Business Intelligence: how to define a governance strategy

Nowadays, companies are building on their analytics culture and becoming much more data driven. As this transformation happens, there are some points to be aware of, one of which is the governance of all data and content.

Data governance exists to ensure that high-quality, accurate data is available to specific users in the organisation. It creates secure, trusted content for users of all levels of data knowledge and expertise as well as segregating corporate and ad-hoc analysis.

What are the key points you should consider when establishing a governance strategy?

The first thing to talk about when explaining governance is access to content. When you create content like datasets and further dashboards, you may want to share them with your colleagues or specific people from your organisation.

Platforms like Power BI Service allow you to publish your content, but you will then want to make it accessible to specific people. The way this works is through access and permissions. For example, in Power BI Service, you can have all users from your company divided into groups with different permissions, and since Power BI is a Microsoft tool, it can easily be integrated with Active Directory. This means that when you connect Active Directory to your Power BI Service, all users and groups you create or have already created will be nicely synchronised.

Something like this gives you the opportunity to choose who can see the content that was created and published for sharing. Even then, there are levels of permissions which means that while some people can only view, others can edit or enjoy other administrator permissions at your discretion.

Power BI Service has another cool aspect. It is divided into workspaces where you can publish content belonging to a specific project. Imagine that you have a marketing initiative to build a dashboard to evaluate your metrics. The dataset and further dashboards that you’re going to build around this initiative will be on a specific workspace. Now imagine a human resources initiative to evaluate turnover, using another workspace, and so on. This is a great way of organising content in different folders, and you control who can access them and who can do what since every user can be given different permissions on each folder (workspace).

Row-level Security (RLS) can be used to control access for certain users. This functionality restricts access to data and can define filters within the roles or permissions that the users have. This is interesting, for example, when multiple users have the same role, but each one can only see their own data. Power BI escalates well with this, restricting the access that users with viewer permissions have to data. You can configure RLS for a direct connection to your data source or an import of data so you may share your dashboards via Power BI Service.

One of the best things about content governance using a platform like Power BI Service is that you can publish the datasets you create separately from your reports and give your users the capability of building completely different reports or dashboards using those datasets. What happens is that you centralise any dataset you build and every user or every team with the right permissions can access it and use it for different purposes.

When you have your datasets centralised and certified by a data steward, you’ll have “one source of truth”, and your company will be closer to a successful data-driven culture.

It isn’t always easy to create governance procedures.

They must be planned carefully, and visualised well, policies must be defined, and risks must be identified. There are tools that can help you with this. We spoke about content management with Power BI which can be centralised on the Azure Synapse environment, but we need to speak about cataloguing data and further quality.

Microsoft Purview is the tool that Microsoft offers to create a holistic mapping of your data with automated discovery, classification of sensitive data and global data lineage. With this type of tool, you can create flows to leverage the data quality of your information assets, and it is a tool where you can integrate, centralise and certify your data sources to help you discover data and label, secure and manage it properly.

Implementing a governance strategy isn’t easy, but it is necessary. Procedures and policies must be defined for a clean, well-organised process.

In recent years, Microsoft has been working to offer a good environment for companies to develop such governance strategies and further implementations with their tools.

By operating good data governance, you’ll be able to relieve your IT team by reducing their focus on content creation, letting them focus more on architecture, datasets, access and permissions issues. This means that with a proper governance strategy, you can empower your business users with the necessarily certified datasets to easily build reports and dashboards, leaving the IT team focused on governance instead of developing business visualisations.

When you achieve your governance model, we know the results will be promptly visible: more easily accessible quality data, new ease of creating insights and reports and, most importantly, reliable data for decision-making.