What you’ll learn: Find out what data governance is and how good data governance practices can help collections companies manage the considerable, emerging risks in ARM industry data collection practices.

Customer preference is shifting towards digital channels, which has led to exponential growth of data that’s generated and retained as a part of the collections lifecycle. Of course, data risk has always existed in the ARM industry. But the sheer number of new data elements correlated with categories like compliance, consent, security and classification is a real concern and a new source of considerable risk.

Failing to manage those risks is a bigger problem every day. That is precisely why financial services companies in asset receivables need data governance.

What is data governance?

Here is one definition:

Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals. It establishes the processes and responsibilities that ensure the quality and security of the data used across a business or organization. Data governance defines who can take what action, upon what data, in what situations, using what methods.

Here’s a data governance pro-tip from Isaac Sarcolick:

“People, and especially leaders, don’t know what data governance means, and often put up mental walls whenever they hear the word “governance.” Governance is bureaucratic. It slows organizations down and creates tension. Why would anyone want to back a governance initiative, especially one tied to data? So it might be better to explain and sell data governance by the sum of its parts, rather than as a multifaceted program.

So, what’s the point?

  • A common understanding of data: Provides a consistent view of and terminology for data, while allowing individual business units to retain flexibility. 
  • Improved quality of data: A plan or approach to ensure data accuracy, completeness, and consistency. 
  • Data mapping: A method for helping your decision makers and analysts understand what data is available to them and where they can find it.
  • An agreed upon “single version of the truth”: Create an appropriate level of consistency across entities and business activities for critical data collections.
  • Compliance: If you’re in the collections vertical you already know how important consistent and reliable data is to achieving and maintaining compliance.
  • Improved data management:  Establishes codes of conduct and best practices, making certain that the concerns and needs beyond traditional data and technology areas, e.g. legal, security, compliance, are addressed as well. To add a finer point to these definitions, data governance establishes policies and procedures around data, while data management enacts those policies and procedures to compile and use that data for business use. 

Data governance is intended to facilitate the supporting functions that organizations require to work towards the goal of creating a self-sustaining business function to manage (or, govern), the risk and compliance associated with a company’s data assets in an effort to deliver optimal business value.

How do I know where to start?

Success of an organization’s data governance program is most commonly measured by plotting the company’s location on a maturity model, such as this one published by Informatica. Holistic Data Governance: A Framework for Competitive Advantage leverages Carnegie Mellon University’s popular Capability Maturity Model Integration (CMMI) process improvement approach to define six broad stages of maturity.

Okay, this sounds like a lot of work

It sure is. Undertaking and implementing a data governance program is difficult and there are a number of obstacles that may stand in the way of even getting started. Some of the biggest challenges are:

  • How to communicate value to obtain executive buy in
  • Establishing business line data stewardship throughout the organization
  • Documenting everything
  • Ensuring that IT doesn’t become the default owner of all things data 

Good news, though! There are resources, like this Informatica eBook that offer practical approaches to how to get started on your data governance journey through an iterative process, which can help demonstrate the value of data governance efforts without immediately having to tackle all of the elements with limited resources.

The method prescribed begins with defining the “right” projects with which to begin the journey. The “right” project is defined by selecting a data challenge that is large enough to deliver results with material value, but small enough to use only available resources. Once this is established, you can layer in an Agile iterative process on top of that model to help organize data-centric projects that fit the criteria.

Continue iterations as long as it takes to reach some level of critical mass, and then, hopefully, you’ll have what you need to justify additional investment and resources. Per the eBook, you’ll know when you reach critical mass when the project has:

  1. Delivered some form of ROI, whether measurable or at least anecdotal at this stage
  2. Gained the backing of internal influential evangelists
  3. Been recognized by many in your organization as a best practice

Data governance is complicated. But it’s a virtuous pursuit, and as we continue to collect more and more data, it will become a crucial part of many company’s compliance, strategy, and security. Just take it one step at a time.

Drew Marston is the Senior Director for Digital Integration at Resurgent Capital Services, where he advocates for utilizing technology to enable transformational change and optimization across all aspects of the consumer collections lifecycle to create a meaningful, fully integrated digital channel communication experience for both external customers and internal consumers.