70% to 80% of ML Budgets Are Wasted on Data Cleanup. Here’s Why.

70% to 80% of ML Budgets Are Wasted on Data Cleanup. Here’s Why.

• Insurance
• July 20, 2026

McKinsey research and the recent 2026 supply chain guide by BrainX Technologies show that 70% to 80% of the budget and time in custom ML projects goes into a single task: cleaning up chaotic historical data.

In our experience, this exact narrative hasn’t changed for the last 5 to 10 years. Over the past decade, we’ve seen the market flood with hundreds of advanced data engineering platforms and fancy MLOps tools. There is an endless stack of software promising to automate everything with a few clicks. Yet, in real-world infrastructure, the fundamental bottleneck remains completely untouched. Tech evolved, but all these modern tools still haven't solved the basic, messy problem of getting data ready before a project can even start.

When companies expect to spend resources on advanced math, they still end up spending them on fixing broken timestamps, normalizing units of measure, and sorting out the mess between legacy ERP and WMS systems.

This is why out-of-the-box chatbots fail in real-world logistics. They just can't handle data friction or real-world anomalies like customs delays or sudden tariff shifts.

At DataObrii, we deliver full-cycle data science solutions. We don't just build clean, repeatable data pipelines to fix your infrastructure, we develop, train, and deploy custom ML models that bring real business value.

Whether you need to audit your current data stack or build complex predictive tools from scratch, feel free to reach out to the DataObrii team to talk through your architecture.