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Why Data Analytics Matters Data analytics is essential for staying competitive in today’s competitive landscape. A recent study by Jack Henry found that 42% of credit unions prioritize leveraging data ...
Welcome to our very first edition of “A Day in the Life of a Data Analyst,” featuring the equally talented and down-to-earth Ann Ditlow, Data Analyst at 4Front CU. Ann ...
Bill has a deep background in the credit union industry. Throughout his robust career in the industry, Bill has utilized technology and data with finance/accounting to help credit unions and banks ...
When it comes to choosing a data analytics solution for your financial institution, the ability to provide flexible and robust integration options should sit at the top of the list. Why? Because data ingestion is the backbone of a successful data analytics program. Integrations provide the data, and when a data analytics platform doesn’t allow – or makes certain critical integrations very difficult – the entire data foundation can collapse like a domino effect. Here at Gemineye, we’ve heard of countless situations where clients and colleagues have been subject to restrictive integration parameters from data analytics providers, including: 1. Flat out inability to perform certain integrations 2. Charging hefty fees for “custom” integrations or integrations not in their wheelhouse 3. Long wait times or delays to implement an integration request In this article, we’ll break down the basics of integrations and provide tips to protect your financial institution. Let’s dive in. Why Integrations are so Important in a Data Platform There are hundreds of vendors that credit unions and community banks use to run their financial institution, and each one plays a critical role in data ingestion. Of course, the heavy hitters include your core, consumer loan and mortgage originations, and digital banking. But as technology becomes more advanced and nuanced, the third-party vendor and custom integration landscape becomes more dense. That, in turn, provides more choices – and more risk for data analytics provides to not accept that your third-party vendor integrations. For example, some data analytics providers have pre-built integrations for Hubspot, but not InMoment. If your financial institution uses InMoment, then you are stuck with the unpleasant decision of either not incorporating this key data into your platform, or switching over your entire CRM. One can quickly see how frustrating and time-consuming this situation can become. The Wild, Inefficient World of Integrations Many of the leading data analytics platforms today are capable of incorporating most integrations into your existing tech stack. The trouble is that: 1. It’s not easy – integrating one single component into a data warehouse that “doesn’t play nice,” is one of the biggest headache situations we hear from data analysts and CTOs. 2. It’s not cheap – to create a custom integration, the price tag can often stretch into six-figures. We’ve heard this many times. 3. It’s not fast – does 1 to 2 years sound acceptable to you? Not us, either. But this is a realistic timeline for the vast majority of data analytics providers. Custom integrations are often out of the question for credit unions and community banks. Your standard data analytics provider is often doing their integrations piecemeal – one at a time, and at a snail’s pace. It may even take an entire team several months to get through making a small change. It’s also not cheap. Some of these companies will charge a significant price tag for just a single change, and any further changes mean a longer waiting period and a higher price tag, often in the realm of six figures. Every data analyst has a horror story to tell about integrations. Trying to fit processes together that are incompatible is extremely inefficient, and in it can be outrageously expensive to boot. In our world of data management, we’ve found that clients are looking to make things that would otherwise be incompatible fit together on a regular basis, because they have no other choice. They’re often trying to create integrations in platforms that simply aren’t built for them, and they’re trying just about everything to make that square peg fit into that round hole (Excel sheets, anyone?) Questions to Ask Before You Choose a Data Analytics Vendor What data sources are the most important for us to have available? What vendors/systems do we consistently struggle to get data from? Have your potential vendors shown competence in the variety of sources they’ve ingested? (Look for 20+ integrations) Can the vendors provide references or case studies regarding their integration successes? How transparent are they about pricing, change orders, and additional fees? Discover More About the Capabilities of the Gemineye Data Lakehouse The Gemineye Data Lakehouse currently supports over 75 integrations – even the ones that other data analytics providers won’t touch. Our integrations incorporate leading credit union and bank solutions, like consumer loan and mortgage originations, digital banking, CRM / MRM, third-party data vendors, and more. We’ve worked hard to develop a robust suite of integrations that are pre-built, meaning less redundant work for your internal team, less implementation time, and a lot more cost-savings.
Sandwich, Mass (September 19th, 2025) – Credit union and community bank data analytics provider Gemineye is thrilled to share that $880M, Chicagoland-area NuMark CU is their newest data lakehouse client. Gemineye’s world-class, Fortune 50 Databricks and Microsoft architecture, flexible integration structure, and client-ownership model made it the obvious choice for the growing credit union who recently acquired a bank. “We’re excited to share that we’ve selected Gemineye as our data partner as we begin building out our data strategy. From the very first conversations, they stood out for their flexibility and technical depth. They were able to meet us where we are, offering solutions that fit into our existing systems without requiring major changes or compromises,” says Kevin Quinn, CIO at NuMark CU (pictured). “One of the biggest reasons we chose Gemineye was their architecture. They provided a Databricks resource within our Azure environment, giving us full control to build and expand our ETLs using a wide range of data integration tools. That kind of freedom was important to us, especially as we look to scale and evolve over time,” says Quinn. Indeed, Gemineye’s use of world-class technology rails is a compelling benefit for financial institutions who are growing but still conscious of cost and efficiency. “They’re doing innovative work with Databricks and Power BI, things we hadn’t seen from anyone else,” Quinn elaborates. Gemineye is a proud Databricks Consulting Partner, a prestigious recognition not given out freely. “They also offered support in areas like data governance and analytics, which is incredibly valuable as we’re just starting out and still shaping our direction.” Maggie Chopp, Director of Business Development at Gemineye comments, “The Gemineye team is excited to welcome NuMark to our client group and thankful for collaboration and trust they’ve extended. They recognize the importance of investing in a solution that both grows with them, and that they own – not a vendor. We look forward to their ongoing success!” “What really made the difference was their collaborative approach, says Quinn. “They offered data engineering hours to help us build and grow within Databricks, making it feel like a true partnership rather than just a vendor relationship. Their platform also gives us the flexibility to layer in advanced models, AI, and other tools to get even more value from our data down the line. Gemineye’s approach is helping us see our data in a more complete way and positioning us for long-term success.” About NuMark Credit Union NuMark CU has been serving the southwest Chicagoland suburbs for over 70 years. Their mission is to enrich the financial lives of their 50,000 members across 13 Chicagoland branches. NuMark CU offers a full menu of financial services from mortgage and auto loans to free checking accounts and business services. They remain dedicated to putting their members first, treating them like family, and helping them do more with their money. NuMark helps members get to their fantastic future faster. See Gemineye’s Data Lakehouse in Action Interested in learning how the Gemineye Data Lakehouse can support your strategy and growth needs like NuMark CU? Schedule a personalized discovery call to see how our platform can transform how your institution stores, manages, and uses data.
Creating a consistent scorecard across departments can be a real challenge for credit unions and community banks. It’s rare to see even two departments from the same financial institution utilizing the same documents to measure their goals for the year. Often, the question is asked “What exactly are we trying to accomplish as an organization?” When there are misunderstandings and misinterpretations of the highest strategic goal in the organization, it’s bound to create conflicting priorities. The Problem with Vague Scorecard Metrics Your credit union’s annual strategic goal is to create positive net organic membership growth, ages 18-44. Seems straightforward enough, right? Let’s explore where there could be a misinterpretation of measuring this goal across departments. Organic: How do we define organic? Do we exclude indirect lending? Membership: How do we define membership? Do we exclude trust accounts, conservatorships, business accounts, and specialty accounts, political campaign accounts and inherited/beneficiary IRAs? Age: When do we determine age? What if a member turns 45 before the end of the year? Are they removed from this measurement even though they joined when they were 44? Growth: What is our data from the year prior? Is every department working off the same data points from the prior year? Alignment: Does each department have a consistent understanding of membership allocation across cost centers? We can see how a seemingly straightforward goal is now ripe with opportunities for misalignment. Department leaders could interpret the measurement of this goal very differently, creating employee outcomes that are pulling in different directions. Overcoming Unified Scorecard Miscommunications with Visibility A challenge we often observe among credit unions and community banks is the difficulty in actual aggregation and modeling of the data needed to produce a reliable metric. This is where having the necessary tooling like a robust data platform becomes key. One of our credit union clients features their four organizational strategic goal trends by month on every employee’s report landing page. Every employee in your organization, whether you have 10 or 1,000, should be able to explain what your unified goal is and be able to access a place where they can see the progress of the goal. The key here is visibility and repetition – and to remember that your employees have vastly different knowledge sets and perspectives based on their roles and tenure. Robust Data Modeling Mitigates Unified Scorecard Challenges From a data analytics tooling perspective, finding a data analytics solution that can provide a robust data modeling approach is critical to creating a unified scorecard. It’s important to have the ability to drill down by granular dimensions that are customizable to your own organization’s logic, such as: Date range Product category Cost center Employee Persona Engagement type If your organization doesn’t have access to these simple categorizations, you could be missing a key part of the scorecard picture. To learn more about creating operational alignment through data analytics, download our complimentary whitepaper. Gemineye’s modern data platform is designed to help credit unions and community banks manage, unify, and activate their data across teams and tools. From data hygiene to real-time analytics, Gemineye gives you the foundation to improve both operations, such as unified scorecards, and member experience. Discover More About the Capabilities of the Gemineye Data Lakehouse
