With over a decade of leadership experience at community banks, Kim Snyder knows firsthand the severe limitations fragmented data places on smaller institutions. After serving as CFO for a publicly traded community bank, Snyder became frustrated by the obstacles to leveraging insights trapped in disjointed systems.
In response, she founded KlariVis in 2019 with the mission of consolidating fractured data sources into unified analytics. KlariVis aims to empower community banks to overcome analytics deficiencies hampering their competitiveness by compiling siloed information from across platforms into integrated dashboards.
Snyder recently spoke with Jim Marous of The Financial Brand, host of the Banking Transformed podcast, to share invaluable insights from her journey leading technology initiatives at regional banks. She outlined common data pitfalls that hinder community institutions, along with emerging solutions to help transform siloed datasets into strategic assets.
As pressure mounts, Snyder provides an insider perspective into the most crippling data challenges and underlines how consolidated analytics can equip community banks to unleash the power of data.
Listen to Podcast:
The High Costs of Fragmented Data
Q: What are some of the biggest data challenges community banks and credit unions face today?
Kim Snyder: Core systems were built for processing transactions, not analytics. So critical data gets trapped across disconnected silos — the core, lending systems, customer data and more. Vendors often restrict access to clean extracts.
This means no holistic view of customer relationships across products. Manual reporting delays actionable insights. And teams distrust inconsistent reports from fragmented systems.
Q: How do these limitations specifically impact key functions like lending and deposits?
Snyder: Without an integrated view, it’s impossible to see full customer behaviors across products. In lending, you lack insights into risk and cross-sell opportunities. In deposits, you can’t analyze balances across account types.
Critical blindspots are the result. And gaps restrict strategically leveraging data to refine tactics. I’ve seen countless hours wasted simply trying to extract and make sense of data. The status quo severely restricts performance.
Q: How far behind are smaller institutions in leveraging analytics capabilities compared to large banks?
Snyder: Sophisticated analytics at mega banks put community institutions at a disadvantage. Smaller banks struggle with dated cores not built for modern analytics. And they lack the technical expertise to consolidate and analyze data on their own.
For community banks and credit unions competing against megabanks, data-driven decision-making can determine success or failure. The playing field urgently needs leveling.
Read more about banking data analytics:
The Maturing Role of Data Analytics
Q: You led technology initiatives as a community bank CFO. What differences have you seen over the past decade in data infrastructure?
Snyder: Ten years ago, I saw how difficult it was to leverage insights from disjointed systems. The situation has only grown more complex with specialized solutions for different lines of business.
Very little progress has been made — especially at smaller institutions — in bringing data together for holistic visibility. Community banks’ hands remain tied without unified data.
“Community banks’ hands remain tied without unified data.”
Q: How are leaders’ perspectives changing regarding the strategic role of data analytics?
Snyder: Leaders recognize data analytics capabilities are no longer just nice to have — they’re a prerequisite to remain competitive. The challenge lies in justified skepticism after years of technology investments failing to deliver unified insights.
Patience is growing thin. Banks need complete visibility into client relationships and operational performance to thrive. Fragmentation can no longer be an excuse.
Centralizing Data for Actionable Insights
Q: At a high level, how does KlariVis integrate siloed sources into unified analytics?
Snyder: KlariVis ingests raw data from across core and ancillary systems. We clean up data, map it to common definitions and surface it in intuitive dashboards. These provide role-based insights — whether lenders, executives, analysts or the CEO.
Everyone sees integrated information in visualizations tailored to their specific questions, all from a single trusted platform.
Q: Could you provide more detail on how KlariVis creates business value from consolidated data?
Snyder: By integrating data and visualizing it nightly, we enable access to timely insights. Changes in customer or operational behavior become visible immediately versus delayed reports.
With a unified view, banks can investigate root causes by drilling into specifics. No more waiting for custom requests when engineers have bandwidth. Common dashboards give the entire organization a single source of truth.
Our team’s banking expertise ensures that data integrations accelerate high-impact use cases rather than just mining trivia.
Bankers Solving Bankers’ Data Challenges
Q: What unique expertise does KlariVis leverage with your background in banking leadership?
Snyder: As former bankers, we have a deep, firsthand understanding of industry priorities and business context. This helps us focus on high-value use cases and data points from the start versus falling into interesting but irrelevant rabbit holes.
We take a hands-on approach to implementations, partnering with the bank’s team rather than just handing them a generic technology toolkit. Our experience helps avoid common pitfalls as we configure the solution to each bank’s needs.
Q: How does your team translate data into insights tailored for frontline bank staff?
Snyder: Our backgrounds enable us to deliver highly intuitive visualizations mapped to the needs of specific roles. We don’t require banks to have specialized data scientists on staff. The platforms’ simplicity and customizability democratize access to insights for everyone.
Integrating Data for Complete Customer Views
Q: How does data fragmentation specifically impact customer intelligence for community banks?
Snyder: Without connecting data across product silos, there’s no way to see complete customer relationships or behaviors. A commercial client may have multiple accounts and loans, but limited systems obscure the full picture.
This breeds critical blindspots regarding customer profitability, risk and cross-sell propensity. And sales conversations happen without insight into how else you could meet needs. Lacking integrated views severely restricts relationship banking.
Q: How specifically could an integrated view of commercial banking clients create value?
Snyder: By connecting operating account activity with lending balances and credit behaviors, a much fuller understanding of client circumstances emerges. This enables proactive mitigation of risks and early identification of unmet needs.
With holistic intelligence, far more relevant conversations occur and new opportunities surface. However, fragmented systems hide insights that could help both the bank and the client thrive.
“Lacking integrated views severely restricts relationship banking.”
Overcoming Data Access Obstacles
Q: You mentioned vendors restricting access to data. Could you expand on this challenge?
Snyder: Yes, many core and other solution providers make data access unnecessarily difficult, even treating it as their own IP rather than the bank’s information. Their incentives are misaligned with customers’ goals.
Banks need to demand open access and clean extracts in contracts from the start. The ideal state is nightly automated data feeds they can use, not just see. With the right foundations, banks can finally leverage data as strategic assets.
Q: How does KlariVis help banks overcome obstacles to accessing their own data?
Snyder: We handle connecting to the core and ancillary systems to pull needed data. Our teams’ experience helps unlock information banks that were previously barred from leveraging in integrated ways.
With accessible, actionable data as a starting point, analytics and unlocking greater intelligence comes next. But none of that matters if the raw information remains locked away. We dismantle those barriers.
Democratizing Data-Driven Decisions
Q: How specifically does KlariVis change employees’ access to data?
Snyder: By eliminating reliance on intermediaries, self-service access empowers all employees to find insights independently on demand. No more waiting on isolated analysts and delayed reporting.
Frontline staff can quickly answer pressing business questions without bottlenecks. This democratization helps scale data-driven decision-making across the organization.
Q: What cultural challenges can greater transparency create when expanding access?
Snyder: Some in leadership fear the loss of control from exposing data more widely. There’s also reluctance from certain employees regarding sharing performance metrics openly. And discomfort with empowerment versus just following directives.
But the benefits outweigh the risks. Proactive communication and coaching are key to overcoming reservations about transparency.
“Frontline staff can quickly answer pressing business questions without bottlenecks. This democratization helps scale data-driven decision-making across the organization.”
Q: How can bank leadership help address concerns about expanded data access?
Snyder: Leadership must communicate the vision for empowering staff at all levels via data. They need to highlight the benefits of fact-based coaching and development. And executives should lead by example, embracing openness themselves.
Skepticism is understandable, given traditional mindsets. Cultural evolution takes time. Patience, combined with leadership commitment, is imperative.
Dig deeper:
The Road to Predictive Analytics
Q: Looking ahead, how do you envision KlariVis leveraging AI capabilities?
Snyder: The next step beyond holistic monitoring is predictive analytics. We can develop models to flag customer attrition risk, estimate loan default rates and anticipate other scenarios.
This evolution surfaces insights that can help banks stay ahead of trends rather than just giving rearview visibility into historical data. Focusing AI on high-impact use cases will maximize its strategic value.
Q: What advice do you have for modernizing data analytics in pragmatic steps?
Snyder: Start with an executive commitment to an enterprise data strategy. Inventory key sources and data elements driving core decisions. Partner to accelerate progress rather than attempt massive internal IT projects. And use visual tools to quickly identify data issues requiring attention.
With the right vision and plan, unlocking business value trapped in siloed data is within reach for community institutions ready to compete.
For a longer version of this conversation, listen to “Unlocking Growth by Tearing Down Data Silos,” an episode of the Banking Transformed podcast with Jim Marous, available here or wherever you get your podcasts. This Q&A has been edited and condensed for clarity.
Justin Estes is an award-winning writer, strategist, and financial marketing expert with expertise in banking, investments, and fintech. His clients include the NYSE, Franklin Templeton, Credit Karma, Citi and, UBS, and his work has appeared in Forbes, Barrons and ThinkAdvisor as well as The Financial Brand.