Power BI has components in one of the following categories:

  1. Data visualization
  2. Data preparation
  3. Data modeling

We will look into AI in all of these three categories:

AI in Data visualization:

Key Influencers:

This AI visual tells what the factors are due to which a particular metric changed.

Example: If you want to know what factors increase the happiness of students, it would let you know.

Q&A Visual:

Using this visual, end-users can ask questions when using the report.

Example: Total number of students enrolled.

Insights feature:

It explains why there is an increase/decrease between two points in the data. It also finds where the distribution is different in data. Example: In April there may be more enrollments than Feb. Insights will show which factor contributed to this happening.

AI in Data Preparation:

Automated Machine Learning:

AutoML lets users create and train machine learning models. It currently supports development of Binary classification, Regression and General Classification models. Forecasting and other models may be added later. 

Example: Using the survey data it will determine the relation between different factors such as curriculum, residence and happiness etc.

Cognitive Services:

They mainly include:

  1. Image Tagging
  2. Extracting Key Phrases
  3. Detecting Language
  4. Score Sentiment

Example: This service will determine the language in which the survey is filled, it will extract the key phrases and detect the sentiments using those phrases.

AI in Data Modeling:

Data models in PowerBI allows you to build reports on top of it. A well-built model allows you to use features such as Q&A and Quick Insights. Quick Insights uses your model to find various trends in your data whereas Q&A allows users to ask natural language questions w.r.t data model.

The Key Influencers feature in PowerBI will recommend you the factors to work on to reach your goal.

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