Why do so many organizations struggle to unlock the value of their data? The promise is clear: better insights should lead to smarter decisions, more efficient operations, and stronger results. And yet, few companies seem to fully capitalize on that potential. We spoke with Joshua Touati, Manager Data & AI at Eraneos Netherlands, about why that gap persists—and what it takes to close it. Drawing from his experience across sectors, he shares key lessons learned (as well as 3 common pitfalls) from the practical implementation of data management in both retail and healthcare settings.
“What I often see is that people in organizations build quick fixes in their own bubble. It helps them move forward in the short term, but it’s a sign that something is fundamentally missing. That’s when I know: there’s value to be gained from data management.”
That field insight comes from Joshua Touati, Manager Data & AI at Eraneos Netherlands. Over the years, he has supported both public and private sector organizations in building and embedding data management practices. His approach is pragmatic and results-driven: structure where needed, flexibility where possible.
“You notice that teams spend a lot of time chasing information: who made this dashboard? What does this column mean? Is this data up to date? You see internal email chains, unnecessary meetings, delays in decision-making. And then you realize—it’s not that the data isn’t there. It’s that no one has been made responsible for making it accessible and trustworthy. A lot of value is being lost there.”
Data management done well
That, in short, is the promise of data management done well. It brings ownership, shared language, and consistency to a part of the organization that too often remains fragmented. But as Joshua points out, it only works if the business is involved from the start:
“The biggest mistake I see is when data management is solely an IT project. That never works. The business needs to own the data it creates and uses. If you don’t bring them in, they won’t change their behavior. They’re the ones entering data into systems and using it to make decisions—so if they’re not part of the process, you risk misalignment, errors, and resistance. In practice, this means involving department heads, team leads, and operational staff early on, so they understand the why behind the change and feel ownership over how it’s implemented.”
In this article, Joshua discusses two cases that illustrate both the challenges and the potential of implementing data management—from a hospital that wanted to future-proof its BI environment to a retail company struggling to unify data across borders. Along the way, he outlines the critical missteps to avoid and the ingredients for success.
Future-proofing BI in a hospital environment
“We were brought in as part of a broader data strategy effort, and data management quickly emerged as a critical element. Their BI landscape was outdated, slow, and definitely not future-proof. Just updating the dashboards with fresh data would take seven to eight hours every night. In just a year, processing would take even longer, so that by the time people came in the next morning, the reports might still be processing. Not a workable situation.”
That was the starting point for Joshua Touati and his team in a Dutch hospital environment. The situation was further complicated by a lack of clarity: no one quite knew what specific KPIs meant, who had built the dashboards, or whether the data being used was accurate or current.
“It sounds basic, but it’s incredibly common—there’s a dashboard showing the number 8, and no one knows what that 8 actually stands for. There’s no central glossary, no consistent definitions. People spend their time chasing the right person to ask, often without success. It’s a huge waste of time and it creates frustration.”
The first step was a maturity assessment, using the DAMA-DMBOK2 framework to evaluate the current state of data management across several domains. This laid the groundwork for identifying gaps, selecting priorities, and determining where to begin.
“We scored each area and then sat down with management to decide: where do we start? We didn’t just pick the lowest score—we looked at strategic alignment and where we could demonstrate value quickly. Quick wins are essential to gain traction. If you start with a big, complex project that only pays off nine months later, you lose people along the way.”
Together with hospital stakeholders, the team selected three focal areas: data governance, data architecture, and metadata management. That included defining ownership and responsibilities, designing future-proof data flows, and implementing a data catalog to centralize and standardize metadata.
“A data catalog makes it tangible. You can log ownership, set update frequencies, define access rights—it becomes clear who’s responsible for what. And just as importantly, it helps people trust what they’re seeing. That trust is the foundation for using data effectively.”
With clear ownership and a shared language, the hospital began to see faster decision-making, fewer redundant meetings, and a more confident use of dashboards. A strategic problem had become a practical solution.
3 common pitfalls in data management
According to Joshua Touati, this disconnect between business and IT is at the root of many failed data initiatives. When data ownership isn’t clearly assigned, and the people who work with the data daily aren’t part of the process, confusion and inefficiency follow.
- Not involving the business “One of the biggest mistakes I see is that data management becomes an IT party. The business isn’t involved, and then you’re building systems that no one ends up using—or worse, they keep working around them. The people entering data into systems are often unaware of how that data flows, what it’s used for, or how mistakes in entry impact downstream decisions. If they’re not part of the implementation, they won’t see the value in doing things differently.”
- Lack of alignment with organizational strategy Another common issue is poor alignment with organizational strategy. Teams sometimes invest time and resources into parts of data management that, while technically sound, have little relevance to the company’s actual goals. “It goes both ways—either you create a plan that looks great on paper but doesn’t work in practice, or you spend a lot of effort on improving something that isn’t a priority at all. Both lead to frustration and wasted time.”
- Over-scoping Lastly, Joshua highlights the danger of over-scoping: trying to fix everything at once. “If you make the scope too big, it becomes unmanageable. You don’t get quick wins, and without visible progress, support dries up. Focus is crucial.” These pitfalls are easy to fall into, but also avoidable. In the next example, Joshua shows how a very different kind of organization—one with lower maturity and little initial buy-in—was guided toward a more promising path.
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Gaining momentum in a fragmented retail landscape
“This organization had grown through a buy-and-build strategy, acquiring companies across Europe. But that meant dozens of systems to manage data, inconsistent field names, and no unified way of looking at performance. Their data quality was suffering, and so was their ability to steer the business.”
In contrast to the hospital case, where the data strategy was already part of a larger transformation agenda, the retail example Joshua Touati shares is one of lower maturity and limited initial buy-in. The technical problems were visible, but the strategic importance of data management hadn’t landed yet.
“Management saw what needed to happen, but the executive team didn’t. They didn’t realize how much time was being lost or how much insight they were missing. Our job was to help build a case that made the problem—and the potential—visible.”
Instead of starting with a full-blown assessment or a broad roadmap, Joshua and his team worked with management to identify pain points that the business could feel directly. This took the form of a collaborative workshop to surface and prioritize data challenges.
“We collected ten key issues and ranked them by impact. Which ones are costing money? Which ones are frustrating staff? Which ones would resonate with leadership? That’s how you build a case for action.”
Quick wins to validate the data management approach
From there, the idea was to scope a small, targeted initiative that could be delivered quickly. One option was to rebuild a handful of key dashboards using cleaner data and a more modern structure. Another was to implement the basics of a data catalog—assigning ownership and standardizing terminology for the most-used data elements.
“The key is to show progress without needing a full year and a big team. When people see that a messy dashboard suddenly works better, or that they can find the data owner in seconds instead of days, the value becomes real. That’s when the conversation shifts from cost to opportunity.”
Even in early stages, with the right framing and a well-chosen quick win, data management can become a lever for change. As Joshua puts it: “You don’t have to start big. You just have to start somewhere that matters.”
Achieving quick wins
“When data management is done right, people stop wasting time looking for answers and start making decisions with confidence. That’s the shift we’re after.” Joshua Touati’s experience across industries shows that while every organization is different, the building blocks for successful data management are largely the same: shared ownership, clear definitions, and a focus on delivering value, quickly.
“If you pick something that only shows results in nine months, you’re going to lose people along the way. You have to achieve quick wins—especially when the topic is still new for a lot of people. The key is to show progress without needing a full year and a big team. When people see that a messy dashboard suddenly works better, or that they can find the data owner in seconds instead of days, the value becomes real. That’s when people start moving with you, and the conversation shifts from cost to opportunity.”
By tailoring the approach to the maturity, culture, and goals of each organization, Eraneos helps clients move from fragmented tools and ad hoc fixes to consistent, scalable data practices. The result? More control, less friction, and a stronger foundation for the future.
If your organization is grappling with data inconsistencies, unclear responsibilities, or untapped potential, it might be time to start the conversation. Eraneos offers the expertise and structure to help you take the first step—and make it count.
