TL;DR: Data product reusability accelerates AI outcomes, reduces costs, and increases trust. Yet most organizations are still rebuilding data products they already have, while drastically underestimating the costs. Learn what’s driving this cycle and how to break it with three key changes that increase data product reuse.
When was the last time someone on your team built something from scratch (like a pipeline, report, or data set), and you found out later it already existed in your environment?
I hear this story all the time when I’m out talking to customers and speaking at events. And I can tell you, rebuilding what already exists is one of the most expensive problems in enterprise data right now, but it’s also preventable.
The trick is prioritizing reusable data products, which starts with understanding the difference between a data product and just another data set. And the difference comes down to one thing: purpose.
A real data product isn’t just another table in your catalog. It’s a governed, trusted data asset that’s directly tied to a business outcome. It’s also complete with the metadata, quality signals, lineage, and business definitions to back it up. And when organizations treat their data that way, the economics change completely.
The chaos and costs of rebuilding data products
When different teams continually rebuild similar data products, they:
- Waste hundreds of thousands of dollars on duplicate work each year
- Add to an already cluttered environment, making it even harder for data users to find what they need
- Erode trust a little more with every inconsistency this creates
The numbers back me up here. McKinsey reports data users waste up to 40% of their time searching for data when they lack a clear inventory. Which is why it’s not surprising that Forrester reports up to 73% of enterprise data goes unused for analytics. That’s clearly a data discovery and reuse problem.
New 2026 Omdia research, commissioned by Quest Software, makes the cost of this cycle concrete. Surveying more than 250 North American enterprise data leaders, the study found that the typical data delivery project takes more than four months to deliver first insights and requires more than 800 person-hours of effort. And here’s the part that really stings: 38% of total data team effort is spent on non-reusable, one-off work. Nearly 70% of projects require significant rework after delivery, often stretching timelines by 30% or more. That’s a staggering amount of effort being poured into work that doesn’t deliver compounding value.
I’ve been talking about this a lot lately, including in our recent webinar with DBTA, “Stop rebuilding data products you already have.” The cool thing about this, though, is the quantifiable value I’m seeing organizations gain when they shift from rebuilding to reusing data products.
When I think about what reusability actually means for an organization, I always frame it in two ways: the savings story and the AI acceleration story. The more formalized and productized your data product is, the more it can be reused, and those savings are real and concrete.
But right now, we’re also in an age of fierce AI competition. And unlike previous waves of technology (cloud, IoT, big data), AI is different. For the first time, the conversation about value and outcomes is happening before the investment, not as an afterthought.
That changes everything. But before you can capture that value, you have to understand what’s getting in the way, so you can fix it.
Why everyone’s rebuilding data products
Nobody wants to rebuild data products. Yet teams keep falling into the same traps, and I’ve seen all of them.
You search for something you think you have but can’t find it. So, you build. You find something close, but you’re not sure you can trust it. So, you build. A stakeholder needs an answer fast and searching feels like too much of a delay. So, you build.
That’s why I tell customers: when you’re under pressure and can’t find what you need, rebuilding feels rational. The problem is, when it becomes the default, you end up spending more time validating data than using data to move your business forward.
And trust me on this one. I don’t think there’s a single data professional on the planet who hasn’t looked at a BI report, noticed something that didn’t match up with another number they’d seen, and immediately thought: I’m never using this report again.
That’s the trust gap in action. Once that sets in, your AI initiatives don’t really get killed so much as they just go into purgatory. So, they still exist, but nobody’s using them, which benefits no one.
Then you’ve got your structural gap, which comes from traditional catalogs being passive. Someone has to remember to go check them before they start building. But when the pressure’s on to deliver quickly, that check won’t happen.
What changes when you shift to reusable data products
When you build something once and reuse it, you dramatically shift the economics. The first build carries that heavy lift, but every reuse gets cheaper, faster, easier, and more trustworthy. By the fifth or tenth reuse, the economic advantage is impossible to ignore.
And the Omdia research confirms what I’ve seen firsthand. Among enterprises that have made this shift to proper data products, nearly two-thirds report KPI improvements of 20% or more, 75% have achieved faster time to insight, and 71% have increased analyst productivity.
That’s why I tell teams to reframe their thinking. Instead of asking, “How fast can we build this?” ask, “How many times can we reuse this?”
A data product that serves five teams is exponentially more valuable than five separate data sets built in isolation. As I always say, the best ability is availability, but reusability is right there with it.
And one thing I’ve seen that really changes behavior is when you can simplify discovery. When teams can use a natural language prompt to find a new data product and the system immediately surfaces an 87% match that already exists in their environment? That drives reuse. You have to make it faster and easier to stop defaulting to build. That structural change makes a huge difference.
Another mindset shift that increases data product reusability relates to AI. You need to think of AI as just another persona consuming your data products. The same requirements apply as with your human users. It needs to be able to find the data, trust it, understand it, and apply it.
When your data lives in silos or gets rebuilt for every use case, your AI outputs reflect that inconsistency. But when your data products are trustworthy, discoverable, and accessible? That’s when AI starts delivering better outcomes.
The Omdia research puts a number on this too: more than 40% of enterprises report a 15% or greater improvement in business impact just from automating key steps in data product delivery. Imagine what full reusability unlocks.
How successful organizations are getting data product reusability right
To make data product reuse stick, you have to design for it. The organizations I see having success build environments where reuse feels like the natural path from the beginning. Like I said before, reuse has to feel faster and easier than starting from scratch.
That’s why I suggest focusing on three key areas that consistently increase reuse:
Simple, plain-language discovery across the entire data environment. This is the single most critical factor for preventing duplicate work. When teams can quickly find what already exists, they stop rebuilding. Semantic search, which surfaces results using business terms and not just technical names, is a game changer here.
Trust built into the product itself. Lineage, quality scores, and clear ownership help teams feel confident reusing what’s already there. Trust scoring that’s customizable based on your organization’s risk tolerance is also key. Not every domain needs the same level of scrutiny, and that flexibility matters.
Governance at inception. Traditional governance processes can’t operate at the speed and scale AI demands. When governance is woven into the workflow rather than layered on top as a separate approval gate, reuse becomes faster than rebuilding. The goal is governance as an enabler instead of a blocker.
When these elements are in place, reuse increases naturally. And the benefits extend directly to your AI initiatives. Consistent, trusted, reusable data products provide the foundation your AI models need. Feed them well and they’ll deliver outcomes you can stake your reputation on.
Building data product reusability into your data and AI foundation
Here’s my practical advice for getting started: look at your backlog and ask, “Where is the same request appearing more than once?” Take one of those patterns and build it into a reusable data product. Then do it again. And again.
I’ve seen teams move from constant rebuild cycles to a culture of reuse in a matter of months by focusing on patterns rather than projects. It’s a total snowball effect. Once you build that momentum, you can scale faster than you’d expect. And it’ll give you a competitive advantage because not everyone’s figured it out yet.
The blueprint for data product reusability isn’t complicated. It requires simplified discovery, built-in trust, and embedded governance. Those are the pillars that replace all the inefficient and costly rebuilding.
You already have the data, talent, and use cases you need. Getting value from those resources requires data product reusability. And when you enable reuse, every data product delivers compounding value, driving faster decisions, stronger AI outcomes, and measurable business growth.
