Taking Your First Steps Toward An Effective Data Governance Strategy

Image_Taking First Steps

Although we don’t remember when we first began to walk, we do know it required a great deal of practice. It’s a balancing act that takes time and patience to truly perfect. Similarly, implementing a data governance program can be a very overwhelming initiative for any organization. Developing a well-defined governance strategy to deal with the ever-increasing amounts of corporate data is no small task.  Like walking for the first time, getting data governance right takes a lot of learning and growing, with bumps and bruises along the way. However, to help get started on your data governance journey, here are a few steps you can take to get you walking in no time.

Perform a Detailed Assessment of Your Corporate Data

This is to no surprise; conducting a data assessment prior to embarking on a data governance initiative can provide your organization with greater visibility of that data and the areas that are of risk to the business. It allows for a complete view of each business area and how they are using and interacting with the data. There are several areas that the data assessment will inspect, including duplicate or inconsistent data within and/or across systems, current processes and responsibilities for test and audit systems, and discrepancies in account data that undermine financial reporting. In order to proactively protect the business from potential interruptions and failures, the data assessment is an important first step to properly identify and determine the type of data governance strategy that is needed. 

Determine the Maturity of Your Data and Build a Business Case

At this point, you’ve now taken your first initial step and got your detailed data assessment out of the way, which surely revealed some bumps in your data that need immediate first aid, but the next step is where you can really show off your discoveries.

The next step in your data governance journey requires a look at the organizational structure and what processes are already in place around data management. The most highly effective organizations have equally as effective processes in place, so this step is especially important. There are three key objectives that need to be followed to properly build your business case.

  1. The first is to have strong executive support by properly communicating the business value that will be achieved from your data governance endeavor.
  2. Secondly, when building a case for data governance, data should be looked at through the perspective of specific areas, such as compliance, analytics and reporting, and HR. Honing in on each area will help with prioritization, as a governance strategy will be unique to industries, as well as organizations. 
  3. Lastly, formalize your business case. At this point, being able to articulate what the financial impact of not implementing data governance will have on your organization is crucial. On the other hand, you will also want to quantify the expected payback you expect to receive from your data governance initiative through such areas as productivity, new revenue and cost avoidance.

Select a Data Governance Strategy

There are four levels, or models, of data governance and your organization must determine which one is right for you. 

The first is of course no data governance. Commonly referred to as the “Wild West” approach to governance, this option involves the most risk as it assumes that all users will enter data accurately, on-time and in compliance with corporate SOPs.

The second model places the responsibility for data governance on the Center of Excellence (COE). This model tasks a central group with the responsibility of creating and verifying all data requests before posting them to a system.

Then there’s passive data governance, which requires users to enter data into a system, and then a toolset or reporting mechanism iteratively identifies data-related errors within that system that automatically reports back to their authors for correction.

The final level is active data governance, where all data required to support the configured business processes is collected prior to posting into target systems and validated automatically through a collaborative environment. This eliminates the possibility of business-process interruptions due to duplicates, inconsistencies or lack of standards.

Now look at you move! You’ve taken your first initial steps, fell down a couple of times only to pick yourself back up again to discover new strengths and weaknesses, but only time and endurance will determine if you continue your data journey moving forward. Now that you have your legs under you, the next steps are to implement, manage and evolve your data governance strategy. Find out how your journey continues by reading our Masterminding Data Governance eBook.  

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