Power BI automates the data science behind the creation of Machine Learning models, ensuring that data professionals, developers, and business analysts who don’t have any data science background can easily build high-quality models with power BI predictive analytics. The Artificial Intelligence visualizations highlight the critical features among your inputs that influence the predictions returned by your model the most.
The most important feature is that you can activate the predictive insights in a business process context.
Below is the step-to-step guide on how you can perform Power BI predictive analytics.
Step One: Data Preparation
Dataflows in Power BI enable organizations to unify data from different sources and make it ready for modeling. Dataflows help to ingest data from a growing and large set of cloud-based and supported on-premises data sources that include Azure SQL Database, SharePoint, Dynamics 365, Excel, Salesforce, and more. You can use dataflows to transfer, map, and ingest data from multiple sources. However, here we are using a single data source Dynamics 365 (CRM), for illustration.
- First of all, create an app workspace, or you can go to an existing application workplace.
- Create a data flow and make a connection with the desired entity.
Dynamics 365 and Power BI both share the ‘common data model’ that ensures a seamless connection, and there is no need to map data. Dataflows also enable limited data transformation and cleansing capabilities.
Step Two: Train, Review, and Apply Machine Learning Models
Configure The Desired ML and Prediction Model
Open the newly created dataflow and then navigate to the Machine Learning (ML) tab. Click on ‘Get Started’ in order to apply the most suitable Machine Learning model. The users of Power BI supervise ML; it means that they learn from the known outcomes of past observations in order to predict the outcomes of other observations. The input dataset for training an AutoML model is basically the set of records labeled with the known outcomes. It is essential to select the field you desire to predict during this step.
Power BI uses three Machine Learning models, and the datatype of the desired prediction generally governs the choice.
-
Binary Prediction Model
The binary Prediction Model is used to predict the events that have a binary outcome. The outcome is a probability score identifying whether the likelihood of outcome corresponding to the value of the label being true will be achieved or not.
-
Classification Model
The classification model classifies a dataset into multiple classes or groups. It is used when the predicted outcomes can have more than one possible outcome.
-
Regression Model
The regression model enables the prediction of a value like revenue realization, customer sentiment, etc.
Refresh & Train Data
Power BI divides the historical data that the user provides into testing and training datasets. Different models are used to deliver better predictive performance. However, in some cases, the final model created might use ensemble learning. You will see that Power BI AutoML picks the input fields from the dataset the users provide automatically, and these could be further tuned.
Review The Machine Learning Performance
AutoML generates a Power BI report summarizing the performance of the model during validation, along with the importance of the global feature. The users can review the model report to understand its performance. You can also validate that the critical model influencers align with the business insights about the known outcomes.
Generate Predictions by Applying the AutoML Model to Your Data
If you are completely satisfied with the performance of the created ML model, you can also apply it to the updated or new data whenever your data flow is refreshed. You can do it from the model report; select the ‘Apply’ button in the top-right corner. When you apply the ML model, it creates a new dataflow entity with the suffix enriched that can be used for visualizations.
Step Three: Deliver & Action the Insights
-
Use The Artificial Intelligence Visualizations in Power BI Predictive Analytics to Surface Insights (Power BI Key Influencers)
The key influencers visual in Power BI assists you in understanding how different factors affect the metric that you are interested in. As you have made the predictions in the previous steps, key influencers’ visualizations analyze how various fields in your data influence the predicted value. The predicted value is quite valuable for the business decision-makers.
Using the enriched entity formed as an outcome of the ML exercise, select the ‘Key Influencer’ visualization, drag the predicted value in the ‘Analyze’ section and then drag the most likely influencing factors in the ‘Explain By’ section. Additionally, it also sections your data and highlights the clusters to offer hidden insights into your data.
-
Embed The Insights in Business Process Context
You can embed the Power BI dashboard and reports in Business apps like PowerApps, Dynamics 365, and Microsoft Teams. It ensures that the predictive insights are now available directly to the business users and can be actioned in context. (Dynamics 365 Predictive Analytics).
Conclusion
Predictive analytics in Power BI is a complex science that encompasses various statistical techniques from predictive modeling, and machine learning to data mining. If you want to master these techniques, contact Xavor Corporation. With all the technical expertise and years of experience in the field, Xavor professionals will help you perform predictive analytics in Power BI seamlessly.
