”Microsoft Power BI extends the Analytics and Business Intelligence capabilities required to meet the dynamic needs of businesses looking for intelligent and intuitive data insights. The suite includes Artificial Intelligence, Cognitive, and Machine Learning capabilities that enrich its native features. They enable users to get the optimal advantage of next-gen BI systems that deliver on advanced requirements such as predictive analysis, NLP, intelligent chatbots, and a host of other features that modern businesses require.
This article will look at how AI, Cognitive, and ML capabilities in Power BI can bring in transformative results to better equip businesses for current sustenance and future growth.
AI insights is a built-in capability of Power BI that can be accessed in the Power Query Editor. It includes pre-configured Machine Learning capabilities that can serve as a base for preparing your data for analysis.
Text Analytics and Vision consists of AI-powered algorithms from Azure Cognitive services to refine data in the Power Query and offers the various services outlined below.
Sentiment analysis analyzes the raw data fields and gives a score of the sentiment of the text-with ‘1’ as positive sentiment and ‘0’ as a negative sentiment. This feature is useful for analysing the user sentiment in large data sets, such as reviews, social media feeds, discussion board comments, amongst others.
Key phrase detection works on the unstructured input text and extracts key phrases from it, which can be used for large datasets, such as social media feeds and data from search engines.
The language detection feature in Power BI detects the language of the text input and returns the language name and its ISO identifier. This is especially useful when dealing with a global set of data and can detect 120 different languages.
Image tagging is an advanced feature of Power BI. It analyses the input image, compares it with close to 2 thousand stored images, and renders a textual description of the image. The scope of the visual recognition is not just limited to the primary image, but the entire landscape of the image with all its constituent images. As an example, Image tagging not only identifies the person in the photograph, but also the background, location etc.
Automated Machine Learning (AutoML) enables users to directly use Machine learning models in Power BI. It uses dataflows to point to the input data and extracts the relevant features used to train and validate the ML model. This model can again be reused with a fresh set of data from the dataflow and get the relevant insights.
The Key Influencers visualization feature in Power BI analyzes data and identifies the key factors that impact the metric. This is shown as a visualization and helps in identifying the key factors that drive a change in the data. As an example, a sales data that is being analysed will have the holiday season as its key influencer.
Chatbots in Power BI have the ability to connect with business data and respond to textual and voice commands to extract the required data and present it in a wide range of formats such as graphs, reports, and dashboards. They include NLP (Natural Language Processing) capabilities that accurately identify the user’s requirements and respond with the required data.
The above-mentioned AI, Cognitive and ML capabilities of Power BI make it an ideal choice for BI and Analytics across various business verticals and requirements. Power BI is now one of the most widely used BI tools in the market, and is preferred for its ease of usage, flexibility, and enabling a self-service analytics model without the need for technical skills.
Exinent offers custom Power BI development services that give you the perfect advantage of getting enriching insights from your data and enabling intelligent business decisions. For more information on how this can work for you, please contact us, and we will be glad to help.