3 Change Management Techniques for Data Governance

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Why is change management always such a challenge with data governance initiatives?

When I meet with organizations, including very successful ones, the ability to manage change related to data is sub-par at best. Master Data is unique because it touches every aspect of an organization.  It’s the bloodline which drives operational efficiencies, trusted analytics and reporting. Without master data, companies cannot make fact-based strategic decisions. With so much at stake, why do organizations continue to fall victim to a one-size fits all change management approach?

While DATUM, an Information Management Solutions Company, largely follows proven change management techniques like role mapping, communication planning and training components, we have also identified some unique aspects of a data governance program that require a more targeted approach.  The three specialized areas of focus we use when tackling change related to data governance are:

1. Crafting the Change Story

Crafting and communicating a ‘Change Story’ is a critical part of any successful program. However, the cross-functional nature and nuances of a data governance program require a slightly different approach. Each functional role within a data governance operating model has unique value drivers. Therefore, it is critical to empower each area of the organization to write their own chapter of the ‘Change Story’ highlighting functional-specific change requirements. When users have the power to choose for themselves, they are far more committed to the program and the desired outcome (by a factor of ~ 5 to 1). These stories should be blended into a cohesive package tied to the overall business objectives. 

2. Reinforcing Outcomes

Users are usually only motivated to adopt a new system, methodology or process when they receive direct benefits from it. Therefore, when implementing a data governance program, it is important to always identify, capture and continually re-inforce the outcomes first and then relay the enablers (people, process and technology change agents) that will provide value to the users.  This subtle difference keeps program participants motivated and provides them with ‘good’ news to take back to their business partners and colleagues.

3. Measuring Readiness

The final aspect of an effective change management program for data governance is measuring readiness.  Again, the complex, cross-functional nature of master data requires us to fully measure the readiness for change.  At a stakeholder level, it’s often difficult to articulate readiness for these initiatives beyond verifying system changes and measuring training course completion.  Our new approach places a heavy emphasis on measuring the readiness and integration of each function (both internal and external) to ensure that full process orchestration and business process optimization is achieved.

These three aspects have proven to help companies establish and implement their data governance strategy through effective change management techniques that are focused on gaining the support and dedication of cross-functional business users. Tailoring the change story, emphasizing benefits and gauging functional readiness will allow seamless execution of a data governance strategy and ensure that the importance of data integrity will be recognized.

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