What to Consider When Building Your Credit Union Data Strategy

Big Data, Analytics, and Business Intelligence have become the most commonly used buzzwords in business. Despite their varying uses and connotations, each boils down to a single proposition: how can we best use our data?

The answer may vary dramatically from credit union to credit union. Perhaps your goal is to improve marketing returns through increased analysis into member product propensity; for others it may be to improve reporting and operational efficiency through automation and data centralization. No matter what the specific aim of your credit union data strategy is, the ultimate goal is always to use data more effectively.

With the advancements in software and technology, business intelligence technology is now within any credit union’s reach. However, simply having business intelligence software does not mean you have business intelligence. Credit unions must invest time and resources to develop a successful data strategy that is both successful and sustainable. Below are several points to consider to help you get started.

Points to Consider for Your Credit Union Data Strategy

1. This is not purely a technology initiative. This is not purely a business initiative.

Many equate business intelligence with software and technology. While this is certainly true to an extent, business intelligence is equal parts business and technology. Successful business intelligence programs are designed to most effectively cater to the data, analysis and reporting needs of business functions of the credit union. However, understanding the technology involved in developing your business intelligence environment is critical to understanding what is possible within the project’s scope of time and budget. A sustainable credit union data strategy leverages key insights held by both business users and technology professionals.

2. Develop a “Data Dictionary”

Do the terms account, member, individual, and household mean different things to different departments within your credit union? Perhaps your MCIF does not include charge-off accounts, yet account-level reporting from your core typically does include these accounts. Member-level reporting may involve different filtering criteria when different departments complete the same request. Does your information systems department report slightly different account totals than your Accounting/GL application?

These subtleties are not uncommon, yet they represent an important aspect of developing your business intelligence strategy: not all terms mean the same thing to everyone. A critical step to a successful credit union data strategy is creating a “data dictionary”. Your data dictionary will include a list of terms used in the business and their specific definition as it will be implemented within your business intelligence program. Key personnel in all areas of the credit union should agree upon these terms. Term ambiguity can cause misinformation and lack of clarity for reports and dashboards – a major detriment to the success of your data strategy.

3. Assess your current business intelligence environment

Being realistic about your current business intelligence and data utilization environment is essential to developing a realistic strategy roadmap. Are users consistently compiling reports? Do relatively simple data requests take days to complete? Are departments storing silos of information in stand-alone Access database or Excel spreadsheets? Do you have staff competent with SQL? All of these questions are essential to assessing your credit union’s current BI environment and BI readiness.

An invaluable exercise in assessing the current state of your business intelligence environment is to compile a “data inventory”. In this “data inventory”, you will identify every data source within the credit union, which department or individual is responsible for that data source, and what type of data is stored in the source. This is often a mix of Excel files, Access Databases, SQL Databases, and various credit union applications (core, residential LOS, residential/mortgage LOS, MCIF, etc.). By compiling the “data inventory” you gain a tremendous amount of knowledge about your credit union’s current data landscape. If you engage with an outside vendor to develop your business intelligence solution – as do most credit unions – the “data inventory” is invaluable in helping your vendor or consultant gain insight into the data environment of your organization.

4. What do you really want to measure?

Data warehouses are often the foundation of a credit union data strategy. In short, a data warehouse integrates data from various sources. The data warehouse is designed for the purpose of reporting and analysis. As such, it is critical to understand what your credit union seeks to measure before beginning to develop a data warehouse.

A great way to assess what your credit union most frequently measures is to compile a “report inventory”. The “report inventory” is a list of all reports that are produced by the credit union. It is best to focus on recurring reports, but it also valuable to understand what types of reports are being requested on an ad hoc basis. In the “report inventory”, capture which department compiles the report, who the report recipient is, how long the report takes to compile, what information is captured in the report, and how frequently is the report needed (i.e. daily, weekly, monthly, quarterly). If you choose to engage with a business intelligence consultant, a “report inventory” allows the consultant to understand the type of data that is often requested and develop the data model accordingly.

5. Are you willing to invest in the long-run?

A successful credit union data strategy is not a short-term solution. It is not like Microsoft Excel where you buy it, install it, and begin using it in a matter of minutes. Business intelligence solutions are best developed in smaller, targeted phases while keeping the larger picture in mind.

For example, the first three months after beginning your business intelligence program might involve much of the analysis as to the current state of your organization’s data environment. Simultaneously, you may be training a select number of staff on how to write SQL queries or develop reports and dashboards. In nearly all data strategy engagements, you should be looking to institute a 24-36 month roadmap.

Now What?

Quite a bit is involved in developing a successful credit union data strategy. It involves a combination of business and technical expertise while simultaneously balancing the goals of your credit union, timeline, and costs. To summarize, when beginning to develop your initial business intelligence/data strategy you should consider the following key points:

  • This is not purely a technology initiative. This is not purely a business initiative.
  • Develop a “data dictionary”
  • Assess your current business intelligence environment
  • What do you really want to measure?
  • Are you willing to invest in the long-run?

Most credit unions are not equipped to manage all facets of a business intelligence deployment. However, by creating a long-term relationship with a committed third-party vendor, credit unions can develop a cost-effective, sustainable, and successful business intelligence environment that accomplishes all of your data-related goals.

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Posted in DW/BI, SQL Tips.