Advanced Analytics – Deeper Insights with Power BI - Textnotes

Advanced Analytics – Deeper Insights with Power BI

This module covers advanced analytics capabilities in Power BI. Learners will use advanced DAX functions, what-if parameters, forecasting, and AI-powered visuals like decomposition tree and key influencers. Integration with Python and R enables deeper insights and custom analytics. Hands-on exercises focus on building forecasts, advanced KPIs, and using AI visuals for data-driven decision-making.

1. Advanced DAX Functions: FILTER, ALL, RELATED, USERELATIONSHIP

  1. FILTER: Returns a table filtered by a condition
  2. ALL: Removes filters from a table or column
  3. RELATED: Retrieves a related value from another table
  4. USERELATIONSHIP: Activates an inactive relationship temporarily

Example:

  1. HighSales = CALCULATE(SUM(Sales[SalesAmount]), FILTER(Sales, Sales[SalesAmount] > 10000))
  2. CustomerRegion = RELATED(Customers[Region])
  3. Sales_LastYear = CALCULATE(SUM(Sales[SalesAmount]), USERELATIONSHIP(Dates[Date], Sales[OrderDate]))

2. What-If Parameters

  1. Enable scenario analysis by allowing users to adjust a variable dynamically.
  2. Example: Simulate changes in sales discount rate to see impact on revenue.

Example:

  1. Create a parameter “Discount %” from 0% to 20%
  2. Measure: Adjusted Sales = SUM(Sales[SalesAmount]) * (1 - 'Discount %'[Parameter Value]/100)

3. Forecasting and Trend Analysis

  1. Predict future values using historical data
  2. Power BI provides built-in forecasting in line charts
  3. Useful for sales, revenue, and demand predictions

Example:

  1. Forecast next 6 months of sales based on the previous 2 years using a line chart forecast feature.

4. Decomposition Tree and Key Influencers Visuals

  1. Decomposition Tree: Drill down into metrics across multiple dimensions to identify patterns
  2. Key Influencers: Analyze factors that most impact a measure

Example:

  1. Decomposition Tree: Analyze total sales by Region → Product Category → Customer Segment
  2. Key Influencers: Identify which factors (Region, Product, Discount) drive high sales

5. Integration with Python & R for Advanced Analytics

  1. Run Python or R scripts within Power BI for custom analytics and machine learning
  2. Can generate visuals, apply statistical models, or perform clustering

Example:

  1. Python script to cluster customers based on purchase behavior
  2. R script to create a custom predictive model for revenue