Your model has a 98% accuracy score. So why is nobody using it?

To every data scientist, ML engineer, and BI analyst: Your model has a 98% accuracy score. The code is beautiful. The architecture is scalable.

So why is nobody using it?

This is the "Last-Mile Problem" of analytics, and it’s one of the most painful forms of "Doing Data Wrong." I’ve seen it countless times: a technical team delivers a brilliant model that ends up sitting on a shelf, unused, while business leaders go back to making decisions with Excel and gut instinct.

The model isn't the problem. The translation is.

As someone who has built data teams and also sat on the executive side at places like Itaú, Ambev, and FALCONI, I’ve lived on both sides of this communication chasm. Technical teams present their work by talking about ROC curves, F1 scores, and clever feature engineering. Business leaders hear a foreign language, nod politely, and move on.

The work fails not because it's technically flawed, but because it was never translated from the language of code to the language of commercial impact.

Let's fix it. Here is a simple playbook for tech professionals to ensure your work doesn't just run, but lands.

The 4-Step Playbook for Translating Data to Value:

1. State the Business Problem First. Before you mention a single algorithm, state the problem in their language.

   • Don't say: "We built a multi-class classification model using a gradient-boosted tree architecture..."

   • Do say: "Last quarter, we lost 15% of our highest-value customers, and we didn't know why they were leaving until it was too late."

2. Name Your Model for its Business Function. Drop the technical jargon. Give your model a name that describes its job.

   • Don't say: "This is the 'Churn Propensity Model v3.2'."

   • Do say: "This is the 'Early Warning System for At-Risk Customers'."

3. Translate Technical Metrics into Business Outcomes. No one on the business side cares about your AUC score. They care about money, time, and risk.

   • Don't say: "The model achieved an AUC of 0.92."

   • Do say: "Our model's accuracy means we can now identify 9 out of 10 high-value customers who are likely to churn 60 days before they leave. This gives the success team a two-month window to intervene."

4. Explicitly State the "So What?" Don't make them guess. Tell them exactly what action your insight enables.

   • Don't say: "Here is a list of at-risk customers."

   • Do say: "Therefore, we recommend the Customer Success team immediately launch a retention campaign targeting these 52 customers. Based on their LTV, successfully retaining just 20% of them would protect $250,000 in annual revenue."

     
     

To our Data Professionals: This translation is not about dumbing down your work. It is the most critical and highest-value part of your work. It's what elevates you from a great technician to a true data leader.

To our Managers: Your job is to demand this. If your team is only showing you technical metrics, ask them: "What business problem does this solve? What does this mean for my P&L? And what specific action should I take by Monday?"

Bridging this gap is the key to unlocking the ROI of your data organization. It’s my mission to arm both sides with the tools to do it. This knowledge isn't mine to keep.

If you are ready to stop building models that die on the shelf and start creating real business impact, join my movement. Subscribe to my email list for more no-nonsense playbooks and insights.

And if you're struggling to translate the value of your data work, book a 20-minute, no-nonsense call with me. Let's build your bridge together.

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