Research tells us even though 80% of executives expect AI to have a huge benefit in DA, less than 50% feel capable of using it. This gap highlights the need for tools to make AI accessible. The use of AI in data analysis has started, and Power BI is paving the way. Power BI now brings AI-powered features to the table. It is designed to uncover hidden insights, predict trends, and make data analysis easier than ever. Even if you are a beginner, AI in Power BI simplifies the process. In this guide, we talk about how Power BI integration with AI can help you analyze data like never before.
Role of AI in Power BI
Named a leader in Gartner’s 2023 Magic Quadrant for Analytics and Business Intelligence (ABI), Power BI is a brainchild of Microsoft. It is aiding uncountable businesses in data analysis throughout the globe. It is one of the largest data analysis applications today. Here’s a look at how AI boosts the functions of Power BI:
Automated Insights and Smart Visualizations
Instead of manually sifting through data, AI can identify hidden trends and patterns without your intervention. It can also suggest the most effective visualizations to represent your data clearly.
Natural Language Queries
Forget complicated coding or query syntax. With AI in Power BI, you can ask questions in simple, everyday language like, “What were our sales last quarter?” – and get accurate answers in seconds.
Anomaly Detection
AI works as your data guardian. It can quickly identify unusual data patterns. AI can help you flag anomalies early so that businesses can investigate and resolve potential issues before time.
Sentiment Analysis
AI integration in Power BI can analyze text data (like customer reviews or feedback) and determine sentiment without any coding. It can help businesses understand how customers feel about their products or services.
Key Influencer Analysis
AI can help you pinpoint factors that drive certain metrics. This aids in making informed decisions. AI can offer a clearer view of the forces influencing performance.
Decomposition Tree
AI-driven visualization tools make root cause analysis simple. It can be immensely helpful in drilling into complex data step-by-step. It can help uncover the behind-the-scenes of specific results or trends very easily.
Forecasting
If there is one thing that AI is most known for, it is forecasting. Predicting future trends is no longer guesswork. Power BI can now use past data to project future outcomes with the help of AI. It gives businesses the confidence to plan strategically to stay ahead of the curve.
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How To Integrate AI into Power BI
AI is helping businesses use ML models to dig out hidden insights and make smarter decisions. Combining AI with Power BI unlocks advanced capabilities and lets you do the same. Here is a step-by-step guide on how to integrate AI into Power BI:
Use Text Analytics and Vision Features
Power BI allows you to use Azure Cognitive Services for text and image analysis directly within Power Query. This helps enrich your data and includes:
- Sentiment Analysis: This analyzes text data and assigns a sentiment score between 0 (negative) and 1 (positive). It is ideal for analyzing customer feedback and social media comments to understand overall sentiment.
- Key Phrase Extraction: It scans unstructured text and extracts the most important phrases. You can think of it as a summarization.
- Language Detection: AI detects the language of a given text and provides both the language name and its ISO code.
- Image Tagging: For image data, AI automatically generates tags based on identifiable objects, people, landscapes, or actions.
These AI-driven Power BI features are run on the Power BI service and require Power BI Premium. The good news? You don’t need a separate subscription for Azure Cognitive Services to access all these.
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Enable AI Workloads
Before using the Text Analytics or Vision features, you need to activate AI workloads in your Power BI Premium capacity.
- Open the admin portal in Power BI.
- Select the capacity you want to configure.
- Under Workloads, enable the AI workload option.
- Set a memory limit for the AI workload. (Recommended: 20% of your total memory allocation)
By doing this, Power BI allocates the necessary resources for running cognitive services smoothly.
Apply AI Functions in Power Query Editor
You can now start applying AI functions directly in Power Query Editor. Follow these steps:
- Open Power Query Editor in Power BI Desktop.
- Navigate to the Home or Add Column tab.
- Select the Text Analytics or Vision option.
- Choose the AI function you want to use, such as Sentiment Analysis or Key Phrase Extraction.
- Select the data column you want to transform.
For example, to perform sentiment analysis in Power BI:
- Choose the Score Sentiment function.
- Select the column containing the text data.
- If needed, specify the text’s language.
- Click Apply to add a new column that contains the sentiment scores.
Once applied, the new column with AI-generated results will appear in your dataset, ready for further analysis.
Conclusion
Many companies have already started to rely on AI to automate business processes. Naturally, integrating AI into business intelligence tools is a natural next step. AI in Power BI is the answer to that prayer for millions of businesses around the globe. From automating repetitive tasks to creating detailed reports and NLQ use, AI is taking BI to a whole new level. Think about all the time you could save and the smart decisions you could make only if your team knew how to make the best use of it!
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