Power BI Artificial Intelligence: Insights Using Visuals

The Power BI’s artificial intelligence helps users with advanced analytics. This will, in turn, breed data-driven insights, predictive capabilities, and an efficient decision-making process. It also plays a major role in swot analysis.

This guide focuses on what the AI-powered Power BI is. It will help you get a good grasp of AI in Power BI, AI-powered features in Power BI, and how AI is used in Power BI.

A section of this guide shows how to integrate AI with Power BI, and how to do Power BI AI analysis.

What is AI-Powered Power BI?

AI-powered Power BI is the integration of artificial intelligence capabilities within Microsoft’s Power BI platform. It helps users to extract deeper insights, automate data analysis tasks, and predict trends.

These features include machine learning models, predictive analytics, cognitive services, and natural language processing (NLP). With tools like Q&A for conversational querying, automated insights, and AI visuals, AI-powered Power BI transforms complex data into actionable intelligence. It also makes advanced analytics accessible to data professionals and business users.

What are the AI-Powered Features of Power BI?

1. AI Visuals
   • Decomposition Tree: Explores data hierarchies to uncover patterns and break down metrics.
   • Key Influencers: Identifies the factors influencing a particular outcome.
   • Smart Narrative: It automatically generates text-based insights from visualizations.
2. Natural Language Processing (NLP)
   • Power BI Chat: It provides AI-driven conversational analytics to simplify interactions with data.
   • Q&A Feature: It allows users to query datasets in plain language and receive answers in the form of visualizations.
3. Cognitive Services Integration
   • It includes image recognition, language translation, sentiment analysis, and other AI services to enrich datasets.
4. Forecasting
   • It predicts future trends based on historical data. Built-in AI algorithms are used for this process.
5. Anomaly Detection
   • It identifies unusual patterns (or data points) to highlight potential opportunities (or issues).

How is AI used in Power BI?

1. Predictive Analytics
   • Forecasting: AI-powered forecasting predicts future values based on historical data. This aids in planning and decision-making.
   • Automated Machine Learning (AutoML): Users can train, create, and apply machine learning models directly within Power BI to predict outcomes, classify data, or forecast trends.
2. Data Insights and Pattern Recognition
   • Anomaly Detection: AI algorithms highlight unexpected patterns (or irregularities) in data.
   • Decomposition Tree: AI helps users drill down into data hierarchies. This way, the user can easily discover trends and relationships.
   • Key Influencers Visual: AI analyzes data to identify factors that impact specific metrics (or outcomes).
3. Cognitive Services
   • Language Translation: AI translates insights (or data) into multiple languages for broader accessibility.
   • Image and Text Analysis: Power BI integrates AI services like text recognition, sentiment analysis, and object detection to enrich data visualizations.
4. Natural Language Processing (NLP)
   • Smart Narrative: It automatically converts complex data into easily understandable insights and summaries.
   • Q&A: Users can ask questions in plain language, and Power BI generates relevant answers or visualizations.

How to Integrate AI with Power BI?

• Utilize Built-in AI Features
  • AI Visuals: It uses built-in visuals like a Decomposition Tree, Smart Narrative, and Key Influencers directly in Power BI.
  • Q&A: It enables natural language querying. This way, users are allowed to ask questions in plain language.
  • Anomaly Detection and Forecasting: Users use these features to identify irregularities and predict trends.
• Integrate Azure Cognitive Services
  • You have to connect Power BI to Azure Cognitive Services to use the AI models for sentiment analysis, image recognition, text translation, and other advanced tasks.

To get started:

• • Set up an Azure Cognitive Services account.
  • Use the Azure AI insights connector to connect the Azure service to Power BI.
  • Apply AI transformations to enrich the datasets.
• Leverage Power Automate for AI
  • Use Power Automate to automate workflows with AI Builder components. This brings AI-powered automation into the Power BI processes.
• Embed Machine Learning Models
  • Use tools like R, Python, or Azure ML to train machine learning models.
  • Export the model and apply it in Power BI:
    • The Python/R scripting should be used in Power BI Desktop for integration.
    • You can also deploy the model in Azure ML and connect it via API.

How to Do Power BI AI Analysis?

1. Prepare Data
   • Import and clean the dataset in Power BI Desktop. You can do this by using Power Query for optimal analysis.
2. Use AI Visuals
   • Add Key Influencers to identify factors affecting outcomes.
   • The decomposition Tree should be used to uncover patterns and explore data hierarchies.
   • Enable Smart Narrative to generate text-based insights.
3. Apply Anomaly Detection
   • You can identify unexpected trends (or data points) by using the anomaly detection visual.
4. Forecast Trends
   • To predict future trends based on historical data, you’ll have to apply the forecasting feature on line charts.
5. Integrate Machine Learning
   • Connect with Azure Machine Learning or use R/Python scripts to apply advanced models.
6. Leverage Cognitive Services
   • Enrich data with image recognition, language translation, or sentiment analysis using Azure AI services.

How to create Visualization in Power BI?

Whether you’re using the Power BI in Office 365, or the regular Power BI for web browsers, you’ll have to create visualizations. This section showcases some Power BI dashboard designs and Power BI report design to help you get started.

Here are the steps to help you create visualization in Power BI.

• 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 Diagram.
• Navigate to the left-side menu, and click on the “Create” option. After that, select “Paste or manually enter data.”

• The customer segmentation data will be used for this illustration.

• Paste the above Power BI datasets into the “Power Query” window. Next, 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 Sankey Diagram in the Power BI extension by ChartExpo, you’ll need to use an add-in or Power BI visual from AppSource. Navigate to the right side of the Power BI dashboard, and 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, you’ll have to select the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel.
• Furthermore, you’ll select the fields to use in the Sankey chart.

• At this point, you’ll have to provide your email address or a ChartExpo license key.
• Add the key under the visual section. After that, you’ll see the Sankey chart.

• Sankey diagram should look as shown below.

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

• Chart should look as shown below.

• You can disable the percentage value.

• Set the Level Labels on the top position.

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

• Here’s the final Sankey Chart.

The Power BI report example above shows how the Power BI data model can be used to perform market analysis. You can create recruitment dashboards, or perform cash flow analysis by following the steps above.

Professionals who are into CRM analytics or offer data and analytics services should incorporate data storytelling into their overall visualization process. This will, in turn, help them draw meaningful insights from their analysis.

Insights

The data tracks customer transitions across value categories over three months. High-value customers dominate revenue, and they come with consistent retention and some downgrades.

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

Benefits of AI Visuals in Power BI

• Deeper Insights: AI visuals like Decomposition Tree and Key Influencers help uncover correlations, hidden trends, and relationships in data. This enables understanding of the data and good decision-making.
• Time Efficiency: AI visuals identify patterns, key drivers, and anomalies without requiring manual analysis. This will, in turn, help the user save time.
• Simplified Complexity: Tools like Smart Narrative translate complex datasets into plain language summaries. This way, data interpretation becomes easier for non-technical users.
• Enhanced Decision-Making: AI-driven predictions and anomaly detection offer actionable insights, and empower users to make proactive, data-driven decisions.

FAQs

Is there any AI for Power BI?

Yes, Power BI integrates AI through features like ChartExpo add-in or Power BI visual from AppSource. It also offers more use for key influencers, AutoML, decomposition tree, and Azure cognitive services.

Will Power BI be replaced by AI?

No, Power BI is enhanced by AI. AI integrates with Power BI to improve analytics, automate insights, and make advanced data processing accessible.

Can AI create dashboards in Power BI?

AI can assist in creating dashboards in Power BI. It generates insights, visual recommendations, and automates data preparation. Human expertise is needed for design and business context.

Wrap Up

Power BI artificial intelligence integrates AI features like machine learning and predictive analytics for enhanced insights.

With the AI visuals in Power BI, users can generate deeper insights. These insights are generated using AI visuals like Decomposition Tree and Key Influencers. AI visuals help to quickly identify patterns, anomalies, and key drivers. This will, in turn, help the users eliminate manual analysis and save time.

AI visuals are a good fit for large datasets. It enables organizations to analyze complex data hierarchies and multiple variables efficiently.

By following the steps outlined in this guide, you’ll be able to use Power BI to create a compelling Sankey chart.

Now you know what the Power BI artificial intelligence is, how will you use it to elevate your data analysis process?