Our leadership team wants to use AI to predict resource constraints and inventory shortages, but our historical operational logs are scattered across three different legacy platforms and contain contradictory timestamps. How do we sanitize this operational data so the AI can actually forecast our capacity?

Question

Tyler Smith · Accepted Answer

To make forecasting work, you must stop trying to clean up years of historical chaos across multiple systems. That is a black hole that will suck up your team's time and yield zero operational value. Instead, establish a single source of truth starting today.

Pick one master system, whether it is your ERP or a centralized data warehouse, to act as the gold standard for your operational timestamps. Define exactly which system owns the definitive start and end time for every transaction or inventory movement.

Next, update your Accountability Chart to assign absolute ownership of data entry for this master system. The person in that seat must ensure that every entry meets your newly defined standards. If the data is not in the master system, it does not exist.

Once you have thirty days of clean, standardized data in this single source of truth, you can feed it to your predictive AI models. Do not worry about the years of dirty historical data. AI models perform far better on a short run of highly accurate, standardized data than on a massive pile of inconsistent legacy records. This systematic approach ensures your business remains system-dependent and ready to scale.