Modern Credit Union Data Stack

Building a Modern Credit Union Data Stack on the Cloud

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Building a Modern Credit Union Data Stack on the Cloud

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Just like every other industry today, credit unions are working to figure out how they can best utilize member data to optimize their experiences. But credit unions do face some unique challenges around privacy regulations, member service expectations and an often traditional hardware and software data infrastructure. We interviewed Adam Roderick, CEO @ Datateer, to learn how credit unions can make data more useful and valuable to their organizations.  

How can a modern cloud-focused data architecture enable credit unions to better serve members or optimize their businesses?

Visibility and speed.

Credit unions have many different types of products and stakeholders. This necessarily means they have many processes in place, with applications and databases to support them. Each of these systems contains a partial view of the organization. A siloed slice of the whole, a glimpse.  

But none of them provides a complete picture, an ability to answer questions using data from multiple sources.  

Efforts like Member 360 bring data together from multiple places into a single, centralized location. This creates visibility across the entire organization! Questions that previously could not be answered, or took days (or weeks!), can now be answered real-time, on demand.  

It is a magic moment when something like that comes to life. When you can see trends and compare metrics. When you can explore and get a clear picture of the credit union’s members and operations.  

So much of the sluggishness in any organization is due to lack of information, or slow information. And most of that is because data is scattered across so many places.  

What are the biggest challenges credit unions face when building a modern data architecture?

Focus and traction.

Regarding focus, the biggest challenges have nothing to do with technology or data. There is a tendency to want to boil the ocean–to make a large, encompassing effort. The organization buys into the big vision, and then tries to execute a huge, complicated project. You can imagine the results. I don’t think this is unique to credit unions.  

On the other hand, the sheer size of potential impact and number of potential applications of data analytics can create paralysis.

The need to focus is critical to getting early wins, building momentum, and growing in maturity and capability. Think big, but take small steps, learn, and iterate.

At Datateer, we follow a framework we call Simpler Analytics. A core tenet is to treat data sources, metrics, and data products as assets–trackable things with lifecycles and measurements of their own. And it ensures each data asset is aligned with a particular audience and purpose it is intended to serve.  

Regarding traction, the challenge is how to get moving and stay organized. With so many moving parts, any data architecture is at risk of becoming bloated, cumbersome, and difficult to maintain.  

How many potential questions could data answer in a single credit union? How many KPIs and metrics might be part of a mature system? How many reports, dashboards, embedded analytics, or other data products?

At Datateer, we address this complexity in two ways.

To get going, we treat each new effort with a crawl-walk-run approach. Anyone surveying the modern data marketplace will quickly become overwhelmed with all the tools. But the basic modern data stack is proven and not complicated. I described an approach for this, including product recommendations, in a recent article.  

As things mature, the number of reports, metrics, etc. can get out of control. This can happen relatively early. We rely again on the data asset inventory I mentioned earlier. This inventory keeps things manageable.

It can get overwhelming as reports and dashboards proliferate, more tooling gets implemented, more processes and procedures are put in place. All of these artifacts and procedures can reference the data asset inventory to stay aligned with what matters and provide a point of reference.

What data governance and security implications or requirements are especially critical to credit unions’ data modernization projects?

Security and governance boil down to ensuring data is available to only the right people, for the right reasons, at the right time.  

The biggest challenge here is the balance between privacy and making good use of data. While governance and policy are essential, they slow things down. It’s necessary to find a balance between compliance and risk mitigation, with moving forward and making an impact.

Good security practices can only go so far. Up to 88% of data breaches are caused by human error. With the personal and financial data credit unions must protect, a culture and training around data protection are critical.  

A solid data governance plan provides a backstop against human error.

What advice would you give on how or where credit unions should start on their data modernization journey?

Your credit union will have unique requirements, but not at the foundational level. Embrace the basic modern data stack and get moving. Don’t get hung up on defining everything up front.  

Stakeholders no longer have the patience to wait months for a big project to show results. And, you don’t have to go it alone. Datateer pioneered the concept of managed analytics and fractional analytics teams. Our model allows companies to get going quickly and confidently, creating business value immediately.  

Embrace the cloud. It’s where all the product and tool innovation will continue to happen. Many people get hung up on two risks: potential cost overruns and security concerns.  

What I see for mid-sized credit unions is actually the opposite.  

  • First, scalable cloud pricing allows organizations to get into the game at a lower price point than any alternative. As you derive value from your data modernization efforts, you can gradually scale up your initiatives and expand their impact. Horror stories related to spiraling costs are not the norm.  
  • Second, cloud security has matured so much that in my opinion it’s better than what the typical mid-sized organization is going to be able to do on their own. Following best practices around cloud security is actually more secure than a home-grown infrastructure strategy.  

What are some of the tools, solutions or partners credit unions can leverage to make their path to the cloud easier, smoother, or more secure?

I conceptualize modern data architecture as a set of components. Each has a responsibility and a set of tools and processes.  

You may come across the phrase “modern data stack” which treats these components as layers that stack or build on each other.  

Here is how I define the core components:  

  • Replication is the first piece. This is extracting data from operational systems to centralize it. Good commercial vendors are Matillion, Fivetran, and Portable, and we often use Meltano or Airbyte if we need something custom.  
  • The warehouse is the central place to store and analyze data. Datateer supports Snowflake and Google BigQuery (after a lot of trial and error with some others)
  • Transformation is combining data from multiple sources into a single data model, shaping it for your needs, and calculating metrics. Matillion is interesting because it can do replication and transformation, simplifying a lot of your set up. dbt Labs is another solid choice
  • Orchestration is coordination, scheduling, triggering, and failure notifications. Matillion handles this, and Prefect and Dagster are good options too
  • Governance ensures data is usable by only the right people, for the right reasons. ALTR makes this a breeze.
  • Business Intelligence is the reporting and dashboarding. We recommend Sigma Computing and Hex to cover just about every scenario. We have an evaluation matrix of almost 50 tools if you really want to get into details.  

How do you predict the data landscape will change for credit unions in the next 3-5 years?

I hope that the credit union cloud adoption trend will continue. The potential benefits are huge for speed and visibility into members and product performance.

Credit unions that focus on Member 360 efforts will have such an advantage over those that don’t invest there. Member 360 is just a buzzword, but it encompasses efforts to truly understand each member by bringing together data from multiple areas of the organization.  

Credit unions that do invest here will try to balance the insights data analytics can provide with the personalized service and relationships they already have with members.  

Data replication will become easier. Some large vendors are already sharing data into your warehouse automatically–no replication needed.  

Last, we will see generative AI become a natural language interface on top of data, allowing easier and faster access to information. This will be exciting because it opens up data to more people. But it will only be useful to credit unions who have a solid data foundation already in place.


Adam Roderick is CEO of Datateer, where he helps companies make their data useful and valuable. He is the creator of the Simpler Analytics framework and the founder of the Open Metrics Project. Adam lives in Colorado with his wife and five amazing daughters, raising them to live life to the fullest in one of the most beautiful places on earth.  

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