Your Data Strategy Isn't Failing—Your Ownership Model Is4 Min Read

In a recent Forbes Business Council article, Cory McNeley shares his perspective on how organizations often mistake data issues for software issues. In this article, Cory breaks down the costs of data ownership issues and provides steps to solve them.

Most companies that struggle with data assume the problem is their tools. They buy better software, a newer platform, more analytics capability or some combination of the above. But the problems often persist, so they buy something else, and the cycle repeats.​

The actual issue is usually simpler and more human: Nobody is clearly responsible for the data. Different departments collect it, store it and use it in inconsistent ways. There's no agreed-upon answer to basic questions like: Whose job is it to make sure this data is accurate? Who decides how customer data gets used across divisions? The business assumes IT owns it, but IT assumes the business owns it. Accountability is lost. And when no one owns the data, better technology just makes bad data more visible.​

Would any serious company let five departments maintain their own separate financial records with no reconciliation process and then call it a “finance strategy problem”? Of course not, but that's essentially what many organizations are doing with their data right now.​

The Costs Of Broken Ownership

The consequences show up in ways that can be easy to misdiagnose, including duplicate records and conflicting reports. Decisions are made on numbers that don't reflect reality, and data sits unused because no one trusts it enough to act on it.​

I remember one client who couldn't figure out how to allocate their workforce effectively. Their data showed certain employees as severely overworked while others seemed underutilized. But what the data showed and what people were actually doing were completely different. When leadership made staffing adjustments based on those numbers, it created chaos, and the organization effectively shut down while it was all sorted out.​

When teams can't agree on what the data says, they can't agree on what to do, and that indecision has a real cost. Your late decision is often one made for you by a competitor who moved faster. ​

Building An Ownership Model That Works

The solution is often a leadership and governance issue, not a technology fix. The first step is identifying a data steward. This is a person who understands both the business and the data coming out of your production systems, as well as who has the authority and visibility to enforce standards and resolve issues. It might be a part-time role in a smaller organization or a full-time position in a more complex one. Either way, the ideal profile is someone with a business background and enough technical fluency to bridge both worlds.​

The second step is categorizing what you have, whether that’s customer data, operational data or financial data. These often get mixed together, and untangling them clarifies who should be accountable for what. Some data categories also need access restrictions, which can only be managed if someone has mapped what exists in the first place. It’s also important to make data quality visible. The more people who can see the data and flag problems, the faster deficiencies get caught. This means creating channels for feedback and actually listening when someone says a report isn't useful. ​

Next, cut what you don't use. If data hasn't informed a decision in two years, move it out of your production environment. I once audited an organization that was generating nearly 300 reports every Monday. As a test, I replaced one of them with a note that said: "The first person who actually reads this wins a $100 prize." Almost a year later, no one had claimed it. Legacy processes accumulate, and just because something has always been done a certain way doesn't mean it needs to be done that way forever.​

Data is becoming a core commodity inside every business. The organizations that get the most value from it are clear about what they have and position someone to be clearly accountable for its accuracy. Before your organization looks at another analytics platform or AI initiative, ask whether you actually have a technology problem. Is the data unreliable because of the tools or because no one has been assigned to own it? The answer will tell you more than any vendor demo.​