Data-Driven
6 best practices to improve change management at scale
When you implement any kind of data governance practice, it’s crucial to consider how your policies and procedures will evolve over time. As your organization grows, so does your data. It makes sense that you must regularly iterate and improve your data policies to match.
That’s where change management comes in: a framework for managing changes to your data governance policies and procedures. In this post, we’ll run through change management as an end-to-end process. We’ll cover what it is, why it’s important, and the best practices for ensuring good data change management.
Ready? Let’s go.
What is change management?
Change management is a systematic approach to overseeing any changes to policies and procedures within an org. It's all about supporting and preparing your people to transition from your current state to a future state.
For data governance, change management helps you effectively manage changes to your data policies and procedures. This can include updating permission and access levels, implementing new data quality controls, or establishing new data governance roles and responsibilities.
Why does change implementation often fail?
If you want to implement any kind of change in your business, you must first determine why the change needs to happen in the first place. What are you trying to achieve? How will this change benefit your business?
Once you’ve settled on the overarching goal for your initiative, you can then put a plan in place. Knowledge of typical missteps can better inform your change management strategy.
Here are some common reasons why change failure occurs:
Poor communication. When people are not properly informed about changes, you risk misunderstanding, which could inspire employee resistance.
Lack of management buy-in. Without the support of upper management, it can be difficult to get the necessary resources and alignment.
System integration issues. When new changes can’t integrate with your existing systems, it can lead to technical problems and have a negative impact on your operations.
Inadequate training. Not properly training your employees on the change can lead to confusion, mistakes, and ultimately failure.
Lack of clear definition of what to change to. It’s important to communicate the desired end goal. Without it, it’s difficult to create a plan and measure success.
6 best practices for great data change management
Good data change management is crucial for the success of an organization's data governance policies. Here are some practical steps that you can take to ensure good change management in your data governance approach.
1. Roll out your change initiative in phases
Without a robust change management strategy, you could negatively impact your business. You risk moving too fast during a delicate transition period.
To manage any data changes, it’s essential to follow these three phases:
Plan phase: Define project success criteria and align them with your overall goals. Review your data maturity model and identify goals for the next 12-18 months.
Launch phase: Implement your change management strategy. Start by consolidating your plans. Then, give early adopters access to the tool while establishing a feedback loop for continuous improvement.
Scale phase: Use the feedback from early adopters to make necessary improvements. After that, you can roll out the change to your entire organization.
2. Maintain one source of truth
It's crucial that everyone uses the same data to make decisions. By storing data in a central location, you can ensure everyone is accessing the same information. It also makes it easier to keep data clean, normalized, and consistent across all data sources. Designate roles and responsibilities for stakeholders.
3. Set roles and responsibilities for all stakeholders
You need to define what every stakeholder owns in your change management process. This will ensure that your data is managed in a consistent and controlled way.
You can learn about the most commonly assigned roles and responsibilities in our other blog post.
4. Review and approve updates to your dataset– always
It is important to review and approve any updates made to datasets before implementing them across the organization. This includes adding or modifying event definitions and names, or deleting/archiving events. This is particularly important when dealing with multiple data sources, as the names must match downstream in the analytics tool.
Don’t forget to communicate to stakeholders which events have been reviewed and approved so they use the right events and properties in their queries.
5. Maintain history logs
History logs provide detailed records of all changes made to your data and the associated policies and procedures. This helps you easily identify and troubleshoot any issues that may arise, such as data quality problems, incorrect data usage, or policy violations.
6. Regularly audit your dataset
Scheduling regular dataset audits ensures data accuracy, relevance, and reliability. As time goes by, data can become outdated, inaccurate, or redundant. Regular audits help proactively identify and address these issues before they become problems. This helps keep data valuable for decision-making.
Audits involve reviewing data for accuracy and relevance, identifying any issues, and taking corrective action. Cleaning up redundant or outdated data can also help streamline data processing and reduce storage costs.
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When it comes to change management, you can’t just set it and forget it. It’s important to consider how your policies and procedures will evolve over time. It’s your trusty framework for managing any changes to your data governance practices so you can keep things sailing smoothly.
Don’t underestimate the power of a smooth transition from old processes to new ones. By following the best practices laid out in this post, you can ensure your organization continues to grow and scale, all while achieving goals with valuable insight.