1. Defining success
Welcome back! I hope you enjoyed the first chapter in Demystifying Decision Science.
2. Success in Decision Science
In decision science, much of the focus is on building models, analyzing trends, and extracting insights from raw data—essential and valuable tasks. However, without a clear definition of what "success" means, even the most sophisticated efforts risk falling flat. You might build models that never get implemented, produce analyses that no one reviews, or generate insights that elicit nothing more than a shrug.
Achieving success in data science requires a definition of success that you and your customer can agree on.
So, let's dissect the idea of success.
3. Understand what success looks like to your customer
First, you have to put on your customer hat. Whether your customer is a department within the company, a paying client, or any other stakeholder, it's vital to understand their needs and requirements. What problems do they need to be solved? What sort of outcomes would be considered a win in their eyes? What model performance is needed by when and at what cost?
Imagine you're building a predictive model to forecast sales for a restaurant chain. While technical accuracy is important, your client is likely more concerned with how the model impacts their decisions. Can it pinpoint high-potential regions for targeted marketing? Can it forecast demand precisely enough to optimize inventory and reduce waste by a measurable percentage?
4. Measures of success
Once you understand the customer view of success, you can put on your performer hat. It's your job to translate those requirements into tangible metrics and targets.
These metrics can fall into a few broad categories:
Performance: How well does your model or analysis work? Accuracy, precision, recall - these are where the technical guts of your project come under scrutiny.
Time: Did you deliver your project on schedule? A project needed in March may be viewed as a failure if it arrives in June.
Cost: Was the project within budget? Decision science doesn't exist in a vacuum; it has to fit within business constraints.
Quality: Is your code and analysis well-structured, documented, and reproducible? Think long-term: will others be able to pick up your work if needed?
Stakeholder Satisfaction: This is more qualitative. Overall, are the customers satisfied with the product? This is tightly linked to whether you communicated effectively. Were the insights actionable and readily presented? Did the project enhance trust in data-driven approaches?
Business Impact: Ultimately, did the project improve the bottom line or the customer's key objectives?
5. Minimum Viable Product (MVP)
It's easy to get caught up with a vision of an all-encompassing, super-intelligent solution. However, focusing on a minimum viable product (usually called an MVP) is often very useful.
Think of an MVP as the simplest functional version of your solution. It's focused on delivering the core value proposition and answering a key question. And, critically important is that it can be readily implemented!
Here's the key takeaway: Start decision science projects with a shared understanding of what success looks like. Agree on these metrics upfront. Communicate them, track them, and learn to tell the story of how your work moved the needle. Decide what constitutes a Minimum Viable Product and drive toward its production. That's the most powerful way to demonstrate the value of decision science and to gain continued support on future projects.
6. Let's practice!
Let's practice these ideas!