Data analytics for credit unions and banks who mean business

Recognizing a Data Warehouse is Critical to Bank and Credit Union Success The number of companies claiming to offer a data solution for financial institutions is increasing at a fast – and alarming – rate. It seems like every week there is a new company  marketing buzzwords like “complete insights,” “member 360 view,” “data-driven,” “comprehensive dashboards,” and everyone’s favorite…“AI-powered analysis.”  Much like the consolidation in the credit union and community bank spaces, technology providers have increasingly converged on similar language to describe very different products. As a result, it’s becoming harder for organizations to distinguish between a true data warehouse solution and tools that simply sit adjacent to it.  But the distinction is critical. Choosing the wrong foundation can lead to:  -Years of wasted time and derailment of your strategic plan  -Six-figures (sometimes more) of sunk cost  -Lack of organizational trust in your data and data program (which can be extremely hard to repair)  In an industry where we are entrusted as fiduciaries of our members’ money, and often operating on a mindful budget, it can be very stressful for business leaders who are trying to make the best decision for their organization’s data needs. In 2026 alone, we’ve counted a half dozen new players who claim to provide a data analytics solution to financial institutions.   In this article, we’ll provide you with the information you need to determine whether an organization is offering a partial analytics service, or a bona fide data analytics solution. This article will focus on what isn’t a data warehouse and highlight what you should know about each look-alike. For each category, we’ll cover:   -What the tool is designed to do   -Why it’s often mistaken for a data warehouse   -What it lacks  -Examples  Continue reading to discover the many data warehouse-adjacent tech solutions available and how to equip yourself with the knowledge you need to make the very best decision for your financial institution.   BI/Analytics-Only Tools What they do: Visualize and analyze data.  Why they’re confused with a warehouse: They’re often the most visible part of the data stack—dashboards are what stakeholders interact with daily.  What they lack:  -Data modeling and transformation  -Centralized governance  -Persistent, reusable business logic  Examples: Looker, Microsoft Power BI, Mode Analytics, Qlik, Tableau  Main Takeaway: Having a viz platform or suite is not indicative of whether the data being used is centralized, modeled, and governed within a warehouse.  Data Analysis & Managed Analytics Providers (“Insights as a Service”) What they do: Provide outsourced analytics, dashboards, and benchmarking.  Why they’re confused with a warehouse: They deliver insights similar to what a warehouse enables—but the underlying data infrastructure isn’t owned or controlled by your organization. Once easy way to identify them is: if you send them your data and they return reports, dashboards, or recommendations, they likely fall in this category.  What they lack:  -Visibility and direct access to modeled data  -Flexibility to answer new questions quickly  -Ownership of transformation logic  Examples: Empyrean Solutions, nCino, Nomis Solutions, ProfitStars, Callahan  Main Takeaway: If you have to package and send your data, this is an analysis service, not an owned and governed warehouse.  Reverse ETL Tools What they do: Push data from a warehouse into operational systems (e.g., CRM, marketing tools).  Why they’re confused with a warehouse: They interact with many business systems and are often positioned as part of the “modern data stack.”  What they lack:   -Data storage   -Transformation logic  -Governance  Examples: Census, Hightouch, Polytomic, RudderStack, Segmint, MelissaData  Main Takeaway: Reverse ETL tools are often apart of a robust outcome drive data initiative, but their function doesn’t replace the necessary lifting that happens before data is returned to ancillary tools.  Customer Data Platforms (CDPs) What they do: Build unified member/customer profiles for marketing and engagement.  Why they’re confused with a warehouse: They promise a “single customer view,” which sounds similar to a 360-degree data model.  What they lack:  -Full business data coverage (finance, operations, risk, etc.)  -Flexible analytics across domains  -Cross-functional metric standardization  Examples: Alkami, mParticle,Salesforce Data Cloud, Segment (Twilio), Tealium   Main Takeaway: A CDP only knows your member/customer, not your entire business.  Data Augmentation Platforms What they do: Clean, enrich, or enhance existing datasets.  Why they’re confused with a warehouse: They improve data quality, which is often associated with “better data infrastructure.”  What they lack:   -Centralized data modeling  -Historical tracking and lineage  -Enterprise-wide metric consistency  Examples: Experian data enrichment, Lob (address standardization), Census data  Main Takeaway: Better data ≠ governed data.  Core Banking/Operational Systems What they do: Process transactions and run day-to-day business operations.  Why they’re confused with a warehouse: They are often treated the single source of truth in lieu of having standardization across data sources.  What they lack:  -Analytical performance at scale  -Historical modeling and transformations  -Cross-system integration  Examples: Corelation (KeyStone), Jack Henry, Finastra, Fiserv, FIS  Main Takeaway: Your core system is a key source of collecting data, but isn’t optimized to hold everything, maintain historicals, or analyze it.  Marketing Platforms/MCIF What they do: Enable campaigns, segmentation, and member engagement.  Why they’re confused with a warehouse: They often present unified customer experiences and segmentation.  What they lack:   -Governed enterprise-wide data models  -Consistent metric definitions  -Deep analytical flexibility  Examples: Adobe Experience Platform, HubSpot, Salesforce Marketing Cloud, Strum  Main Takeaway: A unified experience is not a unified data model.  AI/Advanced Analytics Platforms What they do: Build models, predictions, and advanced analytics.  Why they’re confused with a warehouse: They are often marketed as “intelligent data platforms.”  What they lack:   -Standardized business definitions  -Governed metrics  -Foundational data modeling  Examples: AWS SageMaker, Azure Machine Learning, Dataiku, DataRobot, SAS  Main Takeaway: Like Reverse ETL Tools, AI and ML platforms work on already modeled and governed data, not before.   Why Choosing a True Data Warehouse Matters When financial institutions confuse data warehouse adjacent tools with a true data warehouse, several problems emerge:  -Inconsistent metrics: Different teams define KPIs differently (i.e. what is a member?)  -Lack of trust: Reports don’t match across departments and erodes morale.  -Wasted effort: Analysts repeatedly rebuild the same logic, sometimes week over week.  -Wasted money: The upfront sunk costs of the vendor selection process, training, onboarding and implementation, and the ongoing cost of paying a vendor who isn’t providing the services you need adds up fast.  -Lost time: In the race to connect with members and predict their needs, lost time puts organizations behind the 8 ball against their competitors.   -Slower decision-making: Teams across the organization debate numbers instead of acting on them.  Quick Reference Guide Tool Category  Primary Use  Why It’s Not a Warehouse  BI Tools  Visualization  No data modeling or governance  Managed Analytics  Outsourced insights  No internal ownership of data  Data Lakes  Storage  No standardized metrics  Reverse ETL  Activation  Depends on warehouse  CDPs  Customer profiles  Limited scope  Data Augmentation  Data quality  No modeling or governance  Core Systems  Transactions  Not optimized for analytics  Marketing Platforms  Engagement  No unified data model  AI Platforms  Predictions  No metric definition  What a Data Lakehouse Actually Is A data warehouse is a structured, modeled system designed specifically for analytics and reporting.  It provides:   -Governed, consistent metrics   -Standardized business definitions   -Historical tracking and transformations   ...

Introduction Mobility CU is a three-branch, $360M credit union based in DFW, Texas. Their early adoption of a data analytics culture makes them unique among similarly-sized credit unions. What they have in common with their peers, though, is their desire to improve car repo processes and decrease their losses. Car repossessions are at their highest level since 2009, and are up 16% from 2023 and 43% from 2022, reports Cox Automotive. And the state of Texas, where Mobility CU resides, has the second highest rate of car repossessions in the nation, second to California. Mobility’s CEO Ron Perry and VP of Lending Ruben DeLoera were interested in reviewing their current car repo processes and asked their data analyst to investigate, using the Gemineye Data Lakehouse and their collections integration. Challenging Assumptions with Objective Data There’s certainly no shortage of repo companies in Texas, and not all are created equal when it comes to payout. Historically, Mobility CU worked with repos who they had the best relationship with, which included a half-dozen organizations. But as repossessions grew, it made sense to audit their current relationships and question the old way of doing things. “Selling our repossessions is of big importance to us,” explains Chris Clifford, Data Analyst at Mobility CU. “The CEO and VP of Lending asked me to look at what were we actually getting from the auctions at an aggregate level. We needed to question all assumptions. Which auctions were the best bang for our buck?” Collections Integrations Proves Fruitful Mobility CU had just completed their collections integration with the Gemineye Data Lakehouse, which was the first step in auditing their repo relationships. Once the collections platform was integrated into the Gemineye Data Lakehouse, Mobility suddenly had insights into repo prices that they’ve never had access to before. “I looked at all our repossession cases and calculated the average of fees per repossession and the time between when we got the car and when it was sold”, says Clifford. “We wanted to know who was giving us the best percentage.” Clifford was able to create a report that looked at how various auction repossessions compare in terms of the average fees they charge and the amount they ultimately get at their auctions. This allowed the credit union to identify which companies to shift their business to. By switching repo companies, Mobility was able to save $100-$150 per car, and occasionally more. And with 15-20 repos per month, this one report saves the credit union between $18,000 and $36,000 a year. “A single report paid for the entire collections integration and then some. That’s pretty amazing!” exclaims Clifford. Creating a Data-Driven Culture for Success Transitioning to a data-driven culture takes time and patience, but the benefits can be transformative. Once the bar starts rolling, it becomes easier to incorporate data into strategic thinking and decisioning. This was exactly the case with Mobility CU. “It was so smart of the CEO and CLO to reassess our repo strategy,” says Clifford. “This report was originally intended to be a one-off, but we’ve discovered it’s totally doable to continue to use reports like this.” Mobility CU will continue to integrate reports like this into their departmental strategies. Clifford comments, “The true value of Gemineye is that y’all have lived up to the promise of being our analytics partner and not just another vendor, as evidenced in both the level and quality of service y’all have provided. Y’all have helped us all along our analytics journey even long after the initial implementation was done.” Conclusion While financial institutions look to increase their non-interest income, integrating their collections platform with the Gemineye Data Lakehouse proved quite helpful for Mobility CU. They were able to gain valuable insights into which car repo companies were providing the best payouts in a time when repos are high. What’s more, their transition to a data-driven organization has allowed them to challenge the status-quo and make decisions with facts rather than speculation. Discover More About the Capabilities of the Gemineye Data Lakehouse Interested in learning how the Gemineye Data Lakehouse can help you make data-driven decisions like Mobility CU? Browse the solutions we provide and teams we help!