Best practices for data governance
Businesses collect large amounts of data from their operations. Using business intelligence (BI) tools, they can transform this data into insight. Businesses also have employees that want to access this data. Across an organization, teams can use data for many different purposes to improve many different aspects of operations.
The problem, though, is that not every employee and team should have access to all of a business’s data. This is for many very good reasons. Often, it’s a matter of confidentiality; businesses don’t want to make sensitive information public to every employee.
However, it’s also a matter of usability. At large businesses, there may be hundreds or thousands of different data sets to manage. Unless there’s some way to narrow this data, there’s no hope that someone will ever manage to access the data that pertains to them.
What is data governance?
Data governance is the term for the strategies that data experts use to control who can access data. Modern BI tools have robust and flexible data governance tools, which can help business leaders connect the right data to the right people.
Why is data governance important?
There’s an impulse among many new modern BI tools adoptees to largely ignore data governance. For some businesses, it may seem like more trouble than it’s worth.
However, data governance goes beyond just hiding sensitive data from employees who shouldn’t be able to see it. It’s an important element of any modern BI tool integration, and it’s one of the features that help to make modern BI tools truly self-service.
Managing data at scale
One of the major challenges of managing data at a large scale is making sure that data is getting to the right people. A business may collect hundreds or thousands of different data streams, but any given employee will most likely only need a few to do their job.
Timely access to data
Data managers don’t want employees to have to sift through all of a company’s data to try to find the information that they need. Finding valuable data should be easy, so that employees don’t have to spend a lot of time navigating their BI tool to get data.
Governance is an important element of narrowing the data that’s visible to an employee. Using data governance tools, data managers can limit access to data sets that are irrelevant to an employee.
For example, a regional sales manager doesn’t need to see company-wide sales data to do their job. It’s going to be much more useful for them to see the data that only relates to their region. A data manager can restrict this manager’s access to other regions’ sales data so that they can focus on their own region.
Data governance is also important to businesses that want to communicate data to their clients. If a client logs into their BI tool, they should only be able to see their data. It’d be a bad move to give them access to data sources beyond that. With data governance solutions, clients can only see their own data.
Data governance best practices
There are some best practices that data managers should follow to properly construct a data governance system. These techniques will help you to use modern BI tools to their fullest and boost data adoption across your organization.
1. Establish a data governance framework
Businesses have to decide how they want to organize their data governance system. Some businesses will want to use a centralized system, meaning that there’s a central authority who manages data governance for the entire organization. Often, this is handled by a business’s IT team or data science professionals.
Other organizations use a more decentralized approach. In this framework, departments and teams have more control over their own data governance. It’s often more agile, and is useful for those without dedicated data professionals, but it can be more prone to error.
2. Focus employee’s data views
Many modern BI tools beginners want to know whether it’s better to give employees a narrow data view or a broad data view. The answer is that it depends on an employee’s role and how much a business expects them to act on their own initiative.
For many employees, a narrower data view will be the best choice. With a narrow view, employees are only able to access the information that’s most important to them specifically. They don’t have a lot of opportunity to look at data sources that don’t apply to them.
In some ways, this is good, since employees aren’t getting distracted by other data and can focus fully on the tasks that you’ve assigned to them. However, it can limit out-of-the-box thinking and discourage collaboration.
Some employees will need a more broad data view. A broad view usually means that an employee has access to more general data, and data sets that don’t specifically apply to only them.
This allows employees to use more data to make their decisions, and also gives them more tools to build their own, personalized dashboards and visualizations. However, some employees might get overwhelmed and need a more focused view.
Regardless, the important thing to remember is that employees should have focused data views. They should only see data that’s important to their role. Even if a sales manager has a very broad view of sales data, they still probably don’t need access to IT data or HR records.
3. Use tools in a consistent way
Modern BI tools have many tools that affect data governance in one way or the other. Some of these are very simple, like admin privileges and sharing cards, and others are more complex. A business will probably use a blend of different solutions to build its data governance solution.
Since there are so many different options for restricting or granting access to data, it’s very important that data managers roll out their data governance solutions in a consistent way.
If a data manager uses one method to grant or restrict access to a data set in one situation, they should aim to use that same method in similar situations. For example, if a business uses modern BI tools to restrict access through an embedded portal, then they shouldn’t also give clients modern BI tools credentials to log in and see PDP-restricted data.
This way, if there’s an issue with data governance, data managers don’t have to check every system to figure out what’s gone wrong. Instead, they can check the system that would govern data in that case and see if there are any issues.
Some tools are better for governing how data is shared between individuals and teams. For example, users can share cards and datasets with other users, and give them access to data that they might not have access to otherwise. Users can also export data out of modern BI tools and share it that way.
Other tools are better for managing teams and clients. PDP, which stands for Personalized Data Permissions, allows admins to apply data permissions programmatically to large groups of users at once. Businesses can also set up embedded portals using modern BI tools, and control login credentials that way.
Data governance is an important element of any business’s data strategy. At its simplest level, it prevents users from seeing sensitive or irrelevant data. Used properly, it can shepherd users towards better uses of data by focusing their data view.
Modern BI tools have a large and complex set of data governance tools. To ensure that their data governance strategy is effective, businesses need to follow a few best practices for using these tools. They need to properly build a data governance framework and focus their employee’s data view.
Data managers should familiarize themselves with modern BI tools’ data governance options. Modern BI tools have data governance tools for basically every possible use case, but they need to be applied consistently for best results.