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In this Blueprint Guide, we’ve featured two financial services institutions that have made the move to the Snowflake Data Cloud. This guide outlines how (and why!) to get started down the path of data estate modernization in the Snowflake Data Cloud and show how to get started with an architecture audit and review, define use cases, and identify key stakeholders. Dive deep into the essential components of a modern data management stack, built on Snowflake, and learn how banks and credit unions that are adopting new technologies to address common concerns in regional banking today.
Banks and credit unions across the United States are putting Snowflake at the center of their modern data strategy, and the reason is simple. Embarking on cloud data estate modernization can be a huge undertaking, requiring stakeholder buy-in from your C-Suite to your engineering team. The effort of moving data to Snowflake is rewarded by unrivaled efficiency and security – and once your data is in the Snowflake Data Cloud, it never has to leave. With Snowflake at the center of your data architecture, you can bring applications to your data that enrich and activate it. You can create a hub for applications while maintaining one version of the truth. Best of all, you can own your data while driving business outcomes, and create a scalable framework that is built for YOUR business.
This guide explores how Renasant Bank and Hanscom Federal Credit Union are using Snowflake to build a scalable, future proof data management architecture. These three steps include:
Learn about Renasant Bank and Hanscom Federal Credit Union, and the drivers to their modernization initiatives.
Outline your modernization initiative and set goals for an agile implementation with immediate ROI.
Lay the foundation for data governance by creating a Single Version of the Truth in the Snowflake Data Cloud.
Support Data Access AND Data Security, and bridge the business | IT divide.
Data, and specifically, the ability to effectively use data for competitive advantage, are undeniable battlefields for community banks and credit unions today.
Community financial institutions are facing competition from non-traditional adversaries that were “born in the cloud” and larger institutions that have already embarked on cloud data estate modernization initiatives with bigger budgets and established data teams. There has never been a worse time to struggle with the blind spots in your business lines and customer base caused by siloed data. Market volatility, evidenced by sky-rocketing interest rates and rapidly changing consumer appetites, demands agile and intelligent decision-making with trusted data across the enterprise.
The primary driver of data estate modernization is simple… the brittle tools and systems of yesterday create an opportunity cost that can’t be ignored. When coupled with the expanse and granularity of data needed to support the enterprise, modernization is essential to seize control and build the competencies required to successfully manage and leverage your data as an asset. Without the ability to automate and observe data management, regulatory compliance is burdensome, operational efficiencies are out of reach, advanced analytics, including Machine Learning (ML) and AI – are a pipe dream, and real-time integrations to new technologies that deliver extended functionality and capabilities to consumers and businesses will forever be challenging, if not impossible. In short, without modernization, innovation is unattainable.
What’s more, white-glove service, once delivered in person by local branches and lenders – must now be delivered virtually to account holders who expect a personalized, multi-faceted, seamless banking experience. Jared Silver, the VP of Data and Information Technology at Hanscom Federal Credit Union, said adapting to consumer needs is a primary driver in the credit union’s data estate modernization initiative.
“Deposits are going up nationally across all financial institutions, and retail banking traffic is going down. Branches are closing and we’re not getting that face-to-face interaction anymore,” Jared said. “That personalized experience we’ve always delivered, we now need to turn that into a digital personalized experience.”
The task before HFCU and other community banks and credit unions can’t be supported by legacy tech or a patchwork of point solutions.
“We need modernized systems; we need to get all our data together and commingle it, and then analyze it so it can be really targeted at our offers to our account holders,” Jared said. “The better we know our members, the more targeted we can be. We want our members to know that we know what is important to them and that we are ready to support them on their journey.”
In this Blueprint Guide, we’ve featured two financial services institutions that have made the move to the Snowflake Data Cloud. We outline how to get started down the path of data estate modernization in the Snowflake Data Cloud and show how to get started with an architecture audit and review, define use cases, and identify key stakeholders. This Guide dives into the essential components of a modern data management stack, built on Snowflake, and address some common concerns of banks and credit unions that are adopting new technologies.
We’ve chosen Snowflake as the core technology in this Guide, and the reason is simple. Embarking on cloud data estate modernization can be a huge undertaking, requiring stakeholder buy-in from your C-Suite to your engineering team. The effort of moving data to Snowflake is rewarded by unrivaled efficiency and security – and once your data is in the Snowflake Data Cloud, it never has to leave. With Snowflake at the center of your data architecture, you can bring applications to your data that enrich and activate it, creating a hub for applications while maintaining one version of the truth. Best of all, you can own your data while driving business outcomes, and create a scalable framework that is built for YOUR business.
Renasant Bank is a regional bank based in Tupelo, Mississippi. After its 2025 acquisition of The First, Renasant Bank has more than 250 branches in Louisiana, Alabama, Florida, Georgia, Mississippi, North Carolina, South Carolina and Tennessee. Renasant Bank currently manages $25 billion in assets. Renasant Bank started its data estate transformation but was frustrated by the cost and lack of scalability in its brittle data ingestion tools. Renasant chose Data Rocket® for its ability to support metadata-driven pipelines and create a governed data lake in the Snowflake Data Cloud. Scalable data management adapts to new technologies and opportunities and brings applications directly to Renasant’s Data Cloud. These applications include GrowthLoop, a cloud-native marketing tool that launches omnichannel marketing initiatives directly from Snowflake, and 3rd party datasets to supercharge its first-party data and drive informed outreach campaigns. Technical resources can spend time on high-value data science initiatives instead of manual ingestion and data cleansing tasks.
Hanscom Federal Credit Union (HFCU) was founded at Hanscom Air Force Base in 1953, and today has more than 95,000 members, more than 250 employees and more than 350 business partners, with more than $2 billion in assets and over 95,000 members. HFCU’s outdated infrastructure made it nearly impossible to support self-service reporting, with decentralized data silos that made it hard for users to access and discover data insights. With new leadership that supported data estate modernization, HFCU wanted a platform that could help them consolidate data from across more than 150 systems to support the credit union’s business objectives. With Data Rocket®, HFCU has begun integrating and mastering data from source systems throughout the credit union, beginning with core banking data and moving to HR and loan origination systems. With Mastered Data in the Snowflake Data Cloud, HFCU is working with a standard definition of data throughout the organization, making it easier to measure success and start thinking strategically about its data assets. By bringing in ALTR, a data governance and security application in Snowflake, Hanscom has automated essential compliance and reporting tasks, and can scale data governance across its data ecosystem.
The first step to any data estate modernization initiative is auditing your current data management systems and architecture and identifying strengths and weaknesses. This audit should encompass people, processes, and technologies, with an eye toward supporting the goal of your initiative. During your audit, you will identify Data Champions who will support and implement the solution, a use case that will lay the foundation for success in your organization and set benchmarks that will measure the return on your investment.
Performing an audit at the beginning of a project is critical for identifying areas with immediate impact, aligning stakeholders in your organization, and proactively engaging in change management. Let’s take a closer look at some of the components of an audit.
Looking at the opportunities and liabilities in your current data technology stack and data management procedures can help prioritize investment. Five essential considerations are:
These questions provide a comprehensive framework to assess whether your current data technology stack is capable, efficient, and aligned with your organizational needs and goals. You might find some components of your data management satisfy your business objectives, while some no longer serve their purpose. Build on the strengths you currently have with tools that will grow your capabilities and add value through automation, innovation, or observability.
At the onset of their data estate modernization initiative, Hanscom Federal Credit Union wanted to build an architecture that would allow them to play offense in an increasingly competitive landscape, explained Jared Silver.
“You can’t do that with siloed systems and siloed data,” he said. “We needed to move away from these on-prem, brittle systems that barely supported our defensive, operational reporting. We wanted to get our folks to self-service, democratized data, and get them having fun by answering bigger questions with better analytics.”
When investing in data estate modernization, Renasant Bank leaders knew they were making an investment in their longterm competitive advantage.
Troy Carmichael, SVP of Enterprise Data Management at Renasant Bank, was looking for a data architecture that could scale with the needs of his organization.
“Our data estate is only growing, and our ability to use it and leverage it will be essential to remain competitive with larger banks and similar-sized competitors,” Troy said. “It will not be enough to simply keep doing what we have been doing. We need to be continually innovating – and Snowflake provides the right tools and technologies at our fingertips.”
Similarly, Troy Carmichael, the SVP of Enterprise Data Management at Renasant Bank, was looking for a data architecture that could scale with the needs of his organization. “Our data estate is only growing, and our ability to use it and leverage it will be essential to remain competitive with larger banks and similar-sized competitors,” Troy said. “It will not be enough to simply keep doing what we have been doing. We need to be continually innovating – and Snowflake provides the right tools and technologies at our fingertips.”
Data Champions can have any role in your organization – from data consumers to data stewards to your CDO or CEO. The most important factors to look for in your Data Champion are Impact and Energy – they are impacted by the pain point or benefit of your initiative, and they have the desire to work toward a common goal. There will be times you have to force a data modernization initiative on your data team – in that case, the pain point or benefit will likely be so great that you won’t need a lot of extra energy to carry the project. There will be other times that a Data Champion has the energy to carry out a project and will be proving a benefit for the first time – these scenarios facilitate innovation and can bring enormous value and competitive advantage.
Hanscom Federal Credit Union used the audit process to identify quick wins that would build morale across the organization.
“Culturally, getting people to think in a data-driven, data-centric way has been a little bit of a challenge,” Jared said. “But again, finding the right collaborators, the Data Champions, and ensuring that folks understand that they have the support to do new things, is essential.”
Use-case-based deployment of new technologies and data management practices can help build a strong foundation for modernization initiatives.
At Hanscom Federal Credit Union, the first use case for their modern enterprise data stack created a Balanced Scorecard that provided self-service analytics from four distinct source systems. By taking data out of silos and placing it in the Snowflake Data Cloud, Jared was able to unlock self-service analytics that gave a point-in-time look at member engagement, operational efficiencies, and employee performance. For the first time, the credit union was able to compare and correlate HR performance reviews with branch operations and identify immediate areas for improvement.
Jared and the HFCU refer to these wins as critical to overcoming “Organizational Inertia.”
“I really think starting small, having the big picture strategy and breaking it down into bite-sized chunks, you can effect change quickly,” Jared said. “When you produce the tangible wins and the tangible effects, you have the opportunity to move the needle and you can start gaining momentum quickly.”
The secret to success at Hanscom Federal Credit Union has been building proficiencies on their data team use case by use case. HFCU started by building a Balanced Scorecard to measure KPIs across the credit union and is scaling out it builds capacity.
“I really think starting small, having the big picture strategy and breaking it down into bite-sized chunks, you can effect change quickly,” said Jared Silver, the VP of Data and Information Technology at Hanscom Federal Credit Union.
“When you produce the tangible wins and the tangible effects, you have the opportunity to move the needle and you can start gaining momentum quickly.”
With a use case in mind, setting benchmarks will help you approach the project with agility and an eye toward ROI. Benchmarks align vision with execution and can be both tactical and quantitative. For example, if the goal of data estate modernization is creating an enterprise data warehouse, benchmarks could include creation of a common data glossary (tactical) along with measuring and reducing reporting times (quantitative).
There is no such thing as being too small for data estate modernization – in fact, smaller banks and credit unions can often embark on the data estate modernization without having to factor in the technical debt of previous initiatives or large, home-baked data management systems.
Successful initiatives, especially at smaller institutions, can benefit from agile development – starting with a smaller use case and expanding as proficiencies grow with new techniques and technologies.
“We have been successful by getting up to speed with a proof of concept, and then working with Passerelle on knowledge transfer,” Jared said. “It’s like using a muscle you’ve never worked out before, you can’t lift a 100-pound weight right away, you got to start with the five-pound weight. With our POCs, we are starting with the five-pound weight and building up our internal capacity.”
The goal of cloud data estate modernization is to move data out of silos and into an enterprise cloud data warehouse, where it can be integrated to create a single version of the truth. A typical bank will have customer and critical enterprise data in 30+ different source systems, from core banking to CRMs to loan origination platforms. These systems likely collected data at different points in time, resulting in data discrepancies for critical contact information – like physical and email addresses, phone numbers, and important life events – like marriage and divorce, enrolling in college, and starting a family. You can only start to establish a true Account Holder 360˚ view with a single version of the truth.
At Renasant Bank, the Single Version of the Truth (SVOT) is the polestar for the organization’s data management philosophy.
“Our SVOT is where everything starts at Renasant Bank,” Troy explained, adding that it is introduced on the first day of orientation for new team members. “After our people, our data is the most important asset we have. It is a team effort to be a steward of the data, to monetize the data, and make it part of our company culture.”
The biggest obstacle to creating a Single Version of the Truth is siloed data – but it’s not as simple as moving all your data to Snowflake. Without planning and execution, your cloud data warehouse can quickly become a data swamp, mired in duplicate data, inaccuracies, and outdated information. A successful migration to Snowflake depends on tools that can support the workload and scale with use, and practical data governance methodologies that will ensure data is well-governed throughout the data lifecycle.
The Importance of Data Literacy
The first step toward creating a Single Version of Truth in the Snowflake Data Cloud is ensuring everyone in your organization uses the same language to describe your data assets. Data dictionaries provide detailed descriptions, formats, and usage guidelines for each data element, establish a common language and standardize data handling across business lines and applications. This standardization is essential for ensuring data quality, consistency, and compliance with regulatory requirements.
Additionally, data dictionaries facilitate more effective data integration, management, and security by clearly documenting data characteristics and relationships. They also play a crucial role in training and onboarding, enhancing collaboration among teams, and supporting accurate decision-making and reporting. In essence, data dictionaries act as a foundational tool in aligning an organization’s data management practices with its broader data governance objectives. While data catalog tools can help automate and maintain data dictionaries at scale, getting started with a data dictionary can be as simple as collaborating on a shared spreadsheet.
At HFCU, building a data dictionary was the first step on their data governance journey. At the start of their data estate modernization, departments didn’t share naming conventions for even the most basic – and mission-critical – data points.
Creation of a Single Version of Truth – or SVOT at Renasant Bank – is at the foundation of the bank’s data estate modernization.
Troy Carmichael, SVP of Enterprise Data Management at Renasant Bank, said creating and maintaining the SVOT is a team effort.
“Our SVOT is where everything starts at Renasant Bank,” Troy explained, adding that it is introduced on the first day of orientation for new team members. “After our people, our data is the most important asset we have. It is a team effort to be a steward of the data, to monetize the data, and make it part of our company culture.”
“You could go to marketing, and then lending and then operations, and ask, ‘How many active members do we have?’ And you would get three different answers because they all defined ‘active member’ differently,” Jared said.
The issue wasn’t with individual workers, it was caused by unclear workflows based on siloed data.
“They all have different datasets they were working from, so no one could agree,” Jared explained. Building HFCU’s data dictionary allowed the credit union to go from playing defense with its data to starting to think strategically – creating specific terms that clearly defined member relationships within the bank – including terms like “open member,” “lending member,” and “marketing member.”
“Identifying the stewards, data owners, and subject matter experts and getting them in a room, laying out the systems, and who owns them, and all the data points, and who owns them, that made it possible to build a foundation for data quality,” Jared said.
There are many reasons organizations choose to make the move to Snowflake, including:
When considering how to move data to Snowflake, look to create data pipelines that can keep up with the growth of your data estate and support Snowflake’s native functionalities. For data integration to Snowflake, Renasant Bank and Hanscom Federal Credit Union chose Qlik Talend Data Management as part of Data Rocket® – a modern data stack built Snowflake.
Troy Carmichael chose Qlik Talend data integration and management solutions after Renasant Bank’s homegrown ETL architecture couldn’t keep up with the demands of a growing data ecosystem.
“We had some very serious stability issues as we added data from different environments. We needed to know that we were investing in a future-ready technology that would not just handle our data needs today but address future needs as we bring new sources online,” Troy said.
Jared said Qlik Talend’s cloud-native capabilities set it apart from traditional data ingestion and integration tools. With a small data team, Jared was looking for the efficiencies afforded by cloud-first data management.
“There are integration tools that have been in the game for a very long time – 20 or 30 years,” Jared said. “But it was very obvious to us that they were taking what they’ve always done, and just kind of plopping it and lifting and shifting it into the cloud. It didn’t look very modern; it didn’t feel very modern – it worked the way it always worked. It wasn’t graceful, intuitive, or user friendly.”
As banks and credit unions expand their data ecosystems, it is critical to establish observability into data movement throughout its lifecycle. Organizations should be able to easily track data sources, transformations, and dependencies and monitor data pipelines for issues like data drift, anomalies, and bottlenecks, so they can be sure that data is accurate, reliable, and available for decision-making. As banking institutions look to integrate disparate data sources and adopt advanced analytics and machine learning models, data observability ensures that the data feeding into these systems is trustworthy.
Observability built into your data management solution helps identify and resolve data issues, reduces downtime, and improves operational efficiency, playing a critical role in regulatory compliance by providing traceability and audit trails for data.
Data Rocket Observability Tools
By building data trustworthiness, observability tools can drive adoption of data initiatives, and ultimately, consumption of data throughout your organization. Data Rocket provides observability tools for ingestion, data quality and Snowflake warehouse performance.
Data Rocket features:
ALTR
ALTR’s observability tooling provides an extra layer of security to the Snowflake Data Cloud, allowing organizations to quickly see who has access to what data, and how frequently they access it. Role-based access lets administrators assign user permission at the column level, preventing unintended access to classified data in a scalable and automated manner. By observing how different roles in the organization use data, administrators can set a definition of what “normal” data usage looks like and set thresholds to prevent nefarious data downloads and access. This function can even be performed by non-technical users because ALTR’s simple and intuitive point-and-click UI democratizes what has historically been a very technical task to any data team member, without the need to script SQL statements. In doing so, ALTR reduces the complexity of administrating RBAC on Snowflake, increases the efficiency of data teams, and helps the overall organization manage risk.
Moving data to the Snowflake Data Cloud supports secure data sharing throughout your organization. Snowflake’s unique data-sharing capability allows different departments or teams within an organization to access shared data without the need to duplicate or physically move it, preserving data integrity and security. With fine-grained access controls, administrators can define user roles and permissions and ensure employees only have access to the data they need for their specific roles. This feature, along with Snowflake’s comprehensive encryption at rest and in transit, supports compliance with internal policies and regulations and ensures data remains secure, regardless of where it is accessed from within the organization.
The tools discussed in this Blueprint build Data Governance into data management with an eye toward enablement. Snowflake’s built-in data access conventions have decreased regulatory burdens at Renasant Bank, even as more people gain access to data.
“When we migrated our early datasets to Snowflake, we were able to take advantage of some of the technologies they were implementing, such as role-based masking,” Troy explained. “Now, for example, any time compliance wants to see who has access to PII information, we can quickly and easily supply a report detailing who has access and who sees only masked info.”
The small conveniences add up to big savings and fewer compliance headaches.
“Snowflake has little features here and there that seem small when you talk about them. But these features become huge in the architecture and minimize the time we spend on compliance, making auditing burden minimal.”
Organizations might be reluctant to move from on-premises data storage to cloud data management because of a misconception that data is more secure under lock and key. This is not just a misguided concern, it can inhibit organizations from using data as a strategic asset – for data to be useful, it has to be used.
At HFCU, Jared knew Snowflake would help HFCU use data to its full potential. To help with data security and governance functions, Jared brought in ALTR, the cloud-based solution
that automates data classification and access. With ALTR, Jared can automate Snowflake’s tag-based functionality to assign access privileges and identify PII data as soon as it enters the Snowflake Data Cloud. By establishing a baseline of what “normal” data usage looks like at Hanscom, Jared and his team can better understand the data consumption at the organization, identify outliers, and set proactive rules and guardrails.
“As soon as people see that their job gets easier, that they can do them better, ask better questions, and be a little bit more strategic and a little bit more offensive, onboarding a new technology gets easier. They turn around six months or a year later, and they say, ‘I cannot believe I used to do this the old way – how were we able to service our members without it?’”
“With ALTR, we can deliver a dashboard that has insights into how data is sent and define the trends around sensitive data consumption,” Jared said. “When we can put this information in front of our Chief Security Officer, it leads to all these other questions about how and why data is consumed, which drives new outcomes and possibilities.”
With HFCU’s data in the Snowflake Data Cloud, departments can look beyond their own data for a clearer picture of the organization. HFCU’s Human Resources department became an immediate beneficiary of integrated data in the Snowflake Data Cloud. When data remained siloed, HR could not create Performance Benchmarking based on other data from the credit union’s business lines. The first step was assuring them the data was secure and inaccessible to data consumers outside of the appropriate department.
“Once they see that there is no way salary information is going to make it onto a public dashboard, they can start to envision the possibilities,” Jared said. “Now, we can talk about employee performance with all the data in hand – from employee engagement surveys to their activity in the core banking system. We can look at projects and compare productivity to budgets in a way we never could before. And we can do it without any risk to the safety of our data.”
Making data accessible is critical to creating a data culture at your institution – which should be the ultimate goal of data estate modernization. For the HFCU team, replacing manual processes with self-serve reporting translated into a sea change of support.
“As soon as people see that their job gets easier, that they can do them better, ask better questions, and be a little bit more strategic and a little bit more offensive, onboarding a new technology gets easier,” Jared said. “They turn around six months or a year later, and they say, ‘I cannot believe I used to do this the old way – how were we able to service our members without it?’”
With data in the Snowflake Data Cloud, banks and credit unions can establish a 360-degree account holder view and share internal data with stakeholders throughout the organization.
What’s more, with the Snowflake Marketplace, datasets and applications can be brought directly to your data – eliminating the need to move data to outside systems that might muddy your Single Version of the Truth.
At Renasant Bank, Troy Carmichael sees the shift to the Snowflake Data Cloud as an act of self determination.
“It’s simple – don’t keep sending your data out,” Troy said. “We had vendors who would love to receive all our data and charge us for it. And then you have a sprawling ecosystem, with data swamps in your CRM, and data swamps in your social media analytics. Using Snowflake as our hub removes that complexity.”
To further improve Renasant’s data functionality, Troy turned to GrowthLoop, a Composable Customer Data Platform (CDP). GrowthLoop’s platform acts as an automated segmentation and activation layer in Snowflake that allows Renasant to create audiences from any data point in the Data Cloud, orchestrate custom journeys and campaigns, and measure results over time.
Through Growthloop, Renasant’s campaigns can be optimized in near real-time to align with their carefully cultivated single version of truth.
“With GrowthLoop, audience segmentation and journey building are as far upstream as possible, so we have access to all the data downstream to build precise, custom and responsive journeys for our customers,” Troy said. “It’s helped our team respond quickly to fluctuations in the mortgage rate environment, to brainstorm quickly and to measure the effectiveness of our campaigns.”
Alongside its work with GrowthLoop, Renasant is leveraging the Snowflake Data Marketplace to look beyond its first-party data, using Equifax B2B datasets to get a better understanding of credit risk, identify potential new clients, and find opportunities for cross-selling to current customers. By accessing the data through the Data Marketplace, Renasant was able to get datasets specific to their geographic target areas without worrying about cumbersome file transfers.
“Equifax datasets are huge, but we’re not in all 50 states – with the Marketplace, we can narrow it down to just what we need,” Troy explained. “I sent the paperwork on Wednesday afternoon and by Thursday afternoon, I had all that I needed to light it up in Snowflake. If I can get that data loaded straight up into Snowflake that simply, it reduces the overhead for my team.”
Troy said Equifax datasets augment their Customer-360 view with insights into their financial footprint outside of Renasant Bank – with applications across the bank, from personal lending to commercial lending to retirement and investment accounts. Showing the possibilities to stakeholders across the bank has increased enthusiasm across business lines.
“These enrichment opportunities create buy-in,” Troy said. “If you’re a technologist on this, you can see what is possible that was never possible before. And if you are on the business side, you can bring use-cases that translate to immediate return on the investment. It’s very collaborative.”