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Data Estate Modernization for Banks and Credit Unions

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What You’ll Learn

This Guide helps lay out the fundamentals of Data Estate Modernization – including Guiding Principles to Practical Data Governance, considerations for choosing and implementing new technology components, and how to build a better roadmap to a modern data strategy.

We have interviewed data leaders at from Renasant Bank, Berkshire Bank, and Camden National Bank for this comprehensive guide that can serve as both a starting point and a reference document. 

Download a PDF of this Guide

Why Should Banks and Credit Unions Invest in a Modern Data Estate?

To fully leverage existing and future technology investments, banks and credit unions need to build a scalable and flexible data architecture to  accommodate all data sources and all data users. But this isn’t as easy as it sounds – as data ecosystems grow, data grows – exponentially – in granularity and volume.

Banks and credit unions have an ever-expanding collection of point systems, software solutions and 3rd party data sets to leverage as they seek to provide the best services and offer innovative products for account holders. But with every addition to a banking ecosystem, it becomes increasingly difficult to maintain a single version of the truth, access a complete account holder picture, or uncover operational efficiency issues caused by redundant or stale data. Business line users from different departments will draw from data sources they can access, leaving untapped insights in siloed systems, and creating disparate account holder views across the institution.

Data Estate Modernization helps banks and credit unions move toward a single version of the truth by creating a centralized, scalable architecture that promotes data self service and Data Governance.

With a modern data stack, banks and credit unions can:

  • Simplify data management, security, and observability;
  • Ensure data is consistent, regardless of data type or data source;
  • Support self-service analytics and integrated reporting capabilities from any data source;
  • Invest in an architecture that grows with business needs and burgeoning data and regulatory requirements;
  • Reduce maintenance and infrastructure management costs; and
  • Enable practical Data Governance practices that ensure data is properly managed, secured, and used in compliance with regulations and policies.

This Guide Features

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Table of Contents

 

Practical Data Governance

Read about the Practical Data Governance principles that should be at the foundation of any Data Estate Modernization campaign. Learn how to create a Governed Data Lake Warehouse where data is timely, trusted and known. Start building your Data Governance Posture with advice from three banking leaders.

Adoption of a Modern Data Strategy

Discover the forces driving data estate modernization, and read about how banks and credit unions have adapted to major technology shifts in the past. Learn what features are “must-haves” for leaders from four banking leaders. Uncover the importance of use case based adoption to create transformation champions and stymie adoption fatigue.

Assembling a Modern Data Stack

Build a better technology roadmap with Data Rocket, a modern data stack built on Snowflake. Data Rocket allows you to assemble data in a governed cloud data lake warehouse. Data can be enriched by 3rd party or enriched 1st party data, trusted for use in Enterprise AI applications, and put into action with self-service insights.

Practical Data Governance

Why start this Guide with a section on Data Governance? Quite simply – Data Governance should be at the heart of every decision involving your data estate. Data Governance shouldn’t be viewed as a heavy lift – but as an integrated, strategic component of data management. Data Governance unlocks the ROI potential of organizational data by enabling employees to best know, trust and use data in scale in order to drive revenue and efficiencies.

Data Governance is typically applied across five areas of focus – Data Availability, Data Quality, Data Security, Data Usability and Data Auditability. While there are many useful tools to help build a Data Governance program, most solutions aim to address one or two of these categories. There is no silver bullet – Data Governance should be viewed as a holistic concept, and programs should be organically developed based on the needs and structure of an organization.

Data Governance Guiding Principles

At a surface level, these Guiding Principles for Data Governance might look simple – it would be hard to find anyone who would argue that data shouldn’t be trusted or accessible. In practice, applying these principles takes intention, discipline and tools that add scalability, observability, and ultimately, durability to your Data Governance program.

  1. Data should be ingested, stored and processed to promote accessibility.
  2. Data can be trusted, known and measured.
  3. Data is relevant to business use cases.
  4. Data is secure, observable, auditable and compliant throughout its lifecycle.

Data should be managed to promote accessibility.

Making data available to the right user at the right time is a critical driver of digital transformation and a central tenet of Data Governance, and to do that, more and more organizations are turning to the cloud. According to a report by American Banker, 98% of banks are using specific business goals to make investments in cloud technology. This trend applies to financial services institutions of every size.

Cloud adoption will only increase as banks and credit unions try to keep pace with data availability, cost savings, scalability and seek out the agility to adapt with transforming their business and the innovation unlocked with cloud and cloud-hybrid solutions.

While cloud adoption will continue to increase, adopters of a cloud-based data management strategy have quickly come to understand that cloud utilization is not enough, especially when it comes to establishing Data Governance programs. To support data accessibility, banks and credit unions should focus on the structure of their data ecosystem, with an eye toward business use cases.

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Maintaining a Data Lakehouse at Berkshire Bank

Data Governance has been at the forefront of Berkshire Bank’s data estate modernization initiative.

To achieve their goals, Berkshire adopted Data Rocket, a modern data stack built on Qlik Talend Data Management and Snowflake, and implemented Talend Data Catalog.

With the right Data Governance tools in place, Berkshire Bank has created a workflow that promotes a well-governed cloud data lakehouse, said Peter Love, Chief Digital Officer at Berkshire Bank.

“We use Talend Data Catalog to validate that what data stewards see in the warehouse is what they expect.

Nothing gets in the warehouse if it doesn’t come through the catalog and it’s not governed. In the past, anybody could put anything in the warehouse until it became a data swamp – that’s no longer the case. So now it comes into the data lake in a governed way, and that’s a key part of our entire strategy.”

Create a governed Data Lake (not a Data Swamp)

A data lake is a central repository for all data – unlocking data from siloed systems and creating a landing zone for other data applications. The creation of a data lake is often seen as the primary goal of a digital transformation initiative; in reality, creating a data lake is just the starting point. Data Governance should address how a data lake is administered and maintained.

For data to be usable within a data lake, care should be taken during the ingestion and integration process. A data lake can become a swamp if it is not properly managed and organized. To prevent muddying the waters of your data lake, a metadata management system should be used to keep track of data. Regular data quality checks and monitoring should be
performed to ensure data is accurate and up-to-date. Finally, data should be regularly pruned to remove any unnecessary or outdated data, and cataloged so it can be found easily and understood by anyone in the organization.

Ensure Data is Timely

Functional Data Governance ensures data is as timely as it needs to be. Most organizations don’t need real-time data ingestion, but that doesn’t mean they don’t need to make sure data is timely and up to date.

Change Data Capture (CDC) helps ensure data in the data lake is updated as changes are made to the source system. CDC tools track and capture changes made to a database so organizations don’t have to wait for a full data refresh to identify and capture data changes.

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How Quick Access to Timely Data Helped Berkshire Bank Respond to the Fallout from Silicon Valley Bank

Data Governance has been at the forefront of Berkshire Bank’s data estate modernization initiative. To achieve their goals, Berkshire adopted Data Rocket, a modern data stack built on Qlik Talend Data Management and Snowflake, and implemented Talend Data Catalog. With the right Data Governance tools in place, Berkshire Bank has created a workflow that promotes a well-governed cloud data lake, said Peter Love, Chief Digital Officer at Berkshire Bank. “We use Talend Data Catalog to validate that what data stewards see in the warehouse is what they expect. Nothing gets in the warehouse if it doesn’t come through the catalog and it’s not governed. In the past, anybody could put anything in the warehouse until it became a data swamp – that’s no longer the case. So now it comes into the data lake in a governed way, and that’s a key part of our entire strategy.”

Provide data & analytics for business use

Building robust business analytics is a straightforward focal point for emerging Data Governance programs. Analytics highlight the importance of access in Data Governance programs – with robust analytics, data end- users don’t have to wait on reports from IT and sift through erroneous or duplicate data to get exactly what they need. Relevant analytics removes human error from data decision-making.

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How Data Governance Creates Data Monetization at Renasant Bank

At Renasant Bank, driving business use is at the center of every

data management decision. Troy Carmichael, Director of Enterprise Data Management at Renasant, says that making data accessible is the best way to expand data usage and to create a culture where data adds value to the bank.

“If you open that ecosystem out to the business users, they’re going to start driving more and more use cases. And the best thing that we can do in IT is to enable the people who are on those front lines – anyone with visibility that can help them monetize this data. I see our roles not just as data management, but data monetization officers.”

Data is Known

Building a data literacy program in your bank or credit union can help drive Data Governance and will help identify data owners and data stewards, if they are currently unknown. During the establishment of a data literacy program, you will want to identify and engage with relevant stakeholders, which could include IT personnel, data management teams, business analysts and data users.

With your stakeholders, identify what you want to capture in your data dictionary, including data element names, definitions, data types, accepted ranges and values and data sources. Your data dictionary will help define how data is maintained and who is responsible for maintaining it. Once your data dictionary is in place, every relevant stakeholder should be educated and have access to the data dictionary. Data literacy builds scalability into your data management by eliminating tribal knowledge and building a common understanding of basic data stewardship.

Data is Trusted

In the last decade, banks and credit unions have made massive investments in point solutions for virtually every aspect of the banking experience, from online application platforms to customer relationship management (CRM) to fraud detection. While each serves a functional purpose, they also create multiple versions of the truth, which can have far-reaching implications across business lines and operational teams.

These sprawling data ecosystems leave business users with unpalatable  options – get an incomplete picture of an account holder from one system, cobble together a messy collection of data points from disparate systems, work with IT, who may not be familiar with the data points of the business use case or assemble that picture on your own creating a shadow data mart/data warehouse.

There is a better way. To fully leverage the benefits of point solutions, alongside the power of core banking solutions, banks and credit unions can create a Single Version of the Truth with a well-governed enterprise lake warehouse. With an enterprise lake warehouse, business users across an organization can access trusted data with confidence, knowing they are pulling the most relevant and complete data, and getting a complete, 360-degree view of account holders and their enterprise operations.

Data is Relevant to Business Use Cases

Moving data to an enterprise data warehouse is just the start of a data estate modernization initiative. To make data usable to different use cases and business users, your data architecture will have to support integration into a variety of applications.

Data transformation is THE essential bridge from data lake to business use cases, ensuring your data can be trusted, observed and acted upon as it moves through your organization.

Data availability and accessibility, enabled by data transformation, helps support the most critical indicator of a successful Data Governance program – a Data Governance Culture. To promote a Data Governance Culture, champions must exist throughout your organization – from IT, to customer service, to sales, to senior leadership.

The easiest way to show the value of Data Governance is to make data available for any use case – which often means making it available to third- party data science applications and software platforms. But that’s not as easy as it sounds – data applications are diverse, including ML/AI models, analytics dashboards, and marketing platforms. The data required to drive customer support use cases will be completely different from the data required for laser-focused marketing, or KPI-driven C-suites.

Data integration and transformation are essential components of making sure data consumers have access to relevant and ready-to-use data.

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How Renasant Bank Uses the Snowflake Data Cloud for Curated Reporting

When it comes to reporting, Troy Carmichael, Renasant Bank’s Director of Enterprise Data Management, distinguishes core system reporting and curated reporting. When it comes to curated reporting, Snowflake is at the heart of Renasant’s data strategy.

“Some business users only need to pull data from one source system – adhoc reporting is fine for that. But when you start talking about taking core data and tying it to other pieces of data – you start to see the power of modern data management,” Troy said. “In our lake warehouse, we have curated data that we call the Single Version of the Truth. On the reporting side of that, we have curated reporting. It’s that data – the data that is tied to all the other systems – that is used for decision-making.”

Data is Secure, Observable, Auditable and Compliant throughout its Lifecycle

It has always been imperative for banks and credit unions to keep data secure. But putting data behind a lock and key doesn’t make it more valuable. As data ecosystems grow and applications become more diverse, data security, observability and compliance are foundational in banking data management.

An important first step is to define Data Governance policies and procedures to be applied across your organization. When it comes to putting those processes in place, the right technology tools can help automate and scale Data Governance – reducing the burden on your technical team.

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How Camden National Bank Uses Data Rocket to Scale its Data Governance Initiatives

Camden National Bank has a nimble team of IT professionals – six people deliver data management for the entire institution. Josh Nash, Director of IT at Camden National Bank, uses Data Rocket to enable his small team to do big things.

“We have all the pieces in place – we have a Data Governance Committee and methods for creating data marts and approving them. We have processes for data cleanup that go back to the source,” Josh said. “It’s the right model, but you’ve got to start with the right tools – otherwise, your people are going to be doing a lot of work.”

How to Build Your Data Governance Posture

Data Governance might be seen as a heavy lift, and even viewed as competition for other valuable resources in IT. It’s time to abandon that mindset. Regardless of where you are in your data estate modernization journey, Data Governance should be top of mind.

If you are just getting started, the first step should be establishing a clear understanding of data assets and how they are used throughout the organization. This includes identifying all data sources, classifying data based on sensitivity and importance, and defining ownership. Once you’ve established a glossary of data in the organization, you can determine how you want to control data – establishing who can access data, how they will access it, and the security, auditability and visibility measures in place to enforce your Data Governance strategy.

If you have a Data Governance program that is floundering, take a step back and envision what you could improve. Tools that can automate manual processes help you troubleshoot faster, and ultimately free up resources for higher-value work that pays for itself and helps create a scalable Data Governance framework. Integrated Data Governance keeps data secure while making it accessible and observable throughout the data lifecycle. By integrating your Data Governance program throughout your data catalog, ETL and data stack, you can build practical workflows and formalized Data Governance roles, setting a strong, scalable foundation for your organization.

Adoption of a Modern Data Strategy

You’ve taken the time to establish your Data Governance program, understand your data assets and usage, and identified key stakeholders. How do you begin your data estate modernization?

When it comes to adoption of new technology – modernization is nothing new to banks and credit unions. Banking leaders had to adapt to changing technology and consumer demands. While the first ATM was introduced in 1967 at Barclays Bank in London, adoption didn’t become widespread until the 1980s. Early adoption had to overcome concerns with the cost of machines, maintenance and customer’s initial apprehension toward the new technology. But ultimately, the drive toward ATM adoption was customer-driven, with consumers demanding convenient, 24/7 access to bank accounts. Banks had to adapt or face becoming obsolete in the new landscape of financial services.

Today, the financial services industry is again at the cusp of a technological revolution: cloud migration. Just as with ATMs, the drive toward cloud adoption is spurred by changing customer demands. Today’s consumers expect 24/7 access to banking services, from a variety of devices, with superior user experience, personalized services, and top tier security measures. Additionally, they demand innovative services that can only be achieved through the real-time processing and analysis of data, a key advantage of cloud technologies.

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How Berkshire Bank is Using Data Rocket to Assemble, Trust, Enrich and Act

Three years into its data estate modernization, there are very few aspects of Berkshire Bank’s operations that are not impacted by having access to trusted data. From driving operational efficiency to improving customer experience,

Berkshire Bank is realizing the benefits and opportunities of having an agile, scalable data architecture built on Talend and Snowflake.

“The ability to, first of all, consume data, make it available, and then gain insights out of it in real-time has completely changed the way we address and deepen our relationships with our customers,” said Peter Love, Chief Digital Officer at Berkshire Bank. “We can also better satisfy our regulators, because they’re asking us a number of questions on the fly all the time. We’re able to gather that information, and again, turn it around more quickly than we ever had before.”

Faced with these customer expectations, the need for operational resilience, and the drive for cost-efficiency, banks and credit unions must adapt to the new technological reality or risk becoming irrelevant.

Migration to the cloud is not just a luxury, it is a necessity for survival in an increasingly digital financial landscape. The current transformation echoes the past: adapt or go extinct.

The Forces Driving Data Estate Modernization

Data estate modernization is being propelled by several significant forces. Chief among these is the need to comply with data privacy regulations.

Laws like GDPR and CCPA mandating stringent data handling practices will only become more commonplace, and organizations are compelled to modernize their data estates. A cloud-based data infrastructure offers improved data management capabilities and increased agility, enabling organizations to protect sensitive information more effectively, meet compliance requirements, and maintain the trust of their clients and partners.

The emergence of open banking is another significant driving force.

Open banking, a system where banks and other financial institutions share user data with third-party providers through APIs, necessitates a highly secure, scalable, and flexible data estate.
Modern data platforms can handle these requirements, providing seamless integration capabilities with third- party systems, managing large volumes of data efficiently, and facilitating the development of innovative financial products and services.

Fluctuating markets also play a pivotal role in driving data estate modernization. In the face of unpredictable market trends, banks and credit unions need agile data infrastructures that can adapt quickly and enable rapid decision- making. A cloud-based data estate allows for real-time data processing and sophisticated analytics, aiding organizations in making informed decisions and quickly responding to market changes.

And just as we saw with ATMs, changing consumer demands exert considerable influence on this modernization drive. Today’s consumers expect personalized, convenient, and fast services. To meet these expectations, businesses need to harness the power of data analytics and AI, which require a modern data estate. By transforming their data estates, banks and credit unions can gain deeper customer insights, predict consumer behavior, and tailor their offerings to meet the evolving needs of their customers. This kind of customer-centric approach, facilitated by a modern data estate, is crucial for banks and credit unions that want to stay competitive in today’s dynamic market environment.

How to Choose Technology Components

Cloud First

When it comes to data estate modernization, the future is in the cloud.

The transition to cloud computing is crucial for banks and credit unions as they adapt to open banking, a paradigm shift that emphasizes transparency, interoperability, and customer data sharing with third-party

financial service providers. Cloud migration enables financial institutions to manage data effectively, increase operational efficiencies, and significantly improve the speed and flexibility of their services. As part of the open banking framework, these institutions can securely share customer data with approved third parties, fostering the creation of new, personalized financial products and services. In the era of digital transformation, a cloud-based approach ensures that banks and credit unions remain competitive by utilizing cutting-edge fintech solutions and enhancing the customer experience.

Moving to a cloud infrastructure offers a scalable platform that can adapt to a bank’s evolving data needs. This flexibility and scalability are vital for institutions anticipating growth and intending to innovate their service offerings continuously. A robust cloud infrastructure can handle significant data volumes, providing a powerful platform for advanced analytics and machine learning algorithms. These capabilities are key in creating targeted products and services based on sophisticated data analysis, ensuring these financial institutions keep pace with the customer’s ever-changing needs. Cloud-first solutions provide banks and credit unions with the agility they need to experiment with innovative ideas, implement changes rapidly, and maintain a robust, secure, and efficient IT infrastructure, all of which are key elements to thriving in the era of open banking.
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Why Camden National Bank Chose a Cloud-First Approach to Technology Adoption

Camden National Bank knew that it needed lightning-fast reporting to respond to the data needs of the bank’s data consumers. Josh Nash, Director of IT at Camden National Bank, said moving data out of old systems and into the Snowflake Cloud helps his team provide superior, responsive reporting throughout the institution.

“Whether you’re small, medium, or large bank, you want to be able to respond rapidly and be first to market with things. We need something that’s very flexible and allows us to work quickly,” Josh said. “Cloud solutions allow us to be able to move fast – we aren’t tied to an archaic core. With all our data in one place, we’re able to rapidly produce reports and get it out of the warehouse and into the hands of people who can actually use it.”

Automation

The beauty of data estate modernization is that many banks and credit unions can create efficiencies that go a long way toward paying for the investment in new technologies.

But automation is not just a way to reduce overhead – finding the right tools for automation tasks can play a crucial role in enhancing operational efficiency. When time-consuming tasks like auditing and data monitoring are automated, technology teams can use their valuable human resources for projects that bring value to the organization.

As banks and credit unions respond to increasing regulatory requirements, automation helps lighten the load for IT and reporting arms of financial services institutions and can help manage the increasing complexity and volume of regulatory requirements. With accessible data and automated reporting, banks and credit unions can easily complete necessary compliance reports, demonstrating adherence to regulations in a timely and efficient manner.

Additionally, automation results in an improved customer experience, allowing banks and credit unions to process loan applications quickly and leverage data to deliver personalized customer interactions.

Future Proof

It is critical to choose technology components that are compatible with your vision for your data ecosystem. By focusing on extensibility, you can maximize your technology investments and build scalability into your data management. Extensible tools that can seamlessly integrate with other systems prevent the need for sweeping, costly changes. They also provide the room to navigate the influx of microservices that are flooding the market for banks and credit unions. As an example, as banks and credit unions look to create better customer experiences through web and mobile banking, their data management platform will need to be able to integrate with a variety of technology vendors and interactions.

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How Renasant Bank is Using Data Rocket for a Customer 360 View

Renasant Bank sees Data Rocket as a competitive advantage that allows the institution to have complete ownership of its data in order to support a complete account holder view.

“Knowing your customer and getting that 360-degree view of your customer is only possible if you can pull in data from all your different systems and understand how customers are related to each other,” said Troy Carmichael, Director of Enterprise Data Management at Renasant Bank. “There are a lot of banks right now that cannot bring up a single pane of glass and know what a customer’s footprint is in the entire bank, what the relationships are, how they’re tied to certain businesses and how those businesses are tied to certain businesses.”

How to Guide Adoption and Overcome Implementation Fatigue with Use-Case Based Deployment

Anyone who has worked with data in the last decade has gone through at least one, but probably multiple, technology implementations. In a perfect world, these implementations go smoothly and show immediate results.

But too often, unproven solutions can overpromise and underdeliver or at least lead to a period of instability and frustration.

With use-case-based deployment, you can begin leveraging your technology investment immediately, driving adoption organically and creating transformation champions in your organization. With agile deployment, your IT department can continue its functional support of critical banking operations, while laying the groundwork for scalable data lake data management and building data literacy across the organization.

How to Choose the First Use Case

Choosing a first use case is exciting, but it’s important to be realistic. Your first use case should be Well-Defined and Impactful.

What makes a use case Well-Defined?

Accomplishable use cases can exist anywhere in your institution – from IT to call centers to business lines. Regardless of where the use case exists, it must have two common criteria – your use case must have a sponsor and the data for the use case must be known and accessible.

What makes a use case Impactful?

Impactful use cases are usually persistent business problems that have a quantifiable outcome attached to them, a clear business value. For example, automation of mandatory reporting or loan processing can save thousands of hours of human capital and will help avoid the cost of human error. Integration of 3rd party data can unlock insights into untapped opportunities in your current banking footprint, driving increases in HELOCs or deposit acquisition.

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How Camden National Bank Leverages Use-Case Based Deployment for Immediate Impact

Josh Nash, Director of IT at Camden National Bank, said focusing on use cases has created an agile deployment framework that has accelerated utilization of its modern data platform.

“With Data Rocket, we can build on all the data we have and build data marts for future use cases, while that’s happening, we can give people results immediately,” Josh said. “It’s the best of both worlds. Down the road, we’re going to have something that’s as efficient as possible. But while we’re migrating to it, we have a functional system that can deliver results.”

The benefits of data estate modernization are clear, but starting down the road of data estate modernization might still feel like a Herculean task.

The good news – you don’t have to go it alone. Renasant Bank, Berkshire Bank, and Camden National Bank chose Data Rocket®, a modern stack built on Snowflake, to accelerate their data estate modernization effort.

Data Rocket helps guide adoption with prebuilt connectors that hasten migration to the cloud and build practical Data Governance into your data architecture. With Data Rocket’s agile, use-cased deployment, you can lay the foundation of a modern stack while reaping the benefits of the first use case in 30 days.

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How Berkshire Bank Supports New Use Cases with Data Integration

As Berkshire Bank expands its data estate modernization initiative, it strives to be able to support any new business use case from the Snowflake Data Cloud. Chief Digital Officer Peter Love explained how the integration layer, built into the Data Cloud with Data Rocket, supports new business use cases.

“The integration layer is critical – not only do you want to have access to the data to consume the data, you have to be able to push it wherever you want to in real-time,” Peter explained.

“To control our own destiny, we need to be independent. With Data Rocket, we’re no longer beholden to the core provider, we’re picking the partners we want to deal with. With each new partner, we need to integrate the systems based on the priority of the business. Integration is a key component of our architecture, and it’s critical to leveraging the assets we’ve been able to ingest in Snowflake.”

Building a Better Roadmap

When it comes to building a better technology roadmap, it helps to know where you are, and what you hope to achieve. For banks and credit unions that are happy with their footprint and aren’t worried about account holder attrition, a move toward data estate modernization might not be essential. But for institutions that want to compete and thrive in the coming decade and beyond, data estate modernization is imperative.

There is no shortage of data modernization solutions for consideration. The single-most important factor to consider as you move forward with your transformation roadmap will be maintaining ownership and agency over your data estate. Start conversations about your technology roadmap with your vision for your institution – where you want to grow, what problems you want to address, where you see yourself in 10 years. Avoid paying for solutions with pre-built connectors you don’t need and canned dashboards you’ll never use. The goal is to eliminate your technical debt, adding components that support your vision and help you grow toward your goals.

Data Rocket – An Accelerated Data Stack for Banks and Credit Unions

Data Rocket was created to accelerate the journey of financial services institutions that are migrating toward an enterprise data warehouse. Data Rocket moves data out of silos into the Snowflake Data Cloud, where it can be used throughout the organization. Data Rocket is built on four pillars: Assemble, Trust, Enrich, Act.

Assemble: Easily assemble any data type from any source system. With its Governed Dynamic Ingestion Engine and metadata-driven integration layer, Data Rocket provides a resilient, replicable and automated way to move  data to the Snowflake Data Cloud.

Trust: Automation and acceleration tools build practical data governance into your data stack. Data Rocket comes with prebuilt architecture and code that creates foundational data governance practices. These components include:

  • Mastered Data Framework – eliminate duplicate data to create a Golden Record in your organization.
  • Data Quality Watch® – profile and measure data based on integrity, timeliness, consistency, completeness and popularity.
  • Observe and Control Framework – enable targeted, expedited troubleshooting and decrease business outage time with historic and real-time data ingestion information within
  • Snowflake Watch – quickly gauge key volumetric stats with customizable visual dashboards, and drill down to the query- and user-level for deeper insights.
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How Renasant Bank is Leveraging Its Data Architecture to Compete with Supernational Banks

Renasant Bank wanted to empower its business users and IT with data technology that could vault it past competing institutions, regardless of size. 

“With our data architecture, we can more easily compete with national and supernational type banks. Things we were not normally able to do as regional banks of our size, we’re now able to because of the Snowflake environment and because of what we’re doing with Data Rocket,” said Troy Carmichael, Director of Enterprise Management at Renasant Bank. “There are things this modernization has teed up that allow us to go into our regional market and compete in a way that was not possible beforehand.”

Enrich: Curated industry-leading datasets and AI/ML modeling tools. Data Rocket couples established use cases with pre-built dashboards for faster implementation and ROI.

Act: Prebuilt dashboards democratize data. Put relevant data in the hands of business-line decision-makers with visual analytics dashboards, built for your use case. Unlock new and emerging technologies with a future- ready data stack.

Data Rocket builds a foundation that embraces core banking and point solutions, while preparing banks and credit unions for future customer experience initiatives, Embedded Finance/BaaS, and Open Banking. Data Rocket isn’t a cookie-cutter approach that tries to address the needs of ALL banks and credit unions, it is a versatile, scalable solution that will grow with the needs of YOUR institution.

Ready to Get Started? With this guide, you’ve discovered how banking leaders are taking ownership of their data estate to better adapt to market changes, increase wallet share, gain new customers, fuel innovation, and improve operational efficiencies as well as meet compliance requirements. You’ve learned that Data Governance, when introduced at a foundational level, can help inform the development of your data architecture and data strategy, and create a framework for a Data Governance Culture.

If you’re ready to get started on your data estate modernization, or could use help optimizing the path you are currently on, Passerelle offers complimentary roadmap assessments to help you understand your strengths and liabilities and help you define a use case for immediate value.

More Resources

Case Study:
Baycoast Bank

Video Testimonial:
Berkshire Bank

Guide:
Agile Data Governance