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We currently support 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 selected Gemineye as our data partner due to their remarkable flexibility, competitive pricing, and unparalleled personal touch. Their commitment to tailored assistance made them the clear choice for our needs. Since our launch with Gemineye, our data analytics capabilities have allowed us insights into our business that we never had before.
We currently support 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.
Picture it: Keanu Reeves and Sandra Bullock play an architect and a doctor who live 2 years apart in the same property and exchange letters over time… Wait, that’s ...
In the financial world, your life is all about numbers. In the world of 21st century finance, it’s really all about data. Data was always key to the operations of ...
When running a financial institution you know that data is an important part of your operations. You’re regularly collecting customer (for our bank readers) and member (for our credit union ...
In this edition of “A Day in the Life of a Data Analyst,” we talk with Mike Riley, Data Analyst at The Cooperative Bank of Cape Cod. Mike has been working in IT for almost 40 years, starting his career in the Marine Corp. As someone with Aphantasia, a phenomenon in which one is unable to visualize imagery (lack of a mind’s eye), he provides a unique and fascinating perspective on data analytics and reporting. The Cooperative Bank of Cape Cod is a $1.4B organization with nine branches around Cape Cod, MA. Interviewed by Alicia Disantis, Brand Strategist at Gemineye, Mike talks with us about his love of finding the real picture behind data, being an extrovert, and championing consistency. Alicia Disantis: What got you interested in data analytics and data analytics within financial services? Mike Riley: I’ve been at the Coop for 16 years, but this wasn’t my first go around with data. My data start happened way back in 1986. I was an aviation electrician in the Marine Corps. They automated our shop. We went from a paper-based system to a computerized system. That was my start with computers and data. I took a KUDER test, kind of like a self-assessment that matches your attributes with a job. It’s one of those wacky tests that asks questions like: “On a rainy day you would rather sleep, read a novel, watch TV?”. And it came back saying your number one occupation should be “computer programmer” and your number two should be “bookstore owner.” At that point I changed jobs and switched over to computer programmer and I started a career in programming. The Marine Corp taught me COBOL. I worked on some major systems. After a twenty-year career I retired. My first civilian job was database-driven web design. It was difficult because the web is stateless and that was a big takeaway for me. There were no more global variables. Every web page is stand alone and if you want to go from web page 1 to web page 2, you need to have enough information to connect the two together. Ten years and three-hundred websites later, I started working at the bank. I managed data, nightly processes, and the reporting systems. When I started, we were in-house. We ran, maintained, and updated all the core programs. At the end of the day when the banks closed their doors, we had to process all the transactions. It would start around 6:30 PM and run until 10 or 11 o’clock. There were even some days where the nightly update had not finished before the doors opened the next day. I like problem solving. I like logic. I like taking complex data concepts and explaining it to the average person. For example, if you ask me, “Mike, what’s a database?” And I start telling you, “Well, it’s tables, views, fields, and triggers” you are going to be confused. But if I say, “It’s a place where you can put your data into little cubbyholes.” Boom. Is that what you find most rewarding about what you do? Mike: Yeah, I think so. Trying to explain data and data concepts. Interrogating the data and trying to make sense of it and helping other people make sense of it. I like looking for connections. I am not a banker; I am a programmer. I had to understand what bankers do and the connections between the systems. Every day is a learning process. I’ve been at the Coop for 16 years, and I just last month figured out what the heck a “Now Account” is. Mike: I am an ENTP according to Myers-Briggs, which means I’m an extrovert. I enjoy sitting in a room with other people and trying to make sense of stuff. If I were an introvert, that activity would most likely drive me crazy. I’d have to grin and bear being in a room with people. Then, come back to my desk, by myself and just unscramble everything. But no, I like being in the room, and if something smells bad, I say something. Sometimes they don’t like to hear my bluntness. A key piece is this, everybody must agree on the definitions and terms. That is a concept I try to live by. Another key piece is, “Make everything as simple as possible, but not simpler.” Everybody must agree. I could tell you a story about what happened back in my early Marine Corps data processing days. I was brand new, and the department head was a retired Marine Corps officer. He got all of us into the room and starts talking about how “perception is reality.” I felt like a rock. I didn’t get it. I followed him into his office and said, “I’m not trying to be a jerk, I just don’t understand what you’re talking about.” He said, “Reality could be ‘A’, but if a customer thinks that ‘B’ is the reality, their perception of reality becomes reality. And I was like, “Oh ok, so you got to look at it from a customer’s point of view.” How do you take that learning and apply it to a modern-day outlook on what you do at the bank? Mike: You can’t start from: I want result X and then make the data fit result X. Anybody can do that. You can’t just trim off what you don’t like? You must start with the data in its ugly fashion and show what it really looks like. Have the data tell the story all by itself. And if the data story doesn’t match what you’re expecting, then ask: What caused the data to get like this? And then you must change your behavior or your process. Let the data tell you how you should be changing instead of forcing a result. That doesn’t tell the true story. Don’t manipulate the data, just show the data as it is… however ugly it is. I don’t start with what ...
Sandwich, Mass (August 21st, 2024) – Credit union and community bank data analytics provider Gemineye is thrilled to announce three new credit union visionary clients in Q2 2024: CapEd Credit Union, Credit Union 1, and Veridian Credit Union. As more credit unions grow disillusioned by the big promises and big price points of many data analytics providers, Gemineye’s revolutionary Databricks and Microsoft architecture, flexible integrations, and modest price point is an obvious choice. Gemineye’s mission is to empower community financial institutions to become more data driven, making data analytics accessible to all credit unions. “A credit union’s ability to become data driven is now key to their viability,” said Brewster Knowlton, CEO of Gemineye. “Providing an affordable, yet extremely capable option that credit unions of all sizes can afford is critical to empowering the credit union movement. We are thrilled to welcome these exceptional credit unions to the Gemineye family.” CapEd Credit Union, Meridian, ID, $1.6B Assets “CapEd chose Gemineye after evaluating several analytics vendors because it was clear to us that they have a leadership team who is passionate about both the credit union industry and how to empower credit unions by providing insights,” said Toby King, CIO at CapEd CU. “Gemineye’s robust, mature framework is built on Microsoft’s cloud platform. These are people steeped in credit union operations and terminology, as well as experts in deriving meaning from data. Win-win”. The fact that the Gemineye Data Lakehouse leverages world-class technology from Microsoft and Databricks is a powerful differentiator. It doesn’t take much time before clients see the benefits of using these superior technologies, compared to others. Credit Union 1, Anchorage, AK, $1.5B Assets “We are excited to join the Gemineye family!” said Marvin Anunciacion, Director of Data Analytics at Credit Union 1. “When we went through our vendor selection process, and spoke with other credit union leaders, Gemineye was a clear winner for us in their: – speed of implementation – pre-built solutions for our critical software platforms – native cloud and Databricks architecture – out-of-the-box data visualization solution – extremely high praise from existing clients – very competitive pricing model Icing on the cake for us was our knowledge of Brewster’s long history and dedication to the credit union movement and his role of helping credit union leaders mature their comprehension and utilization of their data.” Veridian Credit Union, Waterloo, IA, $7.5B Assets “After reviewing a variety of credit union-centric solutions, it was clear the product was the best fit for Veridian,” said Angi Erikson, Manager of Business Intelligence at Veridian Credit Union. “Specifically, a few things that stood out to us include: – The modern, cloud-native application – The integrations that connect systems we already use to their platform – Power BI being the preferred visualization tool for both Gemineye and Veridian – The knowledge and expertise of cloud technology and tools – We look forward to a collaborative partnership with Gemineye.” #### > Additional coverage by Finopotamus here > Additional coverage by CUBroadcast here
As the financial services industry becomes increasingly data-driven, credit unions and banks are collecting more information than ever before. This data—from customer transactions and loan details to market insights and regulatory requirements—holds the potential to transform how financial institutions operate. However, to unlock the value of this data, it needs to be organized and accessible. This is where a data warehouse comes into play. A data warehouse centralizes and structures the vast amounts of data that institutions collect, making it easier to analyze, report, and ultimately use to drive strategic decision-making. The Role of Data Warehouses in Financial Institutions A data warehouse serves as the backbone of data management for financial institutions. It consolidates data from multiple sources, including core banking systems, payment processors, loan origination systems and customer/member relationship management (CRM/MRM) platforms. The primary purpose of a data warehouse is to store this information in a way that supports reporting and analysis, allowing institutions to track trends, identify risks, and make data-driven decisions. Unlike operational databases, which handle daily tasks like processing transactions or updating customer accounts, data warehouses are optimized for analytical workloads. This means they are structured to manage large volumes of historical data, enabling credit unions and banks to look at long-term trends and patterns. For example, data warehouses can help financial institutions analyze customer behaviors over time, such as tracking how often customers take out loans, what types of accounts they hold, or how frequently they interact with the bank. These insights are invaluable for identifying opportunities to offer new products or improve services. Why Data Warehouses are Critical for Credit Unions and Banks Given the volume and sensitivity of the data financial institutions handle, managing it efficiently and securely is crucial. A data warehouse offers several key benefits that make it an essential tool for credit unions and banks: Informed Decision-Making: Financial institutions deal with highly complex data from multiple sources. A data warehouse aggregates this information into one central repository, enabling decision-makers to access comprehensive data in one place. Whether it’s evaluating loan portfolios, understanding market trends, or analyzing customer behaviors, data warehouses help institutions make well-informed, data-driven decisions. Improved Customer Experience: Data warehouses provide institutions with deep insights into customer behavior. By analyzing transaction histories, spending habits, and product preferences, banks and credit unions can tailor their services to meet the specific needs of each customer. For instance, personalized loan offers or custom financial products can be developed based on customer behavior, improving satisfaction and building long-term relationships. Enhanced Compliance and Regulatory Reporting: Credit unions and banks must comply with strict regulations and generate frequent reports for regulatory agencies. Data warehouses streamline this process by automatically collecting and organizing the data needed for compliance reports. Additionally, having a central repository of data ensures that these reports are accurate, timely, and based on a complete view of the institution’s operations. Risk Management and Fraud Detection: By analyzing historical and real-time data, a data warehouse allows financial institutions to identify patterns that may signal potential risks, such as loan defaults or fraudulent activity. For example, institutions can track customer behaviors to detect suspicious transactions and respond quickly to mitigate fraud. Data warehouses also help with credit risk management by assessing customer payment histories and loan performance over time. Common Use Cases for Data Warehouses in Finance Credit unions and banks use data warehouses to enhance their operations and optimize decision-making. Below are a few of the most common use cases for data warehouses in financial institutions: Customer Segmentation and Analytics: A data warehouse helps institutions understand customer segments and behaviors, enabling more effective marketing and product development. By analyzing patterns in customer spending, credit unions and banks can identify high-value customers, predict future behaviors, and target customers with personalized offers. Loan Performance Monitoring: Credit unions and banks can track the performance of loan portfolios over time using a data warehouse. This includes analyzing loan approval rates, repayment patterns, and delinquency rates. With these insights, financial institutions can better assess their loan risk exposure and adjust lending practices accordingly. Fraud Detection and Prevention: Data warehouses are essential for detecting anomalies in transaction data, such as unauthorized access to accounts or abnormal spending patterns. With real-time monitoring and analysis capabilities, financial institutions can quickly flag suspicious activities and take steps to prevent fraud. Regulatory Compliance: Data warehouses streamline the generation of reports required by regulatory bodies. Whether it’s tracking the institution’s adherence to anti-money laundering (AML) policies or reporting on financial health metrics, data warehouses make compliance easier by ensuring data is accurate and easily accessible for audits. Financial Forecasting: By analyzing historical trends in market data, interest rates, or customer behaviors, data warehouses can support financial forecasting. Institutions can use this information to predict future trends, adjust their strategies accordingly, and prepare for market shifts. The Future of Data Warehousing in Financial Institutions The need for advanced data management systems like data warehouses will continue to grow as financial institutions become more reliant on data for decision-making. Emerging technologies, such as artificial intelligence (AI) and machine learning (ML), are already beginning to integrate with data warehouses, allowing institutions to derive even deeper insights and more accurate predictions. For example, AI algorithms can be used to analyze data warehouses for predictive analytics, identifying trends and opportunities before they materialize. Furthermore, as more financial institutions move to cloud-based infrastructure, cloud data warehouses are becoming a cost-effective and scalable solution. Cloud-based warehouses offer the flexibility to expand storage capacity and processing power as data needs grow, all while reducing the overhead associated with maintaining on-premise hardware. Why Choose Gemineye for Your Data Warehouse Needs? At Gemineye, we understand the unique challenges credit unions and banks face when managing vast amounts of data. Our tailored data warehouse solutions are designed to help financial institutions unlock the full potential of their data. Whether you’re looking to improve customer insights, enhance compliance reporting, or strengthen risk management, Gemineye’s services provide the tools you need to make smarter, data-driven decisions. With our ...
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