Migrations Data & Model Migration - FAQs

Kept historical data can deliver revenue forecasting that learns from cycles you have already lived through. Churn prediction that knows which customer behaviours preceded historical churn. Demand planning that adjusts for seasonal patterns specific to your business. RFM segmentation grounded on the actual customer base, not industry benchmarks. Anomaly detection that recognises what normal looks like in your data. Each of these works better with five years of history than with eighteen months. Replacement projects often cut history at the migration boundary.

Historical data is important because most organisations underestimate the value of the years of historical transaction data sitting in their existing system. When a replacement happens, this data is often left behind or transferred in a degraded form. Historical data is the asset that makes machine learning, forecasting, and pattern detection possible. Cohort analysis needs years of customer history. Demand forecasting needs years of sales history. Replacement projects regularly throw this asset away because the migration plan cannot afford to bring it across cleanly.