Data-driven credit unions can spot the challenges and opportunities ahead of their competition and create unique value propositions and engagements with their members. This promise is leading to increased spending on their data analytics program. However, many organizations struggle to manage their data and make it available to their business leadership clean, trusted data that can be used for decision-making.

In many of my conversations with CU leaders, they ask if their data analytics team is sized right for their organization. In many cases, they compare the size of their squad with peers of a similar size. This is a very tricky question, and I am trying to address it today.

An organization's investment in a data analytics program at a high level can be broadly bucketed into people and technology investments. Building a reliable and impactful data analytics foundation requires good talent. Here is the list of typical roles that are needed for a mature data analytics program.

Building a new home and a data warehouse has some interesting similarities.

Building a home and data warehouse should start with a vision

If making a home and data warehouse is begun without a vision, then the effort will likely result in missteps, failures, frustrations, gets expensive, and takes much longer to get to the expected results.

To build the vision, engage with the executive team and stakeholders from key functional areas, and understand their requirements and pain points. Understand how they use data today and how they plan to use it. CULytics has helped many credit unions engage with their executive leaders and stakeholders to build a vision, strategy, and roadmap for the data program.

Site-built vs. Manufactured Homes

Once there is a vision, a critical decision needs to be made about how you will build your home. Whether it is going to be a traditional site-built home or a factory-built manufactured home, each option has its pros and cons. With a site-built warehouse, you need a larger team (whether you hire the entire team or a technical consulting vendor to provide the resources); initially, it is likely to take longer, and you get precisely what you are looking for. On the other hand, you do not need a big team with a manufactured home, aka a pre-built data warehouse from a full-service solution provider. Since the house has already been manufactured in the factory, the vendor integrates it with your systems. You have limited configuration and customization options. You are likely to see the results faster. However, there is a dependency on the vendor solution capabilities, design considerations, and roadmap.

For many credit unions, a pre-built data warehouse from a full-service solution provider is a good option. This option may give them good value without the hassle of hiring and retaining technical resources in a highly competitive market, usually faster time to value, etc.

CULytics helps credit union leaders in assisting them in understanding the lay of the land concerning the data warehouse build options. We also help credit unions select the right solutions, in a vendor-agnostic manner, by educating them about the criteria that should be used to make the decision. The outcome is that your team is more confident in their decision, which helps your organization get to value faster.

Challenges with Scaling

When you go down a path where you build a traditional site-built warehouse with a small team (without all the key roles), you should also know that it is going to take longer for you to build the warehouse, and if the resources are newer, then there may be some missteps, and it may need some rework. Therefore, if you want to scale faster, you have two options: either invest in a full-service solution that can give your data warehouse and analytics effort a quick boost or invest to fill the key roles.

So, when you think of the team size with your peers, you need to know if they are traditionally building their data warehouse or using a manufactured warehouse.

There are other factors that impact the size of the data analytics team, and I will discuss them in future posts.