't.
"The business is left to own the rest of the chain," she said. "And the gap to value remains."
Four Companies That Closed the Gap
To illustrate what it looks like to own the full chain, Zaran walked through four case studies — each representing a different entry point on the value chain.
Bloomberg Terminal anchored the left side. With 10 billion data points published daily across 8,000 curated datasets and 40,000 data fields, Bloomberg's product goal was straightforward: connect decision-makers to exceptional data they couldn't get anywhere else. The result is roughly $15 billion in annual revenue.
Netflix moved further right. Rather than selling data, Netflix built decision intelligence — a feedback loop that uses viewing behavior to drive personalization, content strategy, and a low-friction experience. The system continuously collects data, predicts preferences, serves content, and refines itself. Revenue: $45.18 billion, with $3.4 billion invested annually in technology and development.
UPS ORION pushed into action. The route optimization system — a copilot for drivers and logistics planners — took 10 years and $250 million to build. The payoff: $300 to $400 million in annual savings and 100,000 tonnes of carbon emissions avoided. The hardest part wasn't the algorithm. It was getting drivers to trust it enough to follow it.
Waymo went all the way to autonomy. Five hundred thousand rides per week, 200 million miles logged, operating in 10 US cities. The data product isn't a dashboard or a recommendation engine — it's a system that perceives, decides, and acts without human intervention. Governance, Zaran noted, means running billions of simulations so the system never encounters a scenario it hasn't already seen.
"Each of these companies figured out a different way to use the data value chain," she said. "They each stopped at a different point — and in doing so, they each had different problems to solve."
The Winning Pattern
What separates these companies from the 74% struggling to scale isn't better data infrastructure. It's where they started.
Most organizations start on the left: collect data, build pipelines, create products, and hope value follows. The winning pattern does the opposite — start on the right and backtrack.
Four questions drive the approach: What outcomes does the organization actually care about? What actions and decisions drive those outcomes? Can data move the needle on those decisions? And do we have the data, or can we get it?
"A data product is almost a byproduct," Zaran said. "It's a byproduct of the discipline of running an outcome end-to-end using data. You measure it by its purpose. You measure it by the downstream change it creates."
The implication for AI is direct. Organizations racing to deploy AI tools, agents, and copilots face the same trap that data products fell into — investing heavily in capabilities without anchoring them to a specific outcome the organization cares about.
"This is how AI becomes a tool," Zaran said. "Not your destination."
