What are the most common mistakes new data governance leaders make?

1 Mistake 1: Not having a clear vision and strategy

One of the first steps in data governance is to establish a clear vision and strategy for your data initiatives. This means defining your objectives, scope, priorities, and expected outcomes. Without a clear vision and strategy, you risk wasting time and resources on irrelevant or conflicting projects, losing stakeholder support and engagement, and failing to deliver value to your organization. To avoid this mistake, you should align your data vision and strategy with your business goals and values, communicate them effectively to your data stakeholders, and monitor and measure your progress and impact.

2 Mistake 2: Not involving the right people

Data governance is not a one-person or one-department job. It requires collaboration and coordination across different functions, levels, and domains of your organization. You need to involve the right people who have the authority, expertise, and interest in data governance. These include business users, data owners, data stewards, data consumers, data analysts, data engineers, IT staff, legal and compliance officers, and senior executives. Not involving the right people can lead to data silos, data quality issues, data security breaches, data misuse, and data resistance. To avoid this mistake, you should identify and engage your data stakeholders, assign clear roles and responsibilities, establish data governance committees and councils, and foster a data culture.

3 Mistake 3: Not defining and enforcing data standards and policies

Data standards and policies are the rules and guidelines that govern how data is created, stored, accessed, and shared in your organization. They ensure that your data is consistent, accurate, complete, reliable, and secure. Without defining and enforcing data standards and policies, you risk having data chaos, data errors, data duplication, data breaches, and data non-compliance. To avoid this mistake, you should define and document your data standards and policies based on best practices and regulations, communicate them to your data stakeholders, implement them using data governance tools and technologies, and audit and review them regularly.

5 Mistake 5: Not managing data governance as a continuous process

Data governance is not a one-time project or a static framework. It is a continuous process that adapts to the changing needs and demands of your organization and the external environment. You need to manage data governance as a dynamic and iterative process that evolves with your data landscape, business goals, and stakeholder expectations. Without managing data governance as a continuous process, you risk having data governance gaps, data governance fatigue, data governance obsolescence, and data governance irrelevance. To avoid this mistake, you should manage data governance as a cycle that involves planning, implementing, monitoring, and improving your data initiatives, use data governance metrics and indicators, and solicit and incorporate feedback and suggestions.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?