Overview of Microsoft's Data Mining for SQL Server Analysis Services

Jul 11, 2024

Overview of Microsoft's Data Mining for SQL Server Analysis Services

Introduction

  • Overview of how Microsoft’s data mining features work in SQL Server Analysis Services (SSAS).
  • Demo is based on Microsoft’s online tutorial using the AdventureWorks Data Warehouse 2012.
  • Steps include attaching MDF and LDF files to setup demo database.

Scenario Setup

  • Database: AdventureWorks Data Warehouse 2012.
  • Scenario: Targeting customers who don’t have children for a new store opening.
  • Objective: Predict the number of children in a mailing list lacking this information.
  • Use factors like marital status, gender, income, etc. for prediction.

Demonstration Steps

  1. Database Attachment: AdventureWorks Data Warehouse 2012.
    • Attach the MDF and LDF files.
    • Setup involves setting the database in the correct folder and running setup code.
  2. Create Data Mining Demo Database:
    • New database to be used for mailing list.
    • Cheating by copying relevant data from AdventureWorks (e.g., marital status, gender, income).
    • Generate data for new potential customers.
  3. Current vs Potential Customers Data:
    • Comparison of current customers and fictitious potential customers.
    • Analysis of demographics and purchase likelihood.

Data Mining Process

  1. Setting up Data Mining Models: Macro-Level Steps
    • Create a data source view using current customer data.
    • Use the view for data mining model.
    • Wizard-based walkthrough for data mining setup.
    • Choose different mining models like neural networks, regression, clustering.
    • Key columns and input columns selection.
    • Discretize values for grouped analysis.
  2. Running Predictions:
    • Predicting number of children based on demographic inputs.
    • Setting up testing data for verifying predictions.
    • Name and save the data mining model.

Report Generation

  • Publishing Models: Publish to analysis server to view reports.
  • Reports Insights: Show comparisons and insights into demographic data.
  • Lift Chart: Evaluate predictive capabilities, comparing models like neural network and clustering.
  • Additional Reports and Analysis: Combine different data mining models to optimize predictions.

Conclusion

  • Utility: Provides quick insights and predictive analysis without extensive coding.
  • Reports: Basic but useful for demographic and predictive insights.
  • Tutorial Recommendation: Encouraged to follow Microsoft's tutorial for detailed steps and better understanding.
  • Compatibility: Works with SQL Server 2016, though materials might be based on older versions (e.g., SQL Server 2014).
  • Future of Data Mining: Potentially moving to newer structures and integrations with tools like Power BI.

Final Note

  • Check Microsoft’s tutorial for an in-depth understanding.
  • Explore practical applications by working through the tutorial.

Additional Resources

  • Microsoft’s official data mining tutorial.
  • SQL Server Analysis Services documentation.

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