Sanjeev Mohan dives into why the success of AI in enterprise applications hinges on the quality of data and the robustness of data modeling. Accuracy Matters: Accurate, clean data ensures AI algorithms make correct predictions and decisions. Consistency is Key: Consistent data formats allow for smoother integration and processing, enhancing AI efficiency. Timeliness: Current, up-to-date data keeps AI-driven insights relevant, supporting timely business decisions. Just as a building needs a blueprint, AI systems require robust data models to guide their learning and output. Data modeling is crucial because: Structures Data for Understanding: It organizes data in a way that machines can interpret and learn from efficiently. Tailors AI to Business Needs: Customized data models align AI outputs with specific enterprise objectives. Enables Scalability: Well-designed models adapt to increasing data volumes and evolving business requirements. As businesses continue to invest in AI, integrating high standards for data quality and strategic data modeling is non-negotiable.
How Data Modeling Influences Decision Making
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Summary
Data modeling is the process of structuring and organizing data to make it easier to analyze and derive actionable insights. It plays a critical role in decision-making by enabling businesses to create meaningful relationships between data points, simulate scenarios, and design strategies aligned with organizational goals.
- Focus on clarity: Develop data models that simplify complex data into understandable structures, ensuring it aligns with business needs and supports targeted decision-making.
- Consider scalability: Build data models that can grow with your business and adapt to changing requirements or larger datasets over time.
- Integrate decision logic: Go beyond system simulations by clearly defining decision-making policies, including when and how decisions are made based on the available data.
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Kimball's framework was a revelation. Using metric trees to add even more structure to data is an exciting, natural evolution. When I first encountered Kimball’s data modeling framework, I realized that much of the work we were doing - ingesting, transforming and stitching together data pipelines - was essentially about structuring the raw data to make it easier to analyze downstream. By organizing data into facts and dimensions, Kimball created a structure that allowed us to create meaningful, actionable reports. This framework provided a solid foundation for data engineers and BI developers alike, ensuring that data could be more easily consumed and analyzed. But once we modeled facts and dimensions, was the job done? Not at all. As the need for data consumption evolves, so do the use cases—moving beyond basic reporting and into deeper analytics and actionable insights. Business users want to - Root-cause metric changes. - Understand drivers of output metrics performance. - Assess metric performance against targets or budgets. - Pace and forecast metric trends. - Simulate scenarios to inform decisions. - Set metric goals In theory, with the facts and dimensions in place, we could manually write and stitch together queries for each of these workflows. But, this is far from efficient. As with any framework, the value lies in pushing the frontier of productivity and user enablement. Kimball gave us the base structure, but it didn’t quite capture the full potential of how data could work together to drive insights. After we’ve structured facts and dimensions, it becomes clear that metrics should be modeled as a primary concept. But even that isn’t enough to fully realize the value of our data. If metrics are the building blocks, the next step is modeling the relationships between them. This is where metric trees come into play—serving as the missing piece in the evolution of data modeling frameworks. A metric tree, built on top of your existing data platform, can capture the relationships between metrics, streamline and even automate many of the common analytical workflows, making them easier and more efficient for business teams. In a sense, metric trees represent the a logical evolution of frameworks like Kimball—continuing to push the boundaries of data standardization and enabling organizations to not just access data, but to deeply understand and act on it.
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Modeling the world isn’t the same as making decisions in it! A common trap in applied AI, OR, and digital transformation is to spend months building a perfect simulator… a beautiful digital twin with clean architecture, smooth animations, and every physical nuance modeled. And yet… when it comes time to actually make decisions? No policy. No framework. Just dashboards and “what-if” buttons. Here’s the core mistake: We confuse modeling the system with modeling the decisions. A simulator helps you observe behavior. A policy helps you choose actions. They serve different purposes. At Toyota North America, I always separate the two: 🔹 Modeling the system (the physics, flows, stochastic processes) gives you a sandbox to play in. 🔹 Designing the policy means deciding how you’ll act over time, based on what you observe in the system. Want to optimize shuttle routing at a port? Great. Simulate vehicle movements, fueling stations, labor shifts, arrival patterns. But then design a policy that says when the shuttle leaves, who it picks up, and how it adjusts when demand surges. 🚫 A simulator is not a decision model. ✅ A simulator is the environment. Your policy is the intelligence. Throughout my career I’ve had entire projects spin in circles because nobody took the time to define: • What’s the decision? • When is it made? • Based on what information? • Using what logic? These four questions can help drive policy design and be the difference between pretty analytics and real ROI. Build your digital twin if you need to. But don’t forget to teach it how to act!