### 4.1 Analytics Readiness The data is clean (100% completeness, 100% referential integrity) but not yet ML-ready. It consists of five monthly tables (January to May 2022) holding 446 records in total, and the largest, January, accounts for only 26% of them. No validated joins exist between the tables, and none of the seven candidate model types is rated high viability. The five explicit dimension hierarchies support segmented reporting today, but the volume and the month-by-month split limit predictive work. ### 4.2 Strategic ML Opportunities | Model Type | Prediction Target | Viability | Applicable Tables | |------------|-------------------|-----------|-------------------| | Time-Series Forecasting | Future values of a measure over time | Low | (none) | | Anomaly Detection | Outlier or unusual records | Low | (none) | | Regression | Continuous target | Low | (none) | Anomaly detection offers the best near-term return. It works on a few hundred records, and for a commerce audience it can flag unusually large or atypical deal entries for review before they distort reporting. Forecasting is the weakest option for now: five monthly snapshots give only five time points, too few for a reliable trend. Before any model work, the five monthly tables need to be stacked into one consolidated table with a month indicator, once their structures are confirmed to match. Because the evidence does not identify the numeric measures, the team should first confirm which fields can serve as targets. ### 4.3 Investment Recommendations - **Consolidate the five monthly tables (next 2 weeks).** Stack the January to May 2022 tables into one analysis table with a reporting-month field. This raises the usable sample from at most 115 rows (January) to 446 and is the prerequisite for every model type above. - **Pilot anomaly screening on the consolidated table (weeks 3–6).** Use the five dimension hierarchies to compare each record against its peer group, and route outliers to analysts. This delivers value without needing a large history. - **Build the one proposed engineered feature (weeks 4–6).** Validate it against the consolidated table before using it in any regression experiment, and keep regression exploratory until a target measure is confirmed. - **Defer forecasting until more months are loaded.** Revisit once the monthly feed extends well beyond the five periods held in the current tables, and add each new month using the same structure so history accumulates cleanly.