Your Data Strategy is Your Intelligence Strategy

by Eli Moore

Most organizations are not suffering from a lack of data. They are suffering from a lack of connected intelligence.

In the summer of 2023, I was leading the team that created a connected data intelligence system at The Coca-Cola Company. It was an incredible business intelligence tool allowing users to access and analyze multiple sources of structured and unstructured data, used by thousands of people every month. A feat many organizations still struggle to deliver in 2026. However, while it gave users access to data, it couldn’t tell them how to interpret the data, which data to look at, or what to do with it.

So, that summer, we built an AI system on top. It could query data, ask questions, and it knew our acronyms. It could say “I don’t know, let me ask someone,” and it could learn from the humans it reached out to for help. It could talk to you on Microsoft Teams. Essentially, it was a multi-agent system designed to connect business problems and human intelligence. This was 2023.

It’s an example of what good AI looks like: great data foundation, understanding of business context, and designed to integrate with humans, not replace them.

As I describe what a great data strategy looks like below, know that it comes from first-hand experience. It isn’t just an overhyped story of synthesizing unstructured data, but creating advanced systems that allow AI to connect to structured data too. It comes from experience driving usage and adoption among 5,000 people annually. It comes from seeing dozens of other companies succeed, fail, and land somewhere in the middle during their AI transformation journey as the head of Data Strategy at Burke.

Your Data Strategy is an Intelligence Strategy

This is the key. Research lives in one system. Customer feedback lives in another. Analytics sit across disconnected dashboards, teams, vendors, and repositories. Everyone has information, but very few organizations can turn it into fast, trusted decision-making at scale.

That gap is a competitive risk, and the gap is one that could have been solved before AI.

As AI accelerates across the enterprise, the companies that move forward confidently will not be the ones with the most models or the largest technology investments. They will be the ones that can connect data, expertise, context, and human understanding into a system that actually improves decisions. They are not the ones who ask, “Can AI fix my data?” They are the ones who ask, “How do I make my data AI ready?”

That is why data strategy is no longer just an IT initiative—it’s an intelligence strategy. Increasingly, it will define how organizations learn, adapt, innovate, and compete.

AI Readiness Starts Before AI

Many organizations are approaching AI backwards. They start by evaluating tools, experimenting with copilots, or launching isolated pilots before addressing the underlying intelligence infrastructure required to make those systems effective.

It’s like buying a Ferrari with the engine of a lawnmower.

Without connected, trusted, usable information, AI simply accelerates fragmentation. It scales confusion faster, and it does it with the extreme confidence of modern AI.

However, organizations that are making meaningful progress are approaching the challenge differently. They are focusing first on how knowledge moves through the business, how decisions get made, and where friction slows understanding down. That shift requires moving faster—but also thinking more intentionally.

Step 1: Stop Waiting for Perfect Certainty

Organizations often spend months discussing innovation before they begin learning from it. By the time a three-month pilot finally launches, the organization is already six months behind in learning, adaptation, and internal capability building.

The future favors bold moves, but bold moves without judgment create risk.

The goal is not reckless experimentation. Rather, it is a systematic way of thinking focused on creating AI-ready data and transforming the way you work: fast learning cycles grounded in clear business questions, expert oversight, and measurable outcomes. Innovation is not a single transformation event. It is an operational capability akin to evolution. The organizations strategically positioned for adaptability are moving ahead faster than the organizations waiting for clarity.

Step 2: Context Drives Intelligence

AI systems are only as effective as the context surrounding them. Better prompts help, but the larger issue is organizational understanding:

• What does the business know?
• What does it trust?
• What can it access quickly?
• What context exists outside formal systems?
• Where does expertise actually live?

Importantly, it requires designing agentic systems that can abstain from answering when there is inadequate evidence or ambiguity. It requires designing systems that can say, “Let me go ask a human for help.” It requires that system to actually go learn from humans.

Furthermore, strong AI outcomes require aligned thinking before execution. They require organizations to bring together business leaders, researchers, strategists, analysts, and domain experts to define the problems worth solving and the criteria that define success. Because there is no universally “correct” decision, there are only decisions made within different business realities, constraints, risks, and priorities.

The companies gaining advantage from AI are not removing humans from decisions. They are building systems that help experts make stronger decisions faster, and that distinction matters.

Step 3: Start With Data, Not AI

The organizations turning AI ambition into real business advantage are not starting with AI. They are starting with strong data products and the idea of connected data intelligence.

Data is the raw material of organizational understanding. Better data creates better intelligence, better intelligence creates stronger decisions, and stronger decisions create growth. However, most organizations still operate with fragmented insight ecosystems that make even simple questions difficult to answer.

A business leader asks a straightforward question, and suddenly multiple teams are involved, conflicting reports emerge, definitions vary, and the answer becomes a manually assembled PowerPoint instead of a scalable intelligence system. That is not an analytics problem—it’s an operating model problem.

The solution isn’t “put everything in the data lake.” In fact, the world of AI often doesn’t require the data to be in your environment. Data companies who structure their data properly, build in governance, semantics, and business logic are able to deliver a product that is ready for AI. It’s about having the data structured properly, then connecting it to your AI systems.

The Real Problem Is Fragmentation

Organizations today have more information than ever before. Yet teams still struggle to answer the same fundamental questions:

• What is happening?
• Why is it happening?
• What should we do next?
• Which direction creates the best outcome?

The issue is rarely missing data. The issue is disconnected intelligence. AI helps here through synthesis, but synthesis isn’t sufficient.

People have to make choices—choices that come with consequences. In business, these consequences require putting skin in the game, something AI lacks. What AI doesn’t lack is the ability to leverage data, but data can be difficult to find, trust, connect, and operationalize. Insights live across dashboards, transcripts, reports, trackers, spreadsheets, presentations, vendor platforms, and institutional memory trapped inside people’s heads. Every layer of intelligence inherits the strengths and weaknesses of the systems underneath it. That becomes especially dangerous with AI, because AI is very good at scaling patterns. If the organization feeds fragmented knowledge, inconsistent logic, weak governance, or unclear thinking into the system, AI will scale those weaknesses too.

Faster technology does not automatically create better decisions, but connected intelligence helps.

Start With Vision, Not Platforms

Many organizations approach modernization by buying tools before defining outcomes, which usually creates more complexity, not more clarity. A real intelligence strategy starts somewhere more fundamental:

• What should decision-making look like in the future?
• What should be easier than it is today?
• What should teams understand faster?
• What friction should disappear?
• What knowledge should become reusable across the enterprise?

The strongest data strategies are not technology-first. They are human-centered operating models designed around how people access information, interpret context, collaborate, and act. They require understanding where teams lose time, where trust breaks down, where duplicate work happens, and where insight fails to translate into action. These friction points are not side observations, but rather they are the blueprint for transformation.

Not All Data Is Equal

One of the biggest mistakes organizations make is treating all data as if it functions the same way. It does not, and AI interacts with different forms of data very differently.