The logical layer that lets a half-migrated roll-up still answer one question consistently — model once, federate the data, generate insights anyway.
Everest can't wait for every plant and export desk to migrate to SAP before it gets answers. The fix isn't one warehouse — it's a shared ontology (so everyone means the same thing) over a data mesh (each category owns its data as a product), with a semantic layer that federates them. Insights generate today; they just carry a confidence flag where a category isn't on SAP yet.
Ten classes everything maps to. The Plant is the keystone: it's where category, leader, entity and geography reconcile.
79% of revenue is already site-grain actual; the rest is read in place from legacy site/export systems and reconciled — no big-bang migration required.
Every metric has one definition and a grain. The layer federates it across on-SAP and legacy domains, flagging where a value is allocated.
| Metric | Definition | Grain | How it federates across categories |
|---|---|---|---|
| Revenue | Σ recognized revenue | plant · order | actuals where on SAP; allocated from area where not |
| Adjusted EBITDA | revenue − COGS − SG&A (+ add-backs) | category · entity | entity P&L normalized to one chart of accounts |
| Organized + export revenue | annuity-like listing / contract revenue | contract | from SAP SD / DMS across all channels |
| Organized + export mix | organized + export ÷ revenue | category | federated — same formula, many sources |
| DSO | AR ÷ revenue × 365 | entity · plant | legacy/export accounts measured at area grain, flagged |
| Gross margin | (revenue − COGS) ÷ revenue | order · category | mapped via canonical cost categories |
| Repeat-offtake rate | expansion − attrition on base | customer / account | resolved across duplicate customer records |
Entity resolution matches legacy plant / category / brand codes to one canonical node — so the export desk's data lines up with everything else.
Query reads each category's data product in place; the semantic layer maps native SAP / DMS fields to canonical metrics.
Where a category reports at area level, allocation disaggregates to plant on learned drivers and marks it an estimate with a confidence band.
Allocated parts must tie back to the source total; anomalies and duplicate customer / account records & suppliers across categories are surfaced.
This is not theoretical — it's how this cockpit already works. The Story, Briefing and 360 views read the same governed metrics over on-SAP and legacy categories alike; 79% of the numbers are site-grain actuals and the balance is SAP-allocated and labelled. As each category migrates to SAP, its data product's grain rises and estimates flip to actuals — the mesh closes itself.