In the fast-paced realm of data science, building machine learning models that push the boundaries of innovation is exhilarating. However, as seasoned data scientists know, success isn't solely about perfecting an algorithm. The real challenge lies in translating that technical prowess into tangible business impact. This is where the journey gets truly fascinating.
Data scientists often speak in terms of algorithms, accuracy, and innovation, while business stakeholders focus on ROI, efficiency, and customer satisfaction. Bridging this communication gap is not just important; it’s the key to transforming a model from a theoretical exercise into a driver of real business outcomes. In this blog, I'll delve into how to bridge this gap, drawing from my experiences in the FMCG, Cement, Healthcare, and Hospitality industries.
Understanding Stakeholder Needs: The Foundation of Impact
The first step to making your machine learning models impactful is understanding what your stakeholders really need. Often, it’s not the most cutting-edge model that impresses but the one that aligns with business priorities.
For instance, while working in the FMCG sector, I developed a demand forecasting model that predicted the Real Unit of Volume (RUM) for various combinations—Plant, Depot, Chain, and SKU. As thrilling as it was to work at such a granular level, the real win was how the brand used these insights. The right forecast at this level helped them procure the right raw materials, optimize production, balance logistics, and improve market visibility. In short, it wasn’t just about prediction accuracy it was about how that accuracy was applied to streamline the entire supply chain, giving the business a strategic edge.
The lesson? It’s not just about the technicalities of the model but how it serves the broader business needs.
Demand Forecasting in FMCG: An Example of Business Impact
Let's zoom in further on that demand forecasting model for the FMCG brand. The goal wasn’t just to create a demand forecast but to offer precision at the Plant, Depot, Chain, and SKU levels. This granularity was critical to getting a clear picture of future demand, which directly influenced the brand’s ability to procure the right raw materials. This, in turn, optimized their production schedules and helped them balance logistics. By delivering the right products to the right places at the right times, the brand increased their market visibility and minimized stockouts, ultimately improving customer satisfaction.
This experience underscored the importance of tailoring machine learning models to solve very specific business problems. And as a result, I helped my client not only save millions but also build stronger relationships with retailers, enhancing their competitive advantage.
Simplifying the Complex: Communicating with Stakeholders
One of the hardest parts of working in data science is explaining your models in a way that makes sense to non-technical stakeholders. In my career, I learned this the hard way. I once spent an entire presentation explaining the technical details of a price discounting (PD) model we developed for dealers. The model helped our client understand how discounts affected the wholesale price , giving them advantage to control their WSP while keeping dealers happy .
While the model was a success, I could see I was losing my audience with all the technical jargon. So, I pivoted. Instead of diving into details, I focused on how the model helped the client gain an edge in controlling their WSP across different market conditions. This shift in communication turned a technically complex project into something that resonated deeply with their business strategy. The takeaway here? Simplifying the complex is a skill every data scientist must master.
Where the Rubber Meets the Road: Deploying Models Effectively
Building an accurate model is one thing, but deploying it effectively is another. I learned this lesson while working on a project that predicted property prices for the next six months. The model factored in various economic, social, and market trends essentially every variable you could imagine.
But the challenge wasn’t just building a great model; it was ensuring that the model could be operationalized. We needed to collaborate with IT, make sure the data pipelines were robust, and ensure that the model was regularly retrained as new data came in. A great model sitting on a hard drive is useless; a deployed model that's actively informing business decisions is priceless. The project’s success hinged on making sure the model could be easily integrated into the client’s day-to-day operations, offering actionable insights that adapted as market conditions evolved.
Conclusion: Bridging the Gap from Experience
The art of bridging the gap between machine learning and business impact lies in three key actions:
Understanding stakeholder needs
Simplifying the complex
Deploying models effectively
To do so, you must interact with multiple teams and get their versions of the challenges at hand. It’s vital to pay attention during meetings so that you can ask the right questions. The harder you work to understand the business during the early days of a project, the easier it will be to deliver a solution that drives real impact when the project reaches completion.
And if there’s one piece of advice I’d like to leave you with, it’s this: The key to data science success is aligning technical brilliance with business objectives. Stay tuned for more insights—and if you're interested, I may write about how to ask the right questions in future blogs. After all, the devil is in the details!