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Taylor Culver
May 2025
Many organizations still lack a clear, actionable data strategy in a world flooded with tools, dashboards, and AI hype. This article explores why a data strategy is essential for IT and driving measurable business value and explains how to operationalize it in real-world terms. You’ll learn how to align data work with strategic goals, build cross-functional ownership, and avoid the common pitfalls of strategies that look good on paper but go nowhere in practice.
In today’s economy, data is the most underleveraged asset. This likely causes more waste than value, and while most organizations know this, very few have a plan to change it.
They might hire a few data scientists, stand up a data lake, or buy a dashboard tool—but those are tactics, not strategy. A proper data strategy answers a much more fundamental question:
How will data drive business value in our organization over the next 12–24 months?
If that question feels difficult to answer, you’re not alone. Many companies are stuck somewhere between executive urgency (“We need AI now”) and operational chaos (“Our data is a mess”). They sense the opportunity but lack a structured approach.
This post unpacks why a data strategy is essential, how to build one that aligns with your business, and—critically—how to operationalize it so it doesn’t just live in PowerPoint purgatory.
A data strategy is a cross-functional plan that defines:
What business outcomes data will support
Which data capabilities are required to achieve those outcomes
How the organization will prioritize, invest in, and govern those capabilities over time
It’s not just an IT roadmap or a governance policy. It’s a business capability playbook.
A good strategy will include:
Business use cases: Clear articulation of where data can improve revenue, reduce cost, manage risk, or unlock new experiences.
Data capabilities: What tech, talent, and processes are required (e.g., data engineering, stewardship, AI readiness)?
Governance model: How decisions will be made, by whom, and at what level of risk tolerance.
Operating model: How teams will work together to make data usable, trusted, and actionable.
You’re already investing in data...without clear ROI
If you spend on tools, talent, or infrastructure without a unifying plan, you're likely duplicating efforts, chasing noise, or building things no one uses.
Executives are asking about AI...but your data isn’t ready
You can’t unlock AI value without usable, trustworthy, and accessible data. A strategy defines what “ready” looks like—and how to get there.
Your teams don’t speak the same language.
Marketing talks about segments, finance talks about profitability, and IT talks about pipelines. A strategy creates a shared framework that connects business goals to data definitions.
You need to prioritize
There are always more data problems than resources. A strategy helps you invest in what matters most and avoid distractions.
The board is watching.
Increasingly, boards expect data and AI to be core to the company’s operating model. A credible strategy builds confidence and alignment.
Let’s be honest: most data strategies don’t fail in design—they fail in execution. Here’s how to avoid that.
Don’t start with tech. Start with what the business needs to do:
Improve customer retention by 10%
Reduce manual reconciliation by 50%
Increase cross-sell conversion
Anchor every data initiative to a business priority and track progress in terms the business understands.
Data is a team sport. The CFO owns data. So does the CMO. And Ops. And Legal. Your strategy must reflect that:
Shared accountability
Clear owners for use cases, not just systems
Transparent decision rights
A strategy isn’t a static plan—it’s an execution system. Define:
Quarterly planning cycles for data priorities
Steering committees or data councils
Feedback loops from users back into the roadmap
Think agile, not waterfall.
The best tools in the world won’t matter if people don’t understand how to use them. That means:
Training business teams on data fluency
Equipping data teams with clear documentation
Coaching product and analytics leaders on influence and alignment
Don’t stop at data quality scores. Measure:
Adoption of key data assets
Reduction in time to decision
Growth of data-driven initiatives
Stakeholder satisfaction
These metrics build credibility and keep momentum.
You don’t need a perfect data strategy. You need one that is good enough to act on and clear enough to align people.
Done right, a data strategy becomes a unifying force across business and tech teams. It tells a story—not just about where your data is but also about where your business is going.
More importantly, it empowers people across your organization to use data to do novel things—faster, smarter, and more confidently.
And in a world increasingly shaped by uncertainty, clarity might be the most critical capability of all.
