Power BI Governance, What Organisations Need to Know

Power BI Governance Art Built by Bing Image Creator

In recent years, Power BI has become one of the most widely used business intelligence (BI) tools. Power BI is more than just a reporting tool; it is a comprehensive analytical platform that enables users to collaborate on data insights and share them internally and externally. In addition to creating reports and dashboards, Power BI allows users to collaborate and share their work with others. For instance, users can share dashboards with their colleagues, allowing them to view, interact, and engage with the data quickly. However, as more organisations adopt Power BI, it becomes essential to ensure appropriate governance processes, policies, and rules are in place. This blog post explains Power BI governance and why business owners need to be conscious of it.

Power BI governance refers to a set of processes, policies, and standards that organisations put in place to manage and control the use of Power BI. Governance is critical to ensure that the use of Power BI is aligned with the organisation’s objectives and strategy, complies with relevant regulations and standards, and protects sensitive data. Power BI governance encompasses several areas, including security, data management, compliance, and user management. It also involves defining data access, sharing, security, and compliance guidelines within Power BI. This includes defining roles and permissions for users, specifying approved data sources that can be used, and ensuring that the data is accurate, up-to-date, and secure across the organisation. In addition, Power BI governance involves monitoring and auditing the use of Power BI to ensure that it is being used appropriately and in compliance with the organisation’s policies. Lack of Power BI governance can impact businesses in various negative ways, so it is important that everyone within the organisation, especially the managerial teams, has a good understanding of how they can benefit from supporting the establishment of Power BI governance across the organisation. Here are some reasons:

  • Better decision-making
    With Power BI governance in place, organisations can ensure that the data used in decision-making is accurate, consistent, and trustworthy. This can help them make informed decisions based on reliable data insights.
  • Improved security and compliance
    Power BI governance helps to establish security measures to protect sensitive data and ensure compliance with regulations and industry standards. This helps to avoid costly data breaches and non-compliance penalties.
  • Efficient use of resources
    By establishing guidelines for roles and responsibilities, data access, sharing, and storage, Power BI governance can help organisations use their resources more efficiently. This can result in cost savings and improved productivity.
  • Enhanced collaboration
    Having Power BI governance policies help business owners to promote collaboration and communication among team members. This can result in improved teamwork and better outcomes for the organisation.
  • Better management and control
    Power BI governance helps organisations to manage and control the use of Power BI within the organisation. This can help them ensure that the tool is being used effectively and efficiently and that data is being used in a way that aligns with their business objectives.
Continue reading “Power BI Governance, What Organisations Need to Know”

Thin Reports, Real-world Challenges

Power BI Thin Reports, Real-world Challenges

I previously explained in a blog post what thin reports are and why we should care about them. I also explained Report Level Measures in another blog post. In this post, I try to raise some real-world challenges we face when developing thin reports. I also provide a solution to those challenges.

Report Level Measure Related Challenges

Creating and using Report Level Measures is relatively easy, but there are some challenges that we face from time to time, such as:

  • Distinguishing Report Level Measures from Dataset Level Measures
  • Report Level Measure dependencies

Determining Report Level Measures from Dataset Level Measures

One of the challenges that Power BI Developers face is creating many report level measures. Unfortunately, Power BI Desktop currently uses the same iconography for both types of measures, making it hard to distinguish the actual measures created within the dataset from the report level measures. It gets even more challenging if we need to write technical documentation for an existing thin report. We have to open the PBIX file of the thin report in the Power BI Desktop and click every single measure. If the expression bar appears, the selected measure is a report level measure; otherwise, it is a dataset level measure.

So unless we use third-party tools, which I explain in this post, we must go through the manual process.

Report Level Measure dependencies

Another pain point related to the previous challenge is finding the dependencies between the report level measures. It is crucial to be aware of the interdependencies when doing impact analysis. We need to understand how a change in a report level measure impacts other report level measures. Again, Power BI Desktop does not currently have any options supporting that, so we have to click every measure and read through the DAX expressions to identify the dependencies or use the third-party tools to save development time.

Dataset and Thin Reports Dependency Challenges

The other challenges are even more difficult to overcome relate to interdependencies between datasets and thin reports. Power BI Service provides a lineage view that shows the dependencies between a dataset and its connected thin reports. But the challenges can get more complex to overcome manually. The following are some real-world examples of more complex situations:

  • What if we need to analyse the impact of changes in a dataset measure on all report level measures of the connected thin reports?
  • How do we analyse the impact of changes on a dataset measure on all connected thin reports, including the visuals, filters, etc…?
  • What if we need to tune the performance and we want to find a list of all unused tables or unused fields?

As you can see, the situation can get pretty complex, so manual operations are virtually impossible.

But there is a third party tool we can use which provides heaps of capabilities with a couple of clicks.

Continue reading “Thin Reports, Real-world Challenges”