TL;DR: Resisting change in data management is not a neutral position. The cost compounds over time in lost competitive advantage, not just lost efficiency. Organizations that delay building governed, reusable data products are not making a safe choice. They are making a slow one.
In a recent episode of the Data Enablers podcast, I made the case that most organizations already have the data they need. What they lack is data that was designed to be used. If you have not read the first part of this discussion, it covers what mature data products require and how AI is lowering the barrier to building them.
This post picks up where that one leaves off, addressing the question I hear most often from data leaders who already understand the argument but have not yet moved: what does it actually cost to wait?
The answer is more than most organizations realize, and the evidence tends to arrive too late.
“Companies that fail to evolve their core capabilities when the technology around them changes do not stay competitive; they become cautionary tales.”
Why is the cost of inaction in data management higher than most organizations realize?
Every organization I work with understands, at some level, that the old way of working with data is not sustainable. And yet change is hard. The existing processes are known quantities. The teams have built workflows around them. Asking people to adopt a new approach requires champions, communication, and a compelling case built around outcomes rather than features.
The organizations that resist this change are not making a safe choice. They are making a slow one. We have seen this pattern before. Companies that fail to evolve their core capabilities when the technology around them changes do not stay competitive; they become cautionary tales.
“Resisting change in data management is not a neutral position. The cost compounds in lost competitive advantage, not just lost efficiency.”
When fragmentation becomes a competitive liability
A consumer goods company I worked with had delayed investing in proper data governance and reusability for several years. Their data estate had grown organically, with each business unit maintaining its own reports, definitions, and spreadsheets.
When cheaper imports from Asia entered their category, the impact was immediate: revenue dropped across several product lines. This company, however, had no unified, trusted view of demand signals, cost trends, or margin exposure. Each team was working from its own version of the truth, and by the time they reconciled the numbers, the market had already moved on.
The leadership team described the experience as being “caught off balance by information they should have seen coming.” The issue was not a lack of data. It was fragmentation: inconsistent definitions, duplicated effort, and no reusability across teams, which made rapid decision-making impossible.
By the time they reacted, they had already lost ground in several key markets. They are now investing heavily in modern governance and reusable data products, but they are doing so from a position of catch-up.
The data and AI landscape is moving quickly enough that the cost of staying still is no longer feasible. It shows up in how fast competitors move and how slowly your own organization can respond.
Resisting change in data management is not a neutral position. The cost compounds in lost competitive advantage, not just lost efficiency.
“The clearest measure of a data product strategy is not how many products are in the catalog. It is how many people across the organization can act on trusted data without filing a ticket or waiting for an engineer.”
How do you measure whether your data empowerment strategy is delivering results?
The good news is that the transition does not have to be disruptive. A platform approach that supports the end-to-end data product lifecycle, from modeling through governance to publishing and reuse, allows organizations to build toward the new model incrementally. You do not need to throw away what you have built. You need a foundation that lets you build faster and with more confidence than before.
Glenda O’Keefe, Field CTO at Quest Software, on how a platform approach removes the barriers to building data products at speed.
The clearest measure of a data product strategy is not how many products are in the catalog. It is how many people across the organization can act on trusted data without filing a ticket or waiting for an engineer. The measure I use when I talk to data leaders about success is that ratio of empowered users to total users. When it is low, the strategy is not working, regardless of what the catalog count says.
In practice, the organizations that score well on that ratio share a common characteristic: their business users do not think of data as something they request. They think of it as something they navigate. They have access to a governed marketplace of reusable data products, they can assess quality and ownership before they consume, and they can build on what others have already validated rather than starting from scratch.
The organizations that score poorly tend to have the opposite dynamic: a data team fielding requests, a backlog that grows faster than it is cleared, and business users who have quietly given up and gone back to their spreadsheets. That gap is not a technology problem. It is a governance and design problem, and it is exactly what a mature data product strategy exists to close.
When business users can describe a use case and receive a governed, reusable data product in response, that is when the shift from data access to data empowerment becomes real. That is the goal. Platforms that support it bring together the full lifecycle: modeling, governance, creation, and a governed marketplace where products can be discovered, shared, and reused across teams rather than rebuilt from scratch.
With that combination in place, it is more achievable today than at any point in the 20 years I have been working in this space.
This post expands upon my conversation with Sarit Bose of Edgematics on the Data Enablers podcast.
