Introduction to Predictive Querying
SQL Server Analysis Services (SSAS) empowers you to go beyond traditional data analysis by incorporating predictive capabilities. This allows you to forecast future trends, identify patterns, and make more informed decisions based on data-driven predictions.
Predictive querying in SSAS integrates machine learning algorithms directly into your data models. This means you can ask questions of your data that not only retrieve existing information but also predict future outcomes, understand customer churn probabilities, or recommend products.
Key Concepts
- Data Mining Models: SSAS utilizes various data mining algorithms (e.g., clustering, classification, regression, association rules) to build predictive models.
- Predictive Queries: These are special queries that leverage trained data mining models to generate predictions for new data or to score existing data.
- DMX (Data Mining Extensions): A query language specifically designed for interacting with SSAS data mining models.
- Integration with BI Solutions: Predictive insights can be seamlessly integrated into dashboards, reports, and applications built using other Microsoft BI tools.
Building and Deploying Predictive Models
The process typically involves:
- Data Preparation: Ensuring your data is clean, structured, and relevant for modeling.
- Model Creation: Selecting appropriate data mining algorithms and training them on your historical data within SSAS.
- Model Deployment: Making the trained models accessible for querying.
- Querying Predictions: Using DMX or other interfaces to extract predictions from the deployed models.
Example: Customer Churn Prediction
Imagine you want to predict which customers are likely to stop using your service (churn). You can train a classification model on historical customer data, including attributes like usage patterns, demographics, and service interactions. Once trained, you can query the model to get a probability score for each customer indicating their likelihood of churning.
DMX Query Example (Conceptual)
Here's a simplified DMX query to predict churn for a hypothetical customer:
SELECT
[Customer].[CustomerID],
Predict([ChurnModel].[IsChurn]) AS PredictedChurn,
PredictProbability([ChurnModel].[IsChurn]) AS ChurnProbability
FROM
[YourCube].[Customer]
WHERE
[Customer].[CustomerID] = 'CUST123'
PREDICTION JOIN
[YourCube].[Customer] ON [Customer].[CustomerID] = [YourCube].[Customer].[CustomerID]
USING
[ChurnModel]
Benefits of Predictive Querying
- Proactive Decision Making: Identify potential issues or opportunities before they fully materialize.
- Personalized Experiences: Tailor recommendations, offers, and services based on predicted customer behavior.
- Resource Optimization: Allocate resources more effectively by anticipating future demand or risks.
- Enhanced Business Intelligence: Gain deeper, forward-looking insights that drive competitive advantage.