How To Create A Heatmap In Power BI

How to Create a Heatmap in Power BI?

Summary: A Power BI heatmap visually represents data density and patterns using colour gradients. It is helpful for sales analysis, performance evaluation, and more. Creating one involves loading data, adding a heatmap visual, and customising its appearance and interactivity. Power BI’s robust features enhance its heatmap capabilities.


Heatmaps in Power BI offer a powerful way for business analysts to visualise the relationship between two variables using different-coloured patches on a map. This custom visualisation helps users uncover hidden patterns and observe changes across each axis. 

In a Power BI heatmap, the darkest colours represent the highest data density, with lighter shades indicating lower density. These colour patterns can be displayed as tables, histograms, or geographical maps. 

This blog provides insights into creating heatmaps in Power BI, offering practical examples and step-by-step guidance to leverage this tool effectively.

What Is A Heatmap In Power BI?

Heatmap in Power BI refers to a custom visualisation that business analysts use to showcase the relationship between two variables on a map in different-coloured patches. It helps the user visualise the hidden patterns and observe the changes across each axis.

In a Power BI Heatmap, the darkest colours represent the highest data density, followed by lighter shades, which are equally proportionate to the density. The colour pattern can be presented as tables, histograms, or geographical maps. 

Key Features Of Power BI

Power BI is a powerful business intelligence tool developed by Microsoft that enables users to visualise and analyse data from various sources. Its many features make it a popular choice for data professionals, analysts, and organisations. Here are some key features of Power BI:

  • Data Visualisation:
    • Rich Visualisations: Power BI provides various interactive and customisable visualisations such as charts, graphs, maps, tables, and matrices to represent data effectively.
    • Custom Visuals: Users can import custom visuals created by the community or build their visuals to meet specific visualisation needs.
  • Data Connectivity:
    • Data Source Compatibility: Power BI can connect to a diverse range of data sources, including databases, cloud services, spreadsheets, web services, and more.
    • Direct Query and Import: Users can import data into Power BI or create direct connections to databases for real-time data analysis.
  • Data Transformation and Modeling:
    • Power Query: This feature enables users to shape, transform, and clean data from various sources before visualisation.
    • Data Modeling: Power BI’s Data Model feature allows users to create relationships between tables, calculate measures, and define hierarchies for more in-depth analysis.
  • DAX (Data Analysis Expressions):
    • DAX: It is a formula language used in Power BI to create custom calculations and measures for complex analysis.
    • Measures and Calculated Columns: Users can create measures and calculated columns using DAX to perform calculations on data.
  • Interactive Dashboards:
    • Dashboards: Users can create interactive and visually appealing dashboards that display vital insights and KPIs from different reports and datasets.
    • Tiles: Dashboards are composed of tiles that are linked to reports, visuals, or other content, allowing users to interact with the data.

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Applications Of Power BI Heatmap

Applications Of Power BI Heatmap

A Power BI heatmap is a powerful data visualisation tool that utilises colour gradients to represent data values within a matrix-like structure. Heatmaps provide an intuitive way to convey patterns, relationships, and variations in data, making them especially useful for various applications. Here are some detailed examples of how Power BI heatmaps can be employed:

Sales Analysis 

A Power BI heatmap can be employed in retail to analyse sales data. Imagine a matrix showing products on one axis and months on the other. The colour intensity in each matrix cell could represent the sales volume or revenue generated for that product in a specific month. 

A glance at the heatmap can highlight the best-selling products and months, allowing businesses to make informed decisions regarding inventory management, promotions, and sales strategies.

Employee Performance

HR departments can use heatmaps to evaluate employee performance. HR professionals can identify high-performing individuals based on colour intensity by plotting employees against performance metrics like sales targets met or project completion rates. Heatmaps can assist in recognising top talent, distributing rewards or incentives, and offering targeted training to employees who might need improvement.

Website User Behavior

Web analysts can utilise heatmaps to visualise user behaviour on websites. By segmenting website pages and tracking user interactions such as clicks and scroll depth, a heatmap can highlight which page areas receive the most engagement. Heatmaps can reveal user preferences, identify points of interest, and uncover potential usability issues, guiding website optimisation efforts.

Risk Assessment in Finance

In the financial sector, heatmaps can assist in risk assessment. Financial analysts can quickly identify high or low-risk areas based on colour gradients by displaying a matrix of different financial instruments against risk factors like market volatility or credit risk. It helps in portfolio management, risk mitigation, and making informed investment decisions.

Healthcare Data Analysis

In healthcare, heatmaps can analyse patient data and medical trends. For instance, a heatmap could depict patient demographics (age groups, gender) against the occurrence of specific medical conditions. The colour intensity could indicate the prevalence of the condition in each demographic group, helping medical professionals identify health disparities and target interventions effectively.

Customer Segmentation

Marketing teams can utilise heatmaps to segment customers based on purchase behaviour. A heatmap can display different customer segments against attributes such as purchase frequency, average transaction value, and product preferences. By examining the intensity of colours, marketers can tailor campaigns to different segments, enhancing customer engagement and loyalty.

Social Media Engagement

For social media managers, heatmaps can provide insights into audience engagement. By plotting different social media posts against engagement metrics like likes, shares, and comments, a heatmap can highlight the content that resonates most with the audience. This can guide content creation strategies and help optimise social media marketing efforts.

In all these examples, the Power BI heatmap is a visual aid that simplifies complex data and enhances decision-making. The colour gradients effectively convey patterns, trends, and relative values, enabling professionals from various domains to extract meaningful insights and take actions that improve outcomes. 

By leveraging Power BI’s heatmap capabilities, organisations can unlock the potential of their data and drive informed, data-driven strategies.

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How Do You Create A Heat Map In Power BI?

Creating a heatmap in Power BI involves visualising data as a matrix of coloured cells where each cell’s colour represents a value. Heatmaps represent data density, identify patterns, and compare data across two dimensions. Here’s a detailed step-by-step guide on how to create a heatmap in Power BI: 

Step 1: Load Data

To load data in Power BI Desktop, open the application. From the Home tab, click on “Get Data” and select your data source, such as Excel, SQL Server, or CSV. Proceed to load the necessary dataset for your project.

Step 2: Prepare Data

Preparing your data before creating a heatmap is essential. Transform it if necessary to ensure it includes two numerical dimensions, as heatmaps typically use this format. Adequately formatted data is crucial for adequate visualisation, enabling a clear representation of the relationships between the two variables.

Step 3: Create a New Visual

To create a new visual, navigate to the “Visualisations” pane on the screen’s right side. Click the “Heatmap” icon to add an empty heatmap visualisation to your report canvas. This step allows you to start building and customising your heatmap for data analysis.

Step 4: Add Data to Heatmap

In Step 4 of creating a heatmap, you begin by accessing the “Fields” pane on the right side of the interface. Here, you select the relevant data fields and then drag and drop them into the corresponding sections of the “Visualisations” pane. 

Typically, you’ll place one field in the “Values” section, representing the data points to be visualised, and two fields in the “Axis” section, determining how these values are plotted along the heatmap axes. This structured approach ensures that the heatmap effectively displays the relationships and variations within the data set, offering insights through visual representation.

Step 5: Configure Heatmap

To configure a heatmap, navigate to the “Visualizations” pane and select the “Format” section, where you can customise its appearance and behaviour. Under “General,” set the title, data labels, and colour scale options. 

In “Data Colours,” choose a colour scale to represent your dataset’s range, either using a gradient from low to high values or a custom scale. Adjust the “Legend” settings to display values corresponding to the colours on the heatmap. 

Lastly, fine-tune the “X-Axis” and “Y-Axis” by adjusting the axis labels, title, and other formatting options to enhance readability and presentation.

Step 6: Format Heatmap

To format a heatmap effectively, customise its appearance through various formatting options. Adjust the font size, style, and alignment of the axis and data labels to enhance readability and presentation. 

Additionally, modify the background colour and borders of the heatmap cells to make them visually appealing and distinct. These adjustments improve the heatmap’s aesthetic quality and make it easier to interpret the presented data. 

By selecting these formatting elements carefully, you can create a visually engaging and informative heatmap that effectively communicates the underlying data insights.

Step 7: Interactivity and Filtering (Optional)

In Step 7 of creating a heatmap, users can enhance interactivity and filtering capabilities within their report. By navigating to the “Format” section in the “Visualisations” pane and selecting the “Edit interactions” button, users can establish connections between the heatmap and other visual elements on the report canvas. 

This feature lets users dynamically filter data by clicking on specific elements within other visuals, fostering a more interactive and responsive data exploration experience. This functionality empowers users to gain deeper insights by directly interacting with the data presented in the heatmap and related visuals within their analytical reports.

Step 8: Save and Publish

Once you’re satisfied with the heatmap in Power BI, the next step is to save your report. After saving, you can publish it to the Power BI service, enabling easy sharing and collaboration with others. Users can interact with the heatmap and explore the data online. 

It’s essential to note that the specifics of each step might differ depending on your dataset and the version of Power BI you are using. This guide provides a comprehensive overview of creating a heatmap visualisation in Power BI, ensuring you have all the details to visualise and share your data insights effectively.

Read Further: 

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Anticipating Tomorrow: The Power of Predictive Modeling.

Frequently Asked Questions

How do you create a heatmap in Power BI?

To create a heatmap in Power BI, load your data from a source such as Excel or SQL Server. Add a heatmap visual, configure the data fields, and customise the appearance. Adjust formatting options, set interactivity, and publish your report for sharing and collaboration.

What are some examples of using a Power BI heatmap?

Examples include analysing sales data by visualising product performance over time, evaluating employee performance through performance metrics, tracking website user behaviour, assessing financial risk by mapping risk factors, analysing healthcare trends, segmenting customers by behaviour, and monitoring social media engagement to guide content strategies.

What key features make Power BI suitable for heatmaps?

Power BI offers rich visualisations, including custom visuals, extensive data connectivity options, and robust data transformation and modelling capabilities. The DAX formula language allows for advanced calculations, and interactive dashboards enable dynamic data exploration, making Power BI ideal for creating and analysing heatmaps effectively.


We hope you found all the details related to the Power BI Heatmap from the above blog, from its applications to creating a Power BI Heatmap. Significantly, you now know that these maps represent the relationships between unrelated datasets and give users a new perspective. 

While the blog has provided some interesting Power BI Heatmap examples, you can effectively learn from them. You can now create a Power BI Heatmap matrix by following the steps you learnt from the blog. 


  • Tarun Chaturvedi

    Written by:

    Reviewed by:

    I am a data enthusiast and aspiring leader in the analytics field, with a background in engineering and experience in Data Science. Passionate about using data to solve complex problems, I am dedicated to honing my skills and knowledge in this field to positively impact society. I am working as a Data Science intern with, where I have explored the enormous potential of machine learning and artificial intelligence to provide solutions for businesses & learning.

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