In my previous blog post, we talked about:
What data governance is
Why data governance is so important
How different capture methods impact your day-to-day operations
How to use data governance when it comes to a digital event
To recap, data governance is an ongoing practice that ensures your data is accurate, consistent, and secure. It instills a sense of trust in the data you’re using.
But like all things, there are different levels to consider. That’s why this blog post covers the different data governance approaches you can apply in your organization: conservative, liberal, and blended. We’ll explore the different access levels with each approach, as well as how to manage varying roles and permissions within your team. By the end of this post, you’ll know which approach to data governance is best for you and how to implement it effectively.
Let’s dive in.
1. The conservative approach
The conservative approach is the strictest approach you can take to your data governance strategy. This approach restricts the majority of users to view or read-only permissions, with higher-level access reserved for more senior roles: team leads, dataset owners, or lead analysts who have demonstrated sufficient analytics knowledge.
Larger organizations and enterprises favor this approach because of its minimum risk.
2. The liberal approach
The liberal approach gives the majority of users permission to edit and access your dataset, whether that’s updating information or defining events, queries, and dashboards. The liberal approach is the most trustworthy approach, with organizations allowing members to largely self-govern and manage the shared dataset or spaces on their own.
This approach works best for smaller, leaner teams that err on the more technical side. Because all team members will be given Analyst-level access, these same members must have previous experience with analytics tools and high data maturity.
3. The blended approach
The blended approach is a mix of both conservative and liberal measures. Team leads will be given access to shared data and definitions, and may provide that access to their team on an as-needed basis. New team members will default to viewer access.
The blended approach is the best option for growing teams and organizations who want to mitigate risk when it comes to their data governance practices, but also want to enable ownership and innovation across their team.
Different roles and permissions for your data team
After reviewing your current governance practices, it’s time to reevaluate user permissions and roles. Before you do this, it’s important to make sure you understand what each role is and what permission level they should have within the account. Here are some key roles and descriptions:
Once you understand the roles, you can then re-assign permissions and restrict event creation and modification permissions in the shared space. Doing so will help you maintain data trust across different teams and roles.
Here’s an example of what that looks like across your team:
How to divide permission levels across teams
Here is a breakdown of how each data governance approach can be divided across your teams and different roles:
What criteria will you use to decide what permission level users should have? Check out this breakdown of basic criteria:
Choosing the right data governance approach for your organization is crucial to maximizing the value of your dataset, and ensuring the speed and accuracy of analysis across your entire organization.