How the Stories Banks Tell Can Power Strategy – or Sabotage It
By Kim Snyder, CEO & Founder at KlariVis
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Need to Know
- Many banks aren’t struggling with data — they’re struggling with the gap between what they believe and what their data actually shows.
- A new look at “Data EQ” reveals a widening performance divide that has nothing to do with tools — and everything to do with leadership mindset.
- The banks pulling ahead aren’t chasing trends; they’re challenging long-held stories that no longer match reality — and doing it before the market forces their hand.
Why is it that some banks seem to navigate disruption with a greater sense of clarity, while others remain stuck in a pattern of delayed reactions and familiar justifications?
There are always multiple variables behind performance. But one pattern I continue to see is this: institutions that consistently respond with agility and confidence are often those that have built stronger bridges between the stories they tell and the data that could confirm or reshape them.
This tension — the divide between story and data — has always existed in banking. What’s changing now is how visible and consequential that divide has become.
A recent Cornerstone Advisors report put it into stark relief. Of the institutions with high “Data EQ,” or the ability to use data meaningfully, nearly 75% view information as a strategic asset. That drops to 30% among average performers. Among low performers, it’s just 3%. The gap isn’t about tools. It’s about whether leadership sees data as something that supports a living strategy, not just a historical record.
The banks making real progress aren’t abandoning narrative: They’re refining it. They’re using data to ask better questions, to update long-held beliefs and to build cultures that are both grounded in experience and open to change.
Why Context Alone Isn’t Enough
Most seasoned bankers would agree that context is essential in banking. Years of lived experience, deep relationships and long-held knowledge of how business flows through the institution all have a role to play. But in today’s operating environment, where competitive pressure is accelerating and margin for error is narrowing, context alone can be misleading if it’s not paired with visibility.
It is not uncommon for institutions to assume they know who their most profitable customers are, only to uncover a very different reality once they have access to fully integrated relationship data. Similarly, assumptions about why deposits are leaving or what is driving loan yield deterioration can become misaligned with fact, especially when decisions are made based on memory or static reports. What feels familiar often passes as truth, but until those assumptions are tested with real-time data, confidence can be misplaced.
All institutions have the data, but many do not yet have the systems or processes to interpret it meaningfully. This gap between signal and understanding is where performance often stalls. A high attrition rate, for example, is a valuable metric, but it only raises the question of what is wrong. It does not help answer why customers are leaving or which parts of the bank’s model need to be examined more closely.
This is where decision intelligence platforms add measurable value. By translating raw data into patterns and aligning those patterns with broader strategic priorities, these systems allow leadership teams to move from detection to understanding and ultimately, to action. The value is not in the volume of data, but in how quickly and clearly the data can support a more informed conversation.
The Generational Divide: Experience vs. Tech Fluency
One of the more nuanced challenges many banks are now navigating is the subtle tension between experience and technological fluency across their teams. This is not a barrier to change so much as it is a reality to manage thoughtfully.
Those who have spent decades in the industry often bring with them an invaluable understanding of how relationships evolve, how risk manifests and how to interpret nuance in a customer’s behavior. These leaders remember the why behind the policies and understand the rationale behind pricing and structure decisions. But many of them also built their careers in environments where data was reviewed at month-end and where insights were gathered through retrospective reporting rather than proactive analysis.
On the other hand, younger professionals may not have the same institutional knowledge, but they tend to expect a level of responsiveness from their systems that legacy tools cannot provide. They are more comfortable operating in real time, more willing to experiment with automation and more attuned to opportunities that emerge from pattern recognition. However, their ability to interpret what they see may be limited without support from those who can put the numbers in context.
Neither group is inherently better equipped to lead on their own. But without intentional integration between the two, banks risk losing the opportunity to fully leverage both expertise and momentum. When banks create space for shared ownership of insight — where business knowledge is paired with technical fluency — the resulting collaboration is often where strategy gains real traction.
Dig deeper:
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Bridging the Gap: Practical Solutions
There is a temptation to treat the solution to this divide as a technology problem, when in reality, it is a people and process challenge that requires intentional leadership.
For institutions where skepticism around data remains common — often at the senior level — it is helpful to focus less on changing minds overnight and more on fostering dialogue. When leaders are invited into the process and shown how timely insight connects to business outcomes they already care about, they are far more likely to support the changes needed to build a more data-forward culture.
For those who lack confidence in working with data directly, the solution is not to remove them from the process, but to give them access to mentorship. Banks have long understood the value of mentoring junior staff around credit and relationship management. That same model can and should be applied to data literacy. In many cases, the barriers are not technical. They are cultural.
And for those already straddling both worlds, leadership’s responsibility is to support them with the resources and time to influence others. This is not a one-person function. The goal is to ensure that clarity is distributed, not hoarded and that decisions are guided by a shared understanding of what matters most.
Once the people are aligned, the next priority is building a repeatable process. It is not enough to review data during strategic planning sessions or quarterly board meetings. Data has to become engrained into the institution’s operating rhythm — something that informs pricing, staffing, lending and marketing decisions consistently and in real time.
Platforms that support decision intelligence are built for this kind of cadence. They do not simply provide access. They provide structure. And in doing so, they help leadership teams avoid the scramble that comes with relying on outdated reports and instead focus on performance that is already unfolding.
AI Is Only as Smart as Your Data Strategy
There is no question that artificial intelligence (AI) is becoming a more visible part of banking conversations. The potential applications are significant and for many institutions, AI represents an opportunity to accelerate insights that previously took hours or days to surface.
But before banks can benefit from these tools, they must first ensure that their foundational data practices are strong enough to support them. AI is not a substitute for structure. If the data is fragmented, outdated, or poorly governed, the outputs will reflect those limitations, regardless of how sophisticated the model may be.
There is a tendency to view AI as a forward-looking solution, but, practically speaking, it is only as powerful as the platform underneath it and the data fueling it.
This is why I believe the conversation about performance intelligence still matters more than the conversation about automation. Until institutions are confident in the quality of their inputs, they are not yet ready to accelerate their outputs.
Banks That Move Differently Think Differently
No single strategy guarantees performance. But across the institutions I’ve worked with, the ones that adapt most effectively tend to make different assumptions about how to lead.
They do not treat data as a quarterly exercise or an afterthought. They see it as part of how the institution works, not just what it reports. They treat visibility as a precondition for good decisions, not a byproduct of reporting cycles. And they do not rely on instinct at the expense of insight. They use both, in tandem, trusting that good leadership depends not only on what you know, but on how clearly you can see what is changing around you.
At a time when markets are moving faster and expectations are rising, the ability to turn information into forward movement is not just an advantage. It is becoming the baseline for performance.
