¶ Creating Predictive Scenarios in SAP Analytics Cloud: Defining Target Variables and Predictors
SAP Analytics Cloud (SAC) empowers organizations to leverage advanced analytics and machine learning to drive smarter business decisions. A key capability within SAC is the ability to create predictive scenarios that forecast outcomes based on historical data. Central to building effective predictive models is correctly defining the target variable and selecting the right predictor variables.
This article explores the foundational concepts of defining target variables and predictors in SAP Analytics Cloud’s predictive scenarios and best practices for building accurate, actionable models.
A predictive scenario is an analytical model designed to forecast or classify future outcomes based on patterns in historical data. It typically involves:
- Target Variable (Dependent Variable): The outcome you want to predict (e.g., sales volume, customer churn, inventory demand).
- Predictors (Independent Variables): The input factors or features that influence the target (e.g., marketing spend, customer demographics, seasonality).
The goal is to train a machine learning model within SAC that can accurately estimate the target based on predictor values.
Choosing the right target variable is critical as it directly affects the model’s purpose and evaluation.
- Relevant: Aligns with a specific business question or objective.
- Quantifiable: For regression models, it should be numeric; for classification, categorical.
- Predictable: There should be an underlying pattern or relationship with predictors.
- Sufficient Data: Adequate historical data points for training and validation.
- Revenue for next quarter (regression).
- Probability of customer churn (classification).
- Demand volume for inventory (regression).
- Fraud detection flag (classification).
Predictors are the explanatory variables used to estimate the target. Choosing the right predictors ensures the model captures meaningful relationships.
- Domain Knowledge: Leverage business insights to identify factors influencing the target.
- Data Quality: Use clean, consistent, and relevant data sources.
- Variety: Include a mix of numeric, categorical, and temporal variables if relevant.
- Avoid Redundancy: Remove highly correlated predictors to reduce noise.
- Feature Engineering: Create derived variables (e.g., moving averages, ratios) to enhance predictive power.
- Transactional data (sales, orders).
- Customer attributes (age, location, segment).
- Time-based variables (month, quarter, season).
- External factors (market indices, weather data).
¶ How SAP Analytics Cloud Supports Defining Targets and Predictors
- Automated Variable Suggestions: SAC’s machine learning wizard suggests potential predictors based on data analysis.
- Data Preparation: Built-in tools allow cleansing, transformation, and enrichment of data before modeling.
- Visual Data Profiling: Helps explore distributions and relationships to aid variable selection.
- Scenario Builder: Enables easy assignment of target and predictor variables with drag-and-drop.
- Model Explainability: SAC provides insights on predictor importance to refine variable selection iteratively.
- Start Simple: Begin with core predictors and incrementally add features.
- Validate Data: Ensure data consistency and handle missing values effectively.
- Split Data: Use training and test sets to evaluate model performance fairly.
- Iterate Often: Refine predictors based on model accuracy and business feedback.
- Align with Business Goals: Ensure the predictive scenario answers relevant business questions.
- Monitor Models: Periodically review model predictions against actual outcomes to maintain accuracy.
Defining target variables and predictors is the cornerstone of building powerful predictive scenarios in SAP Analytics Cloud. By carefully selecting and preparing these variables, organizations can unlock valuable foresights that support proactive decision-making. Leveraging SAC’s intuitive tools and machine learning capabilities simplifies this process, enabling both data experts and business users to create impactful predictive analytics.