Every organization drowns in data. Few know how to make it valuable. The real power of data lies in context. In my experience, success comes from mastering three types of context: the data itself, its origin, and its intended use.

Data Itself

Not all data is created equal. Before using data to drive decisions or power AI models, ask: What does this represent? How is it defined? Do stakeholders share that definition? Is it accurate enough? Success requires clarity on definition, quality, and completeness.

This is where metadata becomes critical. Metadata, information about the data itself, provides the context that transforms raw numbers into trustworthy assets. Without it, even accurate data gets misinterpreted. With it, everyone speaks the same language, enabling faster, smarter decisions.

I've seen this play out in retail, where packaging dimensions became a critical bottleneck in the supply chain operations. Different countries had varying interpretations of what constituted the height, width, and depth of product packaging. Believing they were correcting errors, each adjusted the measurements according to its own standards. This resulted in conflicting data, costly disruptions, and out of stocks that impacted customer satisfaction. The fix wasn't technical, it was getting everyone to agree on what 'width' meant.

Intended use

The trap isn't using low-quality data, it's using the wrong quality data for your purpose.

A proof-of-concept may tolerate lower data quality, while mission-critical projects often demand rigorous standards.

When data isn't available or quality falls short leaders face a clear choice: either pause until the data is ready or adapt the business case to fit the current state. Too often, organizations press forward with unsuitable data, setting themselves up for failure. This underscores that data is fundamentally a business asset. Its quality, relevance, and use are matters of business authority and accountability, not issues to be delegated solely to supporting functions like a data team or IT.

Data Origin

Understanding how and where data is collected is vital. The source, whether a customer, employee, or third-party, defines the legal, ethical, and contractual boundaries of its use. Leaders must ensure transparency on data processing and provide clear choices to stakeholders. But the challenge doesn't stop with being compliant.

Misaligned expectations across the data lifecycle can quietly undermine projects. Consider the following real world delivery operations example: Analysis revealed nonsensical timestamps from driver devices. After extensive investigation, the issue was traced to inconsistent time settings across devices, a problem the supplier previously dismissed because the data held no value in their context.

Clear expectations with suppliers about data requirements prevent misalignment and ensure the data you receive is fit-for-purpose.

Leading with Context

These three contexts don't operate in isolation. They're interconnected. Clear definitions mean nothing without understanding its origin. Legal rights are meaningless if you can't trust the data. And the highest-quality data is worthless without business purpose.

Treat data quality as a business decision, not a technical one. Align expectations with objectives and ensure shared understanding of what the data represents.

Determine if near-perfect data is worth the effort, and when good enough is sufficient, to avoid over-engineering. Focusing resources where they matter most.

Establish clear legal and contractual foundations. Verify you have the right to use data for its intended purpose, secure agreements with partners that guarantee data availability and quality with build in safeguards against vendor lock-in that could threaten access to critical assets.

Master these contexts with the leadership discipline to act on them, and you'll make better decisions faster, reduce costly mistakes, and unlock the strategic advantage data can deliver.