In the sprint to keep up with industry-shaping data disruptions, companies are investing major funds, time and effort to rethink their data governance strategies. The sad truth is many will fail by neglecting the human side of change — the key element to propel the entire data governance lifecycle. We explain how to avoid this pitfall with examples of organizational change management to drive related mindsets and behaviors.
Companies are seeing the transformative power of data-informed business practices and want in on the gold rush. Many are establishing (or overhauling) their data governance programs to extract greater value from their data assets. But no program can fix a company’s data woes if it overlooks the most important aspect of data governance: buy-in from the people who guide and use their data systems.
That’s where leadership and organizational change management (OCM) practices are crucial to achieving the intended business outcomes.
More than ever, we hear “change is the new norm,” or “change is inevitable,” and so on. Yet, statistics show that most business transformations fail to reach their desired business outcomes. In fact, business value comes when organizations successfully implement and adopt change. Projects (like new data governance technology) with adoption and change management produce as much as seven times greater outcomes compared to those that do not have those practices embedded into the program.
Solid change management strategies account for sponsors and leaders of impacted teams to communicate, provide support, and remove barriers — whether within a new implementation or for improving your current data governance lifecycle. This intentional approach should work in tandem with the project or governance team so you don’t overlook the human element.
What Is Data Governance?
Data governance involves setting rules and protocols, informed by strategy, for how your company should manage its data. It might include the creation of:
- Definitions for key terms.
- Data standards.
- Security protocols.
- Processes for sharing data.
- Systems for quality control.
These measures can help maximize data value while minimizing data risks, but they won’t happen without adoption by people who manage and make decisions based on the data. Real data governance is a thoughtful process with a full lifecycle — from brainstorming to planning to implementation, evaluation, and reform. Applying a change management framework throughout the data governance lifecycle will help to bring employees along proactively and inclusively.
“What’s in It for Me?”
Effective data governance increases alignment between employees’ daily tasks and the company’s strategic aims. When implementing data governance programs, we often hear the expression, “What’s in it for me?” (WIIFM). If people understand the WIIFM, adoption tends to go a lot smoother.
From an employee perspective, one perk of an OCM-informed data governance approach is the potential to work smarter, not harder. If you can optimize the tools and processes people use to do their jobs, you can often streamline their workflows, boost their output, and enhance their performance. In other words — potentially more pay, less stress.
An OCM-informed program can help folks stay out of trouble, too. Data governance programs should address a company’s compliance with data regulations, industry standards, and internal policies. By being involved, employees can better understand their role in protecting the company and themselves against legal and regulatory risks.
One example: If you loop nurses and doctors into clinical data governance, you can show them how their daily charting and workflow habits could put them at risk for breach of patient confidentiality – or potentially save them (and your institution) from nasty malpractice suits.
Finally, because OCM is designed to infuse company culture with both a data-informed and human-centered mindset, it leads to more fair, equitable and transparent work environments. For instance, if employees are directly involved in HR data practices, they can help to create appropriate performance benchmarks and transparent pay and bonus systems for all.
How Change Management Can Propel Data Governance Success
While the technical aspects of data governance are crucial, the success of any program hinges on people’s willingness and ability to adopt it. Apply change management practices to promote data governance adoption with these best practices:
1. Boost employees’ sense of ownership.
Are employees aware of why the change is important and what you expect of them to help achieve the business goals? Data governance typically requires employees to incorporate new tools, technologies and procedures into their workflow.
Beyond informing people about the change, solid OCM practices encourage feedback and measurement of the change process itself (i.e., listening and responding to people’s concerns, questions and suggestions). This step enables key employees or teams to be part of the solution and increases their buy-in. Asking people for input early and often about a proposed change is one of the most valuable ways to ensure success and sustained practices.
2. Ensure awareness across all data stakeholders.
Internal communications can make or break the roll-out of a data governance program. Have a leader-aligned plan of your program’s objectives, goals and benefits — and employees’ roles within it — at routine, predictable intervals. This alignment helps maximize awareness and minimize resistance that too often accompanies siloed data governance implementations.
3. Sustain change over time.
By weaving new behaviors into company culture or proven processes and people, the implementation leads to lasting habits rather than short bursts of activity. Through tactics like positive feedback loops, including key contributors in the solution development, and reward mechanisms for behavior change, you’ll see changes adopted over time as part of a sustained governance program.
More Specific Change Practices to Enable Success
Applying change practices within your data governance program brings a people-centered approach versus only high-level communication in the hope of positive reception. Below are specific examples of how it plays out within a new implementation or when updating your governance lifecycle:
Intentional Change Leadership
Change won’t last if leadership fails to embody it. While a project may and should have a primary sponsor, all leaders of impacted teams need to contribute to delivering key messages and local decisions or supporting new processes. This should be more than sending emails or speaking in town halls but promoting and attending training or testing sessions and providing expertise to solve data-related problems along the way.
What will the program deliver, specifically, to reach the desired business outcomes? This definition, alignment across leaders, and a corresponding message strategy to communicate those objectives to all stakeholders should take place in early change strategy and planning.
Identify the key groups (stakeholders) the data governance initiative will impact. Create a stakeholder map like this one to determine how much influence they’ll have on the program’s success. Use these insights to plan strategic engagements with the groups at each step of the data governance lifecycle.
Employees need assurance that you will provide training or self-guided learning opportunities. Include training and educational materials in your communication plan. Conduct workshops, webinars, lunch-and-learn sessions, and more to educate stakeholders about various aspects of the data governance program. Effective learning within change projects means investing in time and resources — pennies on the dollar compared to unprepared, frustrated individuals and teams.
Gather feedback from different stakeholder groups based on timing and those most impacted. You can do this through open forums, Q&A sessions, surveys, or dedicated email addresses. But it’s not enough to just collect their input. Make sure to capture their concerns or ideas to help resolve issues (even if you disagree), follow up with them on the findings, and weave workable viewpoints into your program where helpful.
Craft core messages about the importance of data governance, its benefits, and the expected impact on people’s work. Tailor these messages to suit different stakeholder groups. Share them with each respective group in clear and concise ways. Consider using a combination of channels and messaging styles to maximize your reach.
Share updates at routine, predictable intervals, such as by presenting at recurring staff meetings. Outline when and how often communications will take place, and stick to the schedule. Include major milestones, deadlines and training sessions in the schedule and announce these beforehand.
Evaluate your initiative continuously. Evaluation can include:
- Stakeholder engagement (like focus groups and pilot testing).
- Data collection (surveys, problem-solving exercises)
- Communications metrics (email click rates, page views, or gaming participation)
- Program outcomes (ROI, error tracking, fill rates, or quality metrics).
Adjust strategies based on the insights uncovered.
Effective data governance is an ongoing process. When you approach it with proven change management practices, you open lines of communication and can quickly increase adoption to support the organization’s evolving needs.
Data governance is rarely easy, but when done with a people and change built into the approach, the more likely you’ll reach your desired business value and achieve success. Does your company put people first in its data practices?