Turning data into decisions takes more than powerful tools-it takes structure. With SAP Analytics Cloud (SAC) and its Smart Predict capabilities, businesses can automate predictive modeling and embed AI-driven insights into everyday planning. However, technology alone isn’t enough. Success comes from following a clear project framework.
In this post, we’ll unpack the CRISP-DM and DMAIC methodologies and show how the SAC Augmented Analytics Project Framework helps teams plan, execute, and sustain predictive analytics with confidence
Why Having a Project Framework Is Essential
An effective project framework provides a structured, repeatable, and scalable approach for analytics initiatives. Consequently, it ensures that every step-from defining business goals to deploying predictive models-is clearly planned and measurable. A well-designed framework for SAC Smart Predict projects allows teams to:
- Record critical processes, challenges, and outcomes.
- Replicate successful approaches across teams and departments.
- Improve planning, management, and communication.
- Offer a “comfort factor” for new adopters of Augmented Analytics
Ultimately, a strong framework transforms analytics from one-off experiments into sustainable, data-driven improvements that foster continuous business growth.
Understanding CRISP-DM: The Foundation of Analytics Projects
The CRISP-DM (Cross-Industry Standard Process for Data Mining) is one of the most widely used frameworks for planning and executing data analytics projects. It provides a comprehensive, step-by-step structure-from understanding the business problem to deploying the final model.
While SAC doesn’t enforce a single framework, CRISP-DM principles are foundational for building predictive models and managing Smart Predict workflows. Moreover, its flexibility allows teams to adjust or skip certain phases depending on the project’s complexity or data maturity.
The DMAIC Process in SAP Analytics Cloud
Originally developed for process improvement within Enterprise Performance Management (EPM), the DMAIC methodology underpins the SAC Augmented Analytics Project Framework. It follows a data-driven improvement cycle that continuously refines both predictive models and business processes. As a result, organizations can achieve more accurate forecasts and greater process efficiency
This approach is divided into 5 key phases:
- Define – Identify the problem, improvement opportunity, and project goals. Clarify customer (internal or external) requirements and business expectations.
→ Example: Using Design Thinking to refine a forecasting challenge. - Measure – Collect and assess data related to process performance. Establish baseline metrics to evaluate predictive accuracy and business impact.
- Analyze – Examine the data to identify root causes of poor performance or variations. Use SAC’s Smart Predict features to detect hidden patterns influencing outcomes.
- Improve – Implement solutions that optimize predictive models or business processes. Test model accuracy, adjust input variables, and validate improvements.
- Control – Monitor the improved process to ensure sustainability. Use dashboards and alerts in SAP Analytics Cloud to track key performance indicators and maintain long-term success.
Implementation of the SAC Augmented Analytics Project Framework
Building upon DMAIC, the SAC Augmented Analytics Project Framework translates these principles into a structured, practical approach tailored for predictive analytics projects within SAP Analytics Cloud. Each phase is interdependent, ensuring a logical flow that enhances stability, transparency, and continuous improvement of predictive models across the project lifecycle:
- Establish goals and identify opportunities for improvement.
- Capture relevant data points and define key metrics.
- Evaluate data relationships and model performance.
- Refine predictive outcomes and validate results.
- Apply governance and monitoring mechanisms to maintain performance.
This structured, iterative cycle empowers not only data scientists but also business users to manage predictive analytics projects confidently within SAC, ensuring methodological rigor and alignment with strategic objectives. Furthermore, it promotes accountability, collaboration, and consistent delivery across business units.
Conclusion
A well-structured project framework is essential for maximizing the value of Augmented Analytics in SAP Analytics Cloud. By combining CRISP-DM principles with the DMAIC improvement cycle, organizations can create a robust, scalable, and user-friendly environment for predictive analytics.
In conclusion, whether you’re a business analyst or a data scientist, following this structured approach ensures your SAC Smart Predict projects deliver accurate, explainable, and sustainable results.