Closing the Gaps: A Familiar Scenario of Incomplete Data Governance - Blog

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Closing the Gaps: A Familiar Scenario of Incomplete Data Governance

It's not why your credit union wants better governance; but why wouldn't it?
As you are all too aware, a credit union, or any financial services company, can no longer provide minimal products or minimal services and expect to retain the member’s attention for long. Due to consumer services like Amazon, Netflix, and Uber, members now either consciously or unconsciously expect personalized online experiences based on their preferences, past activity, and life events.

The reality is that there is no single provider that offers a full suite of systems for a credit union to service its members. During our Data Assessment services at BIG Consulting, we have found that the number of sources and systems starts at around 35-40 for small credit unions with $250-400 million in assets and easily rises to 65-70 systems for credit unions with $2 billion to $4 billion in assets.

Due to the high number of systems and vendors, it is nearly impossible to create a personalized experience for credit union members without major expense and to guarantee quality of data across all those systems. Additionally, credit unions have cobbled together spreadsheets, department-level data marts, and in some rare cases, a partial or entire data warehouse. These are all to get a picture of their membership’s balances, levels of charge off risk and other marketing opportunities. 


However, when comparing reports from different systems, the results do not match. This is natural because each report may have different time windows, different criteria built behind it, and different logic on how members are brought together to form a picture of the accounts those members have.

  • These differences create major inefficiencies in pulling together reports and making critical business decisions, because time is spent arguing over the report results rather than focusing on the decisions that need to be made.
  • It is nearly impossible to build a datadriven organization that finds insights in one of its most valuable assets, member data, because it is too difficult to pull results together and trust the results.
  • When systems are changed out, the impact of that change may be unknown until it is fully rolled out.

What is Data Governance?

First, data governance can take many forms and there is no one-size-fits-all method on how to do data governance. It must be tailored to your overall business strategy, mission, departmental structure and project methodologies. Here are some of the key components of a Data Governance program:


  • Above all, manage consistent definitions and use of data across the organization to arrive at a “Single Version of the Truth”.
  • Map changes to your business strategy to:
    • Define new or updated key performance indicators that measure your business performance.
    • Enable high levels of member service and risk management like credit risk and fraud risk by acquiring new analytic, predictive and prescriptive capabilities.
    • Take advantage of data assets to streamline your member facing digital channels.
  • Understand the impact of acquiring new systems or changing existing systems to your data analytics infrastructure.
  • Understand and drive the skills required to derive insights from your data.
  • Ensure that your vendor contracts give your credit union rights to access and own the data during and after a contract expires.
  • Verify that employees only have access to data required for their assigned job.
  • Create a collaborative environment so analysts across the organization are not performing duplicate work.
  • Set recovery time objectives for when critical data is taken offline during a system outage.

More Red Tape?

The main concern of CEOs and other business leaders when contemplating a data governance program is “Will it slow our organization down?”. 

If created properly in your organization, there should be a dramatic gain in decision making speed, consistency in results, and less impact with system changes due to better coordination of changes and better day-to-day insights on the front lines while interacting with members.

Many of these activities may already be performed in some form or fashion in your organization, but not organized under a formal Data Governance program. By adding a formal structure around these activities, it will be more efficient and realize a broader goal of improving the data analytics capabilities of the organization.

While developing the program, the full Data Governance Team should meet bi-weekly or monthly to get the program off the ground and then subsequently move to monthly or quarterly meetings depending upon how rapidly the organization’s strategies, systems, and marketplaces are changing. If decentralized, a separate Analytics Team should meet at least monthly to ensure they are fully utilizing the analytical systems, not duplicating analytic work and using the data definitions consistently.

Where Should Data Governance Reside in Your Organization?

Data Governance can be centralized in a team and/or dispersed amongst the organization.


With any program it’s best to have an owner or co-owners. This can be a technical or non-technical leadership role. Typical owners might be the CFO, CMO, CDO, VP or Director of Data Analytics, CLO, CIO, or Enterprise Data Architect. The leader(s) will need time, goals and buy-in from the top of the organization to ensure they will be given the organizational resources to develop the program, as well as drive and monitor its success.

Other Departmental Considerations

  • Other CLevel members whose team’s success is either measured or driven by use of data.
  • Business analysts who derive insights using the data.
  • One or more technology personnel supporting, building and managing your data analytics infrastructure.
  • Any programmers who are integrating predictive models into business processes.
  • Anyone proposing a new system that has data which is important to get a picture of your membership.

End Goal of Data Governance

A more effective data governance team will enable you to convert your organization from a gut-reaction decision making organization that relies on inaccurate or incomplete data, which tends to make improper short-term investments, to an organization that has trusted data to back up its decisions and gain insights into its membership. In the long run, the business will be able to move these decisions from senior level decisions to middle management and subsequently to the frontlines and digital channels where business decisions that impact the membership and bottom line are performed on a day-to-day basis.


Tim Tibbals is a member of the BIG Consulting team which performs data assessments of a credit union’s current business and technical strategies to build a credit union-specific roadmap. This road map enables credit unions to reach the highest ROI on their data analytics, data science and artificial intelligence initiatives.

Data Governance is a key component in the credit union roadmap.

About the Author

Best Innovation Group

Best Innovation Group

BIG is an innovation think tank started by @JBFintech that points its clients in the right direction to achieve quicker adoption of cutting edge technologies.


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