Excel and SQL Server have been used together for extracting, cleaning, exploring, and modelling business data. It is important to distinguish current analytical workflows from historical SQL Server Analysis Services data-mining tools.
A sensible workflow
Load transaction data into a staging table, validate missing values and outliers, create a curated dataset with T-SQL, and export or connect that dataset to Excel, Power BI, Python, or another analytical tool.
Example analytical query
SELECT CustomerID,
COUNT_BIG(*) AS OrderCount,
SUM(OrderTotal) AS LifetimeRevenue
FROM dbo.Orders
WHERE OrderDate >= '2025-01-01'
GROUP BY CustomerID;Legacy compatibility
The original Analysis Services data-mining algorithms and related Excel add-ins are historical technologies. Microsoft says SSAS data mining was deprecated in SQL Server 2017 and discontinued in SQL Server 2022. Do not design a new system assuming that discontinued feature is available.
Current practice
Use SQL Server for reliable storage, data preparation, and governance; choose a separately supported analytics platform for forecasting, classification, clustering, and experiment tracking. Evaluate model accuracy and leakage independently of the database engine.
Documentation
Microsoft’s historical feature reference: https://learn.microsoft.com/en-us/analysis-services/data-mining/data-mining-ssas
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