A few months ago, we sat down with 5 data professionals to explore the challenges and ambitions they encounter across different industries and data types.
What we hear is that companies tend to think they have a data problem. In reality, many of them face a bigger issue. No one is clearly responsible for how data is requested, understood, and trusted.
Data teams sit right in the middle of this tension. The company expects them to support decisions.
They need to deliver insights quickly and keep numbers accurate. This is important in complex systems. They also face questions when results do not meet expectations.
The mix of pressure and scepticism comes directly from how data is positioned and handled inside companies, and across departments.
Quantity is suddenly more important than quality
As your business becomes more data-aware, the number of requests directed at data people explodes, while the quality of those requests often drops. Questions tend to arrive framed as urgent, with little agreement on definitions, scope, or the decisions they inform.
Multiple stakeholders depend on the same dashboards, each with different priorities, timelines, and interpretations; thus, data professionals stay in constant delivery mode, where speed is prioritised over solutions and processes.
From the outside, this looks like productivity. Within the team, there’s a buildup of unresolved issues.
Inconsistencies and multiple tools affect trust faster than errors
People don’t slowly lose faith in data; it’s more like a switch flips.
Trust in data doesn’t usually fade gradually; it disappears the moment something doesn’t add up. When two dashboards show different results for the same metric, people lose trust in the numbers. They begin to rely on their instincts, personal trackers, or gut feelings. At that point, whether the cause is technical or process-related, you don’t have enough time and just want something you can trust.
The problem runs deeper than a few mismatched dashboards. Most companies have several data tools with different owners and histories. They didn’t intend to compete, yet they constantly tell slightly different stories. Naturally, people stick with the system they know best, even if it’s less accurate, because comfort feels dependable.
That’s how small inconsistencies grow into bigger structural issues. By the time the internal data team steps in, trust has usually broken, and instead of generating insights, they’re busy trying to earn back confidence. Suddenly, every discussion about data turns into a conversation about whether the data itself can be believed.
The easy way out is not what you need to look for
When a data platform/ hub becomes complicated or outdated, it is easy to decide to replace it. And when the next platform shows its limits, people add plug-ins, dashboards, and manual fixes to cover the gaps, and the cycle continues.
In most cases, the problem isn’t the technology itself; it’s the capability and connection. Different departments struggle with advanced systems when they lack the skills or habits to use them effectively, and simpler setups start to malfunction when the external expectations increase.
When something breaks, it’s easy to blame the system. Rarely does anyone zoom out and ask whether the structure, ownership, or communication around the data might be the real issue.
The people closest to the data analysts, product owners, and mid-level managers usually spot the problems first, yet they don’t have the authority to fix them. Senior leaders approve tooling decisions, but they often don’t experience the daily friction those choices create.
Once there is no trust in the data shown, every report becomes something people question, and every meeting turns into a debate about “which number is right,” which makes analysts spend more time defending previous work than exploring new possibilities. It becomes a draining feedback loop.
Data maturity
Within the same company, people often have very different relationships with data. Some use it to support their decisions, while others feel it challenges their experience or limits their freedom to act.
The difference can appear in small behaviours. This includes ignoring reports, choosing only certain numbers, or interpreting results in ways that feel comfortable.
The data team is then expected to influence decision-making, even though they don’t have the authority to enforce change. They’re responsible for encouraging adoption across teams that don’t share the same understanding of what working with data actually means. That situation can become exhausting, because responsibility and control don’t exist in the same place.
Don’t wait until it’s too late to bring in external expertise
External experts (data & AI partners) usually enter when the pressure’s already high. By that point, everyone wants quick results, and there’s little patience left for testing or iteration.
Progress can still happen. However, it won’t last if you don’t address the underlying issues. These issues include ownership, decision-making, and alignment.
Without shared responsibility for defining, understanding, and sharing data, the issues extend beyond technical aspects. They become structural, and this overload becomes the norm in business.
Data only works when it’s everyone’s responsibility. When it isn’t, the same cycle of confusion just keeps repeating.
