In developing IHS Markit's Energy & Natural Resources forecasting tools, how do we weigh the inclusion of more variables against maintaining model simplicity and user-friendliness?

Introduction

The development of IHS Markit's Energy & Natural Resources forecasting tools presents a critical trade-off between model complexity and user-friendliness. We must balance the inclusion of more variables for accuracy against maintaining simplicity for ease of use. This decision impacts our product's effectiveness, user adoption, and market position.

I'll address this challenge by:

1. Clarifying the context and constraints
2. Analyzing the product and its ecosystem
3. Identifying key metrics and designing experiments
4. Developing a decision framework
5. Providing recommendations and next steps

Step 1

Clarifying Questions (3 minutes)

• Based on the industry trends, I'm thinking this tool is primarily used by energy analysts and decision-makers. Could you confirm our primary user segments and their typical use cases?

Why it matters: Helps tailor the solution to user needs and behaviors Expected answer: Energy analysts, corporate strategists, and government policymakers Impact on approach: Would influence the balance between complexity and simplicity

• Considering the competitive landscape, I assume accuracy is a key differentiator. How does our forecasting accuracy compare to competitors, and how critical is improving it to our business goals?

Why it matters: Determines the importance of adding variables vs. maintaining simplicity Expected answer: We're slightly behind in accuracy but leading in ease-of-use Impact on approach: Might justify adding complexity if it significantly improves accuracy

• Looking at our technical infrastructure, I'm curious about our current model's scalability. What are the performance implications of adding more variables to our forecasting model?

Why it matters: Assesses technical feasibility and potential user experience impacts Expected answer: Current infrastructure can handle moderate increases in complexity Impact on approach: Would set boundaries for how many variables we can reasonably add

• Considering our product roadmap, how does this decision align with our long-term strategy for the Energy & Natural Resources suite?

Why it matters: Ensures alignment with broader product vision and strategy Expected answer: Long-term goal is to provide comprehensive, yet accessible forecasting tools Impact on approach: Would influence the balance between short-term improvements and long-term product evolution