Descriptive analytics summarizes historical business data to answer the question: “What happened?” It uses aggregates, visualizations, segmentation and explanatory models to reveal patterns in past behavior — for example, customer purchase paths in an e‑commerce store or repayment patterns in a loan portfolio.
Why it matters
- Provides the foundation for predictive and prescriptive analytics by turning raw history into structured insights.
- Informs model design and feature engineering (explanatory models often feed predictive models).
- Reduces risk by highlighting past failures and opportunities before they are repeated.
Approaches and focus
- Determine the type of analysis your use case needs, is it cross-sectional, longitudinal or panel? The analysis approach and methods follow from which of these types of dataset your use case fits.
- Graph-based analysis can be a very powerful tool for descriptive analytics. Traditional machine learning approaches work can be sufficient for Independent Identically Distributed data, when this is not a reasonable assumption, you need analysis methods that account for the dependencies in the data. Graph based analysis do this naturally. A lot of enterprise data reside in relational databases. A common first bottleneck in using graphs for descriptive analytics is related to getting the data from native relational format to a graph analysis model. For this reason, there is a complete section of this repository dedicated to transforming relational data to graphs. Please see this video for a summary of this process.
For an overview video, please see this video. For some samples, please see this playlist
This repository covers operational or analytical datasets that are relational. In particular, Large Language Models are not part of any solution here. Solutions developed here utilize machine learning methods that have served the knowledge discovery in databases for a long time. The following are common use cases for descriptive analytics:
- Traffic and Engagement Analysis: Analyzing website traffic data to understand user behavior, popular pages, and engagement metrics.
- Sales and Revenue Analysis: Summarizing sales data to identify top-selling products, seasonal trends, and revenue patterns.
- Customer Segmentation: Grouping customers based on purchasing behavior, demographics, or engagement levels.
- Financial Reporting: Creating summaries of financial data, such as profit and loss statements, balance
- Inventory Management: Analyzing inventory levels, turnover rates, and stock movement patterns.
- Social Media Analytics: Summarizing social media interactions, follower growth, and content performance
Explanatory models are commonly used for:
- Customer Churn Analysis: Identifying factors that contribute to customer attrition.
- Fraud Detection: Analyzing transaction patterns to identify unusual behavior indicative of fraud.
- Incident Analysis: Understanding the root causes of operational incidents or failures.
- Product Performance Analysis: Evaluating product features and their impact on sales or user satisfaction.
- Marketing Campaign Analysis: Assessing the effectiveness of marketing campaigns and identifying key drivers of success.
- User Behavior Analysis: Understanding how users interact with a product or service to improve user experience
- Risk Assessment: Evaluating factors that contribute to risk in various domains, such as credit risk or operational risk.
Contributions are welcome!
Feel free to open issues, submit pull requests, or suggest new recipes. If you have questions, please open an issue or contact the repository maintainers.
This project is licensed under the MIT License.
Made with ❤️ for the data community.