Point Systems Might Be Hindering Data Strategy at Banks and Credit Unions – Here’s What to Do About It 

Improving the customer experience has been top of mind for banks and credit unions for years, leading to expansive technology investments. Financial institutions have implemented targeted point solutions or microservices 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 siloed versions of the truth, which can have far-reaching implications across business lines and operational teams.  

Banks and credit unions should be able to enjoy all the benefits of point solutions without sacrificing data strategy.  

How can they do it?  By embracing a cloud-enabled Enterprise Data Warehouse that turns a hub and spoke model into a best of breed data stack.  

The Problem: Point Solutions Create Data Siloes and Partial Versions of the Truth 

Picture this: 

Theresa, the VP of Lending at a regional bank, needs to get a clear picture of customers who might be ready for a home equity line of credit. She can access baseline loan information about her current mortgage holders through her core banking or mortgage servicing platform, but that will only tell part of the story – it is likely missing detailed information about the borrowers, properties, and underwriting details on the loans themselves that may be useful in this volatile market. Her Loan Origination System (LOS, such as Encompass) can generate the underwriting information she needs about the loans themselves, the borrowers, and closing details. However, the LOS cannot answer some important questions that Theresa needs to create a strategic plan for her audience.

To get answers to these questions, Theresa needs to refer back to other sources of data such as core banking, online application platforms and the bank’s CRM. These questions include: Has anything changed in the customer’s overall financial health since acquiring the loan? How have current financial markets impacted their debt-to-income ratio? Has the customer’s footprint at the bank grown or decreased? Have they contacted customer support, applied for anything online or visited a branch to discuss concerns or opportunities? Have they opted out of receiving marketing solicitations? Are they in good standing with their loans at the bank? Is there another business unit, such as the Wealth and Investment Division, currently in discussions with the customer? 

In a traditional hub-and-spoke tech stack and data architecture, the most informed answer to these questions can be incredibly difficult to get to and time-consuming to piece together for the business stakeholder, given the vast number of data sources required to be referenced and leveraged. In a bank’s highly regulated vendor, technology and data infrastructure, Theresa might not even have access to these individual systems, let alone be familiar with the specific data points or queries that she needs within each. Theresa wants to make an informed decision in a timely manner but she is also under a deadline for this initiative. Rather than slow down the process by submitting queries to IT (who in turn may not be as familiar with the data points or the business case as a whole), Theresa falls back on using her core banking and mortgage servicing platform data tools because they are the easiest for her to access and she knows the most about them. 

While some of the information Theresa pulls from these sources will be serviceable, she will inevitably miss out on critical insights from others: Encompass (LOS), the bank’s CRM, recent online application history, and several other point solution data sets. The opportunity cost of these missed insights might never be measured, and the bank will continue utilizing a hub and spoke, decentralized and un-governed data infrastructure that can’t fully support the data and strategy needs of the organization. 

How could Theresa more easily leverage all her bank’s data, and how would that look different? 

To be able to fully leverage existing and future technology investments, financial services institutions need to build a scalable and flexible data architecture to accommodate all bank 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. 

Building an Enterprise Data Warehouse 

The first step to building a future-ready data architecture is moving away from hub and spoke data management with a cloud-based Enterprise Data Warehouse. 

An Enterprise Data Warehouse helps move banks and credit unions toward a single version of the truth by creating a centralized, scalable architecture that promotes data self-service and data governance. An Enterprise Data Warehouse allows banks and credit unions to: 

• 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. 

Creating an Enterprise Data Warehouse most likely means moving away from a homegrown data estate that puts core banking at the heart of data management. By doing so, banks and credit unions can move toward fully owning their data assets – putting the financial institution and its account holders above the interests of third-party vendors.  

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 Governed Dynamic Ingestion 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 using Talend’s Data Management tools​. 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. 
• Audit and Control Framework – enable targeted, expedited troubleshooting and decrease business outage time with historic and real-time data ingestion information within Snowflake. 
• Snowflake Watch – quickly gauge key volumetric stats with customizable visual dashboards, and drill down to the query- and user-level for deeper insights.​ 

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 PowerBi or Tableau dashboarding, 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.  

Considering the Art of the Possible for a Better Banking Data Architecture 

Back to Theresa.  

With Data Rocket, Theresa has access to revelatory tools and insights as she prepares for her HELOC outreach campaign. Instead of navigating to her core banking or mortgage servicing portal to generate a report of current residential loans, Theresa opens a PowerBi dashboard, where she can easily slice and dice data based on demographics, location, and recent customer behavior across multiple touchpoints with the institution. Instead of looking at data from one source, she is looking at data that was transformed from data sources throughout her bank – and beyond – including: 

• Customer information and deposit history from her core system, Jack Henry; 
• Detailed mortgage origination data from Encompass; 
• Customer interactions over the six months from Salesforce; 
• Online applications that were previously started but not completed from the LOS;
• Segmint KLI data, which shows customers who are interested in home improvement projects; and 
• Equifax IXI data, which provides data about customer financial footprints outside of the institution.  

Across the bank, Theresa’s colleagues in consumer lending, business lending and wealth management have access to similar dashboards with customized insights tailored to their business objectives and strategies.  

Creating a future-ready architecture isn’t easy – but the opportunities created when data is accessible and leveraged are worth it. Passerelle created Data Rocket to accelerate the journey and help banks and credit unions find faster ROI. The launchpad is here – are you ready to Rocket?  

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Greg Spencer is the Director of Strategy, Product and Alliances for Financial Services at Passerelle. He can be reached at greg@passerelledata.com.

• data governance
• Data Rocket
• data rocket for financial services