What is Displayed in the Power BI Data Hub?

Finding the right data to build a report can feel like searching for a needle in a haystack. Your company has data everywhere, but which sales dataset is the official one? Is the marketing data you're looking at even up to date? This is exactly why Power BI introduced the Data Hub, a centralized catalog designed to bring order to your data chaos. This article will walk you through everything displayed in the Data Hub and explain how to use it to find trustworthy data fast.

Graphed

Build AI Agents for Marketing

Build virtual employees that run your go to market. Connect your data sources, deploy autonomous agents, and grow your company.

Build Your First Agent

What is the Power BI Data Hub?

Think of the Power BI Data Hub as a library for all the shareable data assets in your organization. Instead of having dozens of analysts creating their own versions of a "sales dataset," the Data Hub provides a single, searchable place to discover official, high-quality data that's ready for use. Its main goal is to make data more discoverable, promote the reuse of existing data models, and give users confidence that they are working from a single source of truth.

By centralizing these key data assets, it helps shift organizations away from a culture of messy, duplicated report files and towards a more streamlined, governed approach to business intelligence. You spend less time hunting for data and more time finding insights.

The Main Types of Data on Display

The Data Hub isn’t just a simple list of files, it’s a curated gallery of specific Power BI objects designed for reuse. When you navigate to the Hub, you'll mainly find these four types of assets.

AI Agents for Marketing

Crash Course

Graphed

Free PDF · the crash course

AI Agents for Marketing Crash Course

Learn how to deploy AI marketing agents across your go-to-market — the best tools, prompts, and workflows to turn your data into autonomous execution without writing code.

Download the PDF

1. Datasets

Datasets are the cornerstone of Power BI and the most common item you'll find in the Data Hub. A dataset is a collection of data that has been modeled for reporting. It's not just a raw table, it's a purposefully structured package containing:

• Data Connections: The links to the original data sources (like a SQL database, a SharePoint list, or a Salesforce account).
• The Data Itself: If the data is imported, it's stored within the dataset.
• Data Model Relationships: The connections between different tables (e.g., how the 'Sales' table relates to the 'Customers' table).
• DAX Measures and Calculations: Pre-built business logic, like calculations for 'Year-over-Year Growth' or 'Profit Margin'.

The beauty of a shared dataset is that ten different people can build ten different reports all based on the exact same data model and calculations. If a business definition changes, you only have to update the DAX measure in one place - the central dataset - and all ten reports will update automatically. This is fundamental to establishing a trustworthy data culture.

2. Datamarts

Datamarts are a newer and more powerful addition to Power BI. Think of a datamart as a self-contained, fully managed database that lives entirely within Power BI, designed for self-service analytics without needing IT intervention.

A datamart bundles three things into one neat package:

• It ingests data using dataflows (more on that next).
• It stores that data in a fully managed Azure SQL Database that you don't have to configure or pay for separately.
• It automatically generates a dataset on top of it for reporting.

The key benefit of a datamart is that you're not just limited to creating Power BI reports with it. Because it contains a real SQL database, you can connect other tools (like SQL Server Management Studio) to query the data directly, giving advanced users more flexibility.

3. Dataflows

While datasets and datamarts are about the final, modeled data, dataflows are all about the process of getting it there. A dataflow is a self-service, cloud-based data preparation process (often called ETL — Extract, Transform, and Load) created in Power Query Online.

Imagine you need to pull customer data from Salesforce, clean up the country names, merge it with support-ticket data from Zendesk, and remove a few unneeded columns. You could perform those same cleaning steps over and over in every report you build. Or, you could do it once in a dataflow.

This dataflow then saves the clean, prepared table of data in Azure Data Lake Storage. Now, multiple datasets across your organization can source their data from this one clean, repeatable dataflow. It simplifies and standardizes the often-messy work of data prep.

Graphed

Build AI Agents for Marketing

Build virtual employees that run your go to market. Connect your data sources, deploy autonomous agents, and grow your company.

Build Your First Agent

4. Metrics (Scorecards)

Metrics, also known as Scorecards, allow you to curate and track key business performance indicators (KPIs) in a systematized way. Instead of just having a visual on a report showing your revenue number, you can create a 'Metric' for "Total Quarterly Revenue."

This metric has defined targets, tracks its status over time (e.g., 'On track,' 'Behind'), and lets you assign owners. People can check in with notes, explaining why a target was missed or hit. Displaying metrics in the Data Hub makes it easy for everyone in the company to see and follow the most important KPIs that leadership is tracking, without having to dig through different reports.

Navigating the Data Hub Interface: What You See

When you open the Power BI service (the web version) and click "Data Hub" in the navigation pane, you'll be greeted with a list of all the data assets you have permission to see. Here's a breakdown of the specific information displayed for each item, which helps you quickly assess its quality and relevance.

Key Information at a Glance

The list view presents several important columns:

• Name: The name given to the dataset, datamart, or other asset.
• Endorsement: This is one of the most important columns. It shows the asset's level of quality and authority.
• Owner: The person who created or is responsible for the asset. This is incredibly helpful so you know who to ask if you have questions.
• Workspace: The Power BI workspace where the asset is stored. This helps you understand the context (e.g., is it in the official 'Finance' workspace or someone's personal 'Test' workspace?).
• Refreshed: The date and time the data was last updated. Seeing a dataset that was refreshed five minutes ago gives you much more confidence than one that hasn't been updated in three months.
• Sensitivity: If your organization uses Microsoft Information Protection sensitivity labels, you'll see them here (e.g., 'Confidential,' 'General,' 'Public').

Using the Details Pane

Clicking on any item in the list opens a details pane on the right. This gives you even more information without having to navigate away.

• Actions: You'll see several quick-action buttons here. The most useful is "Create a report," which lets you immediately jump into the report builder using that dataset. You can also analyze it in Excel.
• Related Reports: This shows you all the existing reports that have already been built using this exact dataset. This is a great way to see how others are using the data and to avoid duplicating work that already exists.
• Dataset Details: Includes the exact server address you would need to connect to this dataset from Power BI Desktop.

Why the Data Hub is a Game Changer for Teams

The Data Hub isn’t just a nice-to-have feature, it fundamentally changes how teams work with data.

AI Agents for Marketing

Crash Course

Graphed

Free PDF · the crash course

AI Agents for Marketing Crash Course

Learn how to deploy AI marketing agents across your go-to-market — the best tools, prompts, and workflows to turn your data into autonomous execution without writing code.

Download the PDF

1. Find and Trust Data Faster

The endorsement and metadata features take the guesswork out of finding the right data. Team members can confidently connect to a "Certified" sales dataset, knowing it's the single source of truth approved by the company, rather than emailing around asking, "Hey, is this the right file to use?"

2. Reduce Redundant Work

By making existing assets so discoverable, the Data Hub prevents your organization from constantly reinventing the wheel. A marketing analyst doesn't need to rebuild a complete customer data model if they can see that the sales analytics team has already created and certified one. They can simply connect to the existing dataset and start building their visuals.

3. Improve Data Literacy

Showcasing existing data models and related reports helps everyone learn what data is available and how the business measures success. It democratizes access to curated data, enabling less technical users to create their own valuable reports without having to be data modeling experts.

Final Thoughts

The Power BI Data Hub is Microsoft's solution for taming data sprawl and promoting a culture of centralized, trustworthy BI. By giving your organization a clear catalog of endorsed datasets, datamarts, and dataflows, it ensures that your team spends less time searching for data and more time using it to make smart decisions.

An organized data hub is a fantastic foundation for any data-driven company. For marketing and sales teams who often need answers fast without a long development cycle, we built Graphed to take that accessibility to the next level. Instead of manually building reports in a complex tool, you can connect your scattered data sources - like Google Analytics, Shopify, and Salesforce - and simply ask questions in plain English. We turn your request into a live, interactive dashboard in seconds, letting you go from question to insight without getting stuck on the technical details.