The People Problem: The Hardest Part of Data Transformation

Financial institutions have spent millions modernizing their tech stacks – implementing digital platforms and cloud-based lending systems.  And yet, the same complaint keeps surfacing:

“We have the tools. But we still don’t have the answers when we need them.”

This isn’t a technology failure. It’s a translation failure; and more specifically, a people problem.

Because while infrastructure has evolved, the hardest part of transformation isn’t systems. It’s adoption. It’s execution. It’s culture.

Outdated Infrastructure Still Sets the Pace

Most banks are still anchored to core systems built decades ago. These are rigid, closed platforms that were never meant to support real-time insights or enterprise-wide collaboration.

More modern systems – CRM, LOS, treasury, digital – get layered on, but they often operate in silos. They don’t share data back with the institution in meaningful ways. So instead of becoming more agile, banks find themselves tangled in even more fragmentation.

To solve this, some pursue data lakes or lake-house architectures. But many of these projects get stuck in complexity. Months go by configuring pipelines, monopolizing teams and workloads without improving anyone’s ability to make decisions.

And that’s the core issue: data that isn’t usable, isn’t valuable.

The Real Barriers Are Human, Not Technical

Technology can produce insights. But it’s people who decide whether those insights are trusted, shared, and acted upon.

And in many banks, that’s where things break down.

Here are the most common people-related roadblocks we see:

• Silo-first mindsets – Departments treat data like proprietary territory, making cross-functional visibility and alignment nearly impossible.
• Relationship banking bias – Lenders still lean heavily on instinct and history, even when data shows a clearer picture.
• Low data literacy – Many employees simply haven’t been equipped to interpret or use data confidently.
• Spreadsheet comfort zones – Familiar tools like Excel persist, even when they introduce inefficiency and risk.
• Risk and compliance anxiety – And perhaps most quietly destructive: over-corrective controls.

In an effort to manage risk, some institutions lock down data so tightly that no one can actually use it. Access is restricted. Sharing is discouraged. Cloud adoption is delayed. The intent behind these policies is protection, but the unintended consequence is paralysis. According to KPMG’s 2025 banking tech survey, 93% of banking executives cite data privacy and risk concerns as a major challenge.

At our most recent Executive Data & Innovation Summit, Ron Shevlin, MD & Chief Research Officer at Cornerstone Advisors, made an important distinction: the gap in most banks isn’t technical capability — it’s cultural maturity. What he coins “Data EQ” is less about system literacy and more about leadership follow-through. High EQ institutions treat data as a shared asset, not a departmental deliverable. They measure quality, build trust, and make space for accountability. Low EQ institutions, on the other hand, tend to over-rely on control — restricting access, locking down tools, and avoiding the messy conversations around what good data really looks like. As Ron said: “if your Data EQ is low, your AI strategy will blow”.

When no one can access the data they need, when they need it, and in a manner that they can easily interpret and act on it, it doesn’t matter how advanced your platform is. Your processes have stagnated your growth.

Over-Customization Kills Momentum 

Another common trap is the belief that software must be completely tailored in order to be scalable: every view, every report, every workflow customized to perfection.

In theory, it sounds reasonable. That is until you realize the project has dragged on for eight months, internal enthusiasm has faded, and what should have been a strategic rollout now feels like just another system to learn. Suddenly, what was originally the project’s strength, actually becomes its weakness.

Speed matters. When you’re trying to shift culture, you have a short window to capture attention and build momentum. Wait too long, and your teams will retreat back to the comfort of their spreadsheets and homegrown workarounds. And across departments, the fatigue is real. Banks are rolling out more platforms than ever, and employees are simply overwhelmed.

If It Doesn’t Fit the Way People Work, It Won’t Work

This is the crux of the people problem: most data strategies are designed around platforms and architecture, not around the end user.

Banks today ask their data team to build dashboards, run specific queries, navigate complex reporting tools and the like.  The problem is that the data team doesn’t always speak the same language that the business team speaks.  And such, the reports or dashboards created miss the mark entirely and the data team is left pondering why when I build it, they don’t come?

For data to be used, it has to be:

• Easy to access
• Intuitive to understand
• Embedded in existing workflows
• Clear about what matters, and why

It has to remove friction, not add it.  It has to empower people, not overwhelm them.

Jason Henrichs, CEO at Alloy Labs and keynote speaker at our September Summit, put a sharper point on this challenge: too many institutions confuse presentation with purpose. Dashboards are useful — but only if they drive action, right? The real issue isn’t how data looks, it’s how clearly it translates into a next step. As Jason shared, insight without movement is just performance art. Tools that aren’t embedded in workflows, aligned to goals, and made actionable in real time? They may inform, but they rarely transform. That’s why usability has to come first; not just in interface design, but in how information connects to the way bankers actually make decisions.

Data doesn’t need to be perfect. It needs to be useful.

 

Banks with KlariVis are making data accessible to all.

Here’s how.

Insight Doesn’t Drive Change By Itself

It’s easy to assume that once data is available to bankers, actions and decisions will immediately follow, but that’s rarely how it works in practice. In most banks, reporting and coaching is still a passive exercise, a performance review that happens after the fact, once the month is closed, or the board book is finalized. And by then, the opportunity to make a better decision has already passed.

The institutions that outperform don’t just analyze. They act. Not quarterly. Not monthly. Daily. With urgency and clarity. That kind of agility doesn’t come from having more dashboards, it comes from embedding data into everyday decisions.

And that level of engagement only happens when leadership sets the tone. If executives treat data as a back-office deliverable, the rest of the organization will too. But when they use it to ask better questions, challenge stale assumptions, and align performance across teams, the culture shifts. Not through memos or mandates, but through visible, intentional, and consistent action.

As one executive recently noted, if you’re not using the data to coach in the moment, you’ve already missed the opportunity.

What Actually Works

The institutions making real progress with data aren’t just investing in systems. They’re doing five things differently:

1. They start with business goals, not reporting wish lists
2. They think with the end user in mind, not for the data team
3. They focus on speed to value, not perfection
4. They embed data into daily decisions, not quarterly reviews
5. They foster a culture of transparency and follow-through

 

So. Here’s what you can ask yourself right now:

• Is our bank’s data strategy designed around how our people actually work?
• Are we enabling conversations and decisions, or just adding complexity?
• Are we chasing perfect architecture, or delivering clarity where it counts?

Technology is easy to buy. Dashboards are easy to build. But none of it matters if people won’t use it. If you want transformation that sticks — start with the people.