Power BI Data Governance: Importance, Elements, Features & Implementation Approach

Last updated on: June 16th, 2023, Published on: June 16th, 2023
Power BI Data Governance

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Analytics platforms like Power BI are critical to unearthing valued insights from data. Yet with increased reliance comes the pressing need for robust data governance strategies.

Effective Power BI data governance is crucial for maintaining data integrity, security, and compliance while empowering data practitioners to leverage Power BI for data analysis and reporting.

In this article, we outline the importance of Power BI data governance, examine self-service aspects and features, and conclude with a guide for full implementation.

Table of contents

  1. What is Power BI data governance?
  2. Why you need Power BI data governance
  3. Elements of a Power BI data governance model
  4. Enabling self-service Power BI data governance
  5. Power BI data governance features
  6. How to roll out Power BI data governance
  7. A comprehensive approach to Power BI data governance
  8. Summing up
  9. Power BI Data governance: Related Reads

What is Power BI data governance?

Power BI data governance is a comprehensive approach that meets the needs of Power BI users, head-on. It entails decision rights and accountability for managing, improving, and maintaining the quality of data in Power BI environments within an organization.

Such a framework incorporates policies, procedures, and technology to facilitate a controlled yet user-friendly way of handling data, ensuring its quality, privacy, security, and compliance.

Implementing Power BI governance practices is essential to mitigate risks, promote data consistency and accuracy, set up access controls, and get the most out of Power BI.

Why you need Power BI data governance

Power BI data governance addresses challenges specific to the platform, as it is essential to regulate access to sensitive data, comply with regulatory requirements, and foster customer trust.

Current data governance approaches are trending towards bottom-up, decentralized, and human-powered models that favor self-service and user autonomy.

Without a robust data governance strategy in place, Power BI can expose your organization to numerous risks.

Microsoft itself lists many classes of governance challenges, including:

  • Strategy challenges
  • People challenges
  • Process challenges
  • Data quality challenges
  • Skills and data literacy challenges

These potential pain points are very real, and experienced by data professionals all too frequently. Power BI’s ease of use sometimes means its users don’t ask too many questions about the underlying data.

For example, one Twitter user noted recently how one company wanted him to apply advanced analytics to their Power BI data sets - but, by their own admission, the data quality was abysmal.



Power BI data governance helps prevent such scenarios by implementing clear rules around data usage, ensuring data integrity, privacy, and regulatory compliance, thus enabling a secure, efficient, and trust-building self-service BI environment.

Elements of a Power BI data governance model

Power BI works by using connectors to various data sources, from which it creates data sets. These data sets can help in developing reports and dashboards that offer valuable business insights.

The three core elements of a Power BI data governance model would thus involve the following:

  • Data set sources
  • Dashboards and reports
  • Sharing and collaboration

Let’s delve into the specifics of each element.

Data set sources


Effective data governance begins with a clear understanding of data sources. It involves determining who has access to what data sets and establishing procedures for managing access to these source data sets.

There also needs to be transparency regarding the origins of these data sets, their update frequency, and who has permission to introduce new data sets.

Dashboards and reports


Data governance extends to the creation of dashboards and reports, which are derived from data sets.

Clear guidelines should be established to regulate who can create dashboards and reports, and from which data sets, ensuring consistency and accuracy in data analysis and interpretation.

Sharing and collaboration


A comprehensive Power BI data governance model addresses the aspect of sharing and collaboration.

This includes determining who can share data sets and reports, the conditions under which data can be exported, and how to track changes to data once it leaves the Power BI environment.

Enabling self-service Power BI data governance

Enabling self-service access to Power BI in a governed manner requires a strategic approach that strikes a balance between autonomy and control.

Historically, organizations have utilized several models:

  • Bottom-up: Where teams handle all data-related tasks
  • IT managed: Where IT departments oversee data preparation while teams create reports and dashboards
  • Top-down: Where teams can only execute pre-set reports

However, the future of data governance lies in a hybrid, decentralized, and “adaptive” approach.

This model champions a scenario where business units create and maintain their data assets in compliance with the organization’s data governance framework and data quality standards. IT then supports this by providing role-based access control and maintaining data catalogs, as well as facilitating data classification, data lineage, and other critical governance features.

With this approach, all users with appropriate access levels can locate relevant data and generate their own reports as needed. This promotes a culture of self-service while ensuring data integrity and compliance.

This model is increasingly being recognized as the way forward for data governance, offering a balanced, flexible, and efficient solution to the complexities of modern data management in Power BI.

Power BI data governance features

Power BI incorporates several features to facilitate a level of data governance within the platform.

The existing Power BI data governance features include:

  • Workspaces
  • Dataset discovery
  • Certified data sets
  • Row-level security

All of these are specific to the platform and don’t comprise a comprehensive, company-wide approach to data governance.

However, organizations and teams may consider leveraging them to provide additional governance controls and security within individual environments.

Let’s briefly understand each feature further.

Workspaces


Workspaces allow you to segregate and manage data sets based on teams or projects.

By controlling which data sets are available in specific workspaces, you can regulate who has access to them, thereby enhancing data security and management.

Dataset discovery


Power BI includes basic search features, enabling users to easily find relevant data sets. This aids in streamlining workflows and encouraging efficient data utilization within the organization.

Certified data sets


To uphold data integrity and reliability, Power BI provides a certification feature for data sets.

Once a data set gets a stamp of approval - certifying its accuracy, completeness, and compliance with data standards - it shows up prominently in data set searches.

This guides users toward reliable and approved data sources, promoting consistent and accurate data usage.

Row-level security


Row-level security (RLS) in Power BI allows you to control data access at a more granular level. RLS lets you restrict data access at the row level based on user roles or attributes, thereby adding an additional layer of security and personalization to data access.

But there are some weaknesses in how Power BI manages data on desktop devices. According to James Beresford, an Enterprise Power BI Strategist, Power BI Desktop is a huge security risk:

“A Power BI file is an unencrypted store of all that data and is easily shared. It can’t even be password protected. If someone has a Power BI file, they have a means of reading all that data at their convenience.”

Regulating access to your Power BI assets based on user roles, projects, or data domains requires using modern, proactive data governance tools like Atlan.

Read more → How Atlan democratized trusted data across Elastic

How to roll out Power BI data governance

When it comes to implementing a Power BI data governance plan, timing and approach play crucial roles. According to Microsoft, there are three recommended strategies to consider, each with its unique advantages.

  1. Power BI rollout followed by data governance: This approach involves launching Power BI first, allowing users to familiarize themselves with the platform and its capabilities. Once Power BI is established within the organization, a comprehensive data governance plan can be introduced to control and manage data usage effectively.
  2. Data governance plan followed by Power BI: This strategy calls for setting up a robust data governance framework before deploying Power BI. This way, by the time Power BI is rolled out, governance policies and procedures are already in place, ensuring a controlled environment for data handling from the get-go.
  3. Agile/iterative model: This approach adopts an iterative process where pieces of the data governance plan and Power BI rollout are implemented simultaneously in stages. This allows for continuous improvement and adaptation, enabling the organization to optimize both its data governance framework and Power BI usage over time.

Regardless of the approach taken, it is essential to maintain a focus on fostering a culture of data stewardship and empowering users to derive maximum value from their data in a regulated, compliant manner.

A comprehensive approach to Power BI data governance

Successfully implementing Power BI data governance requires a multi-faceted approach encompassing various strategies and tools. Here are four steps to consider:

  1. Define your company’s data governance framework
  2. Deploy a data catalog
  3. Define and implement your Power BI approach
  4. Iterate on your approach over time

Now, let’s look into the specifics.

1. Define your company’s data governance framework


Firstly, lay the groundwork by defining your organization’s data governance framework. This should align with a bottom-up approach, empowering business teams to drive the creation and maintenance of data sets.

Such an approach will democratize data usage while still ensuring that regulatory and organizational standards are met.

2. Deploy a data catalog


As noted above, Power BI provides some of its own data governance and data quality features. However, most organizations need a more comprehensive approach that encompasses all of their data stores and analytics tools.

A data catalog provides a one-stop location for indexing, searching, and collaborating on an organization’s data no matter where it lives. Data catalogs can also enforce security controls, data quality and formatting standards, and other data governance principles uniformly across the company.

A data catalog like Atlan can track the data and metadata associated with Power BI objects such as workspaces, dashboards, data sets, and dataflows, among others.

Features like data lineage can show you which Power BI data sets and reports are using what data, and the potential impact that data changes might have on those downstream objects.

Read more → How Nasdaq uses a modern data catalog to activate metadata and drive a common understanding of their data assets

3. Define and implement your Power BI approach


Once you have this structure in place, design and execute your Power BI strategy. This involves defining collaboration and delivery scenarios, ranging from personal, team, and departmental to enterprise-wide usage.

Determine your self-service scenarios, content management, and deployment strategies, tailoring them to suit your organization’s unique needs.

To reinforce governance, integrate your data catalog with Power BI using a connector. This allows for the classification, monitoring, and management of Power BI datasets, reports, tables, and other assets.

Iterate on your approach over time


Lastly, remember that data governance is not a one-time task but an ongoing process. Regularly reassess your approach to ensure that it fosters a genuinely bottom-up environment where employees can access the datasets they need for informed decision-making.

Summing up

Effective data governance is a critical element of modern businesses, ensuring that data is used responsibly, securely, and in accordance with regulatory requirements.

Given its ubiquity, Power BI should be carefully considered when formulating a comprehensive data governance strategy. The Power BI data governance tools (native), when paired with a modern data catalog, can ensure that users are empowered to create and manage analytics data safely and accurately.

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