AI features in Power BI
Data Preparation You can enrich your text data with text analytics, analyze sentiment, analyze sentiment, identify key phrases, and detect languages in your text data. With Power BI vision feature you can tag your images, detect your images, and extract text from the images. Both these features need Power BI premium as they use pre-trained Machine Learning Models from Azure Cognitive Services. Data Analysis Power BI offers various AI capabilities and visuals for both developers and business users. Business users can use natural language to query data and examine reports with AI-driven visualizations. With AI driven visuals like key influencers, smart narrative, decomposition tree users can discover hidden insights in the data. Features like anomaly detection, forecasting, and integration with Azure Cognitive Services can identify unexpected patterns, predict future outcomes, and use custom ML models in Power BI. Data Visualization Smart Discovery enables Power BI to automatically discover and visualize your data, it is enabled and delivered through AI. This is also available in different forms within Microsoft Ecosystem. With Natural Language Query in Power BI, you can directly type your question and get quick answers and insights about your data. Preview Features Microsoft Copilot will expand Power BI's AI capabilities, including generating DAX formulas from natural language.10 Ways AI Enhances the UX in Coveo
Coveo is a cloud-based intelligence platform that leverages artificial intelligence and machine learning to provide personalized search and recommendations. This enhancement to the user experience is primarily achieved through the analysis of user behavior, which allows Coveo to understand and predict user needs more effectively. Here's a summary of how this works: Behavioral Analysis: The platform analyzes the collected data to identify patterns and trends in user behavior. It can recognize which content or products are popular among certain user segments, and which search queries typically lead to successful outcomes. Learning Models: Using machine learning algorithms, Coveo creates models that continuously learn from new and historical data. These models become better over time at predicting what content is most relevant to different user intentions. Personalization: The platform personalizes the search experience by tailoring results and content to the user's context and behavior. For example, if a user has shown interest in certain topics or products in the past, Coveo can prioritize similar content in future search results. Recommendations: Coveo also uses users' previous interactions to recommend content or products that they might be interested in. Query Suggestion and Autocomplete: Machine learning models can suggest relevant queries or complete the user's search terms as they type, based on what's been popular or successful in similar searches. Content Optimization: By understanding which content performs well for which users, Coveo can give feedback on how to improve content to match user preferences and improve findability. Dynamic Navigation: The platform can alter the navigation experience on a website by highlighting or prioritizing links, categories, or pages based on what is most likely to be relevant to the user’s interests. Intent Prediction: Coveo can predict the user’s intent even with limited information, which helps in delivering more accurate search results early in the user's search journey, improving the likelihood of a satisfying experience. Contextual Relevance: The context of a search query is taken into account, meaning that Coveo’s platform understands the difference in intent behind similar searches depending on variables like the user's location, device, or time of the day. A/B Testing and Analytics: Coveo also provides tools for A/B testing different search algorithms and UI changes, as well as comprehensive analytics to understand the impact of different personalization strategies on user behavior. By fine-tuning its approach with every interaction, Coveo's machine learning ensures that each user's experience is increasingly personalized and relevant. This optimized information retrieval has the advantage of increasing user satisfaction, improving engagement, and potentially boosting conversion rates, while also reducing the time and effort users spend searching for the information they need.
