Power BI Data Flows: Practical Use Cases for Businesses

Power BI data flows allow users to automate data transformation and disintegration from multiple sources. This way, the user will be able to create reusable data models for consistent analysis. It helps streamline data preparation and enhance reporting efficiency.

In this guide, you’ll discover what data flows in Power BI are, why data flows in Power BI are important, and how Power BI data flows work. You’ll also discover how to create a data flow in Power BI, and how to utilize data flows in specific scenarios in Power BI.

What are Data flows in Power BI?

Definition: Data flows in Power BI are data preparation features that allow users to extract, transform, and load (ETL) data from multiple sources into Power BI.

It helps automate data transformation processes and create reusable, standardized data models.

Why are Data Flows in Power BI so Important?

• Streamlined Data Preparation: Data flows in Power BI automate the extraction, transformation, and loading (ETL) of data from multiple sources. The automation reduces manual effort and minimizes errors and inconsistencies in the data preparation process.
• Improved Performance: Data flows lower computational load on Power BI reports. That’s done by handling data transformations outside of the report layer. This will, in turn, lead to faster report generation and better efficient data processing.
• Single Source of Truth: Data flows ensure that all users are working with up-to-date, accurate data by maintaining a single source of truth. It enhances decision-making and ensures that analytics are based on consistent and trustworthy information.
• Reusable and Consistent Data Models: Data flows give users the room to create centralized, reusable models that can be applied across multiple dashboards and reports. It ascertains consistency in data usage across the organization.

How Power BI Data Flows Work?

1. Connect to Data Sources: Dataflow gives users the room to connect to multiple data sources like cloud services, files, and databases.
2. Data Extraction: Data is extracted from these sources into the Power BI for more processing.
3. Data Transformation: The Power Query helps users clean, filter, and transform data without writing code.
4. Centralized Storage: The transformed data is stored in centralized dataflow storage within the Power BI service.
5. Automated Refresh: Data flows can be scheduled for automatic refreshes to ascertain up-to-date data.
6. Reusable Data: The processed data can be reused in multiple Power BI dashboards and reports.
7. Simplified ETL: The process automates the ETL (Extract, Transform, Load) workflow, and reduces manual work and errors.

How to Create a Dataflow in Power BI?

Step 1: Log into Power BI Service (app.powerbi.com).

Step 2: Navigate to the preferred workspace where you want to create the data flow.

Step 3: In the workspace, click on the “Create” button and select “Dataflow.”

Step 4: Click “Add new tables” and choose the data sources you want to connect to. It could be databases, cloud services, or files.

Step 5: Use the Power Query Editor to clean, filter, and transform the data. Apply the right transformations like merging tables, adding calculated columns, or removing duplicates.

Step 6: After the transformations, click “Save & Close” to save the data flow. After that, load the data into Power BI.

Step 7: To keep the data up-to-date automatically, set up a refresh schedule.

Power BI Data Flows Practical Use Case

Lets say you have integrated your data using Power BI data flows and now you wants to visualize it. This section shows you some Power BI design ideas. It also shows you how to use a compelling Power BI dashboard vs report to represent your Power BI datasets.

• Stage 1: Log into Power BI, enter your email, and click the “Submit” button.

• You’ll be redirected to a Microsoft account. After that, enter your password, and click “Sign in.”

• You can opt to stay signed in.

• Stage 2: Create a Data Set and Select the Data Set to Use in the Sankey Chart.
• Navigate to the left-side menu, and click on the “Create” option. You’ll have to select “Paste or manually enter data.”

• The Customer Segmentation data below will be used for this illustration.

• Paste the data table above into the “Power Query” window. After that, select the third “Create” option.

• Navigate to the left-side menu, and click on the “Data Hub” option. Power BI populates the data set list. If no data set has been created, you’ll get an error message.

• Click on the “Create report” dropdown.

• To add the Power BI Sankey Diagram Extension by ChartExpo, you’ll have to use an add-in or Power BI visual from AppSource. Navigate to the right side of the Power BI dashboard. After that, open the Power BI Visualizations panel. Click the ellipsis symbol (…) to import the Power BI Sankey Diagram extension by ChartExpo.
• In the menu that opens, select the “Get more visuals” option.

• Enter “ChartExpo” in the highlighted search box. You’ll see the “Sankey Diagram for Power BI by ChartExpo.”

• After that, click on the Sankey Diagram and also click the highlighted “Add” button.

• Power BI will add the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel.

• To draw a Sankey Diagram with ChartExpo’s Power BI extension, select the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel. You’ll have to select the fields to use in the Sankey chart. After that, provide your email address or a ChartExpo license key. Add the key under the Visual section. At this point, you’ll see the Sankey Chart.

• You can add the top header text in the chart under the General section.

• After that, click on General and add the top header text.

• There’s the option to disable the percentage value.

• You can set the Level Labels on the top position.

• You can change the Nodes color by following the steps below:

• Here’s the final look at the Sankey Chart.

The strategy used for this Power BI data model can be applied when performing other similar data analytics and data visualizations like self-service analytics, healthcare analytics, and visual analytics.

Insights

This chart tracks customer transitions across value categories over three months. High-value customers dominate revenue, and the retention is consistent with some downgrades.

Medium-value customers shift between higher and lower tiers, and that reflects volatility. Low-value customers usually remain in their category but occasionally upgrade — that shows potential growth opportunities in targeted engagement.

Advantages of Data flows in Power BI

1. Centralized Data Management: Data flows enable centralized management of data transformations, and reduce redundancy. It also ensures consistency across multiple dashboards and reports.
2. Reusability: Transformed data models can be reused in multiple Power BI reports, and that eliminates the need to repeat the same transformations and ensures consistency in data analysis.
3. Automation: Data flows automate the ETL (Extract, Transform, Load) process. It reduces manual effort and minimizes errors.
4. Improved Performance: Offloading data transformations to the dataflow layer helps in optimizing report performance. It also gives room for faster loading and processing of large datasets.

Power BI Data flows Limitations

1. Performance Bottlenecks: Handling large datasets (or complex transformations) may lead to slower processing and refresh times.
2. No Direct Live Connections: Data flows only support import mode. They do not allow direct live connections to underlying data sources.
3. Limited Debugging Tools: Troubleshooting complex data flows is challenging — that’s due to limited error-reporting and debugging capabilities.
4. Storage Constraints: Data flows are subject to storage limits, and that can restrict the size of datasets that can be processed.

Best Practices for Microsoft Power BI Data flows

Whether you’re a newbie or a Power BI advanced user, here are some Power BI best practices to help you get the most from your data interpretation process.

• Plan Data flows Strategically: You have to design data flows to centralize reusable data transformations and reduce duplication across reports.
• Avoid Overloading Data flows: You have to keep data flows focused on specific tasks. This will help prevent performance bottlenecks and complexity.
• Leverage Linked Entities: Use linked entities to reference existing data flows. That promotes modularity and consistency in data usage.
• Optimize Transformations: Always avoid unnecessary steps that could slow processing when performing data transformations in Power Query.

FAQs

What is the difference between data flow and dataset in Power BI?

A dataflow is a reusable ETL tool for centralizing and transforming data. A dataset, on the other hand, is a structured collection of data ready for visualization and analysis in Power BI.

How do you create a new data flow in Power BI?

Navigate to a workspace, and select “Create” > “Dataflow,” connect to data sources, transform data in Power Query, and save it.

How do you enable data flow in Power BI?

You have to ensure you have a workspace, access Power BI Service, and create a data flow. To create a dataflow, select “Create” > “Dataflow” from the workspace options.

Wrap Up

Power BI data flows are ETL tools used for extracting, transforming, and loading data from multiple sources. This way, users can create reusable and centralized data models for analysis.

Data flows automate data preparation, ensure consistency, centralize data management, and improve performance. It also enables scalable, efficient, and reliable analytics across an organization.

To get the most out of the Microsoft Power BI data flows, you have to plan data flows strategically. You also have to regularly monitor performance. Periodically review and optimize data flows to ascertain they meet your business needs.

By following the steps in this guide, you’ll be able to use the workspace in Power BI, and also create visualizations like the Sankey chart.

Now you know what the Power BI dataflow is, how will you incorporate it into your data analysis process?