One model, any system

Every vendor analytics product reads its own system and stops at its own wall. Your warehouse has no wall: the ERP lands beside the CRM, the point of sale, and the plant floor, and one join answers questions no single vendor can even see.

RuleBlend at the shared dimensions: one customer, one cost center, one worker, one calendar. Systems meet where their keys agree, and nowhere else.

Neverbuy a second analytics product to see a second system. The walls do not merge; the meters do.

◆ The pictureyour systems, left to right, into one model.

Every system you run, one meeting place you own. Hybrid estates are the everyday case: Workday people beside Oracle or SAP money.

◆ The wall, product by producteach vendor analytics reads exactly one estate.

Product	Reads	Cannot read
Oracle’s analytics	Oracle Fusion. Covered on the decoder page.	Your CRM, your point of sale, your plant systems.
SAP’s analytics stack	SAP systems. Covered on the limits page and the cost page.	Everything that is not SAP.
Workday’s analytics	Workday. Covered on the pricing page.	Your finance system, if it is not Workday’s, and everything else.
Your warehouse	All of the above, plus anything with an export.	Nothing you choose to land in it.

This is not a missing feature awaiting a release. Each product’s wall is its business model: it exists to keep analysis inside the estate its vendor sells. The one place your systems can meet is the place none of those vendors runs, which is exactly the warehouse this catalog teaches you to own.

◆ The blends, workedquestions that need two systems, answered by one join.

The blend	Joins on	The question it answers
Workday people against Oracle or SAP money	Cost center	Fully loaded cost per department across a hybrid estate: the everyday case, already proved by the cost center bridge.
CRM against the ledger	Customer	Which closed-won deals actually invoiced and collected: the sales claim reconciled to receivables, pipeline to cash in one view.
Point of sale against inventory	Item and location	Margin by store by day, sales against stock and cost, with shrink surfacing as the difference nobody can see across two systems.
Marketing spend against revenue	Customer and calendar	Campaign payback on invoiced revenue instead of platform-reported conversions: cost per acquired customer the ledger agrees with.
Syndicated market data against sales	Item and market	Your sell-in beside the panel’s sell-through, Nielsen-style: share, velocity, and where distribution lags the category. Panels blend only in a model you own.
Usage signals against revenue	Customer	Churn risk with money attached: product usage and support tickets beside invoiced revenue and margin, so renewal models rank dollars, not logins.
Receivables against open deals	Customer	Which open deals sit with customers already past due: the credit-hold surprise sales forums rant about, caught before the quarter depends on it.
Credit bureau against receivables	Customer	Exposure that moves with the ledger: ratings beside open receivables and payment behavior, so credit limits reflect what they owe and how they pay.
Supplier filings against payables	Supplier	Concentration and health in one view: bureau or filing data beside open orders and payables, the single-source-of-failure check procurement asks for.
Plant output against labor cost	Cost center and calendar	Cost per unit produced: manufacturing output joined to the payroll cost the labor cost report already proves to the ledger.

None of these are invented. Closed-won deals that never invoice are a documented revenue-leakage pattern. Store owners fill the payment vendors’ own community forums, year after year, asking why the day’s sales match neither the books nor the bank. Plant writing calls the standard-versus-actual review a debate about estimates, and sales forums rant about credit holds discovered at shipment. Every one is a two-system question, practitioner-reported, and every one dies inside a single vendor’s wall.

The proof of mechanism is already on this site: the cost center bridge joins Workday people cost to ERP spend with one mapped dimension, and every module star shares it. A CRM, a register, or a production system enters the model the same way: land it raw, map its keys to the shared dimensions, and every existing report can suddenly see it.

◆ Why this changes what the site isnot ERP analytics. Your analytics, starting from the ERP.

The ERP is where we start because it is where the money is proved: ties, crossfoots, and the ledger discipline every page here teaches. But the model those pages build is not an ERP accessory. It is your company’s one place where systems agree, and each new source is a mapping exercise, not a procurement cycle.

The same shelf serves the assistant: one that can read the blended model answers across systems, which no single vendor’s assistant will ever be allowed to do. Own the meeting place, and everything that plugs into it compounds.

Use case

Problem

Cross-system questions, pipeline to cash, margin by store, cost per unit, die in the gaps between vendor analytics products that each read only their own estate.

What we build

Your warehouse as the meeting place: the ERP model this catalog teaches, with CRM, point of sale, or plant data landed beside it and mapped to the shared dimensions.

What you get

Answers no single vendor can see, on numbers the ledger ties already prove, with each new system a mapping exercise instead of a new subscription.

Which two of your systems have never met?

We land them in one model and make the join, in your cloud.

Talk to us

Terms on this page

ERP: Enterprise resource planning, the system of record this catalog starts from.
SAP: The vendor of S/4HANA, one of the finance systems this model starts from.
HR: Human resources. The people half of a hybrid estate.
CRM: Customer relationship management, where sales pipeline lives.
POS: Point of sale, the registers and the transactions they capture.
MES: Manufacturing execution system, the plant floor’s system of record.
shared dimension: One customer, cost center, worker, or calendar table every source maps to. Where systems meet.
syndicated data: Purchased market panels, Nielsen-style, describing the category you sell into.
churn: Customers leaving. Worth modeling in dollars, not counts.
credit bureau: An outside rating of a customer’s or supplier’s financial health.
blend: A question answered by joining two systems’ data on a shared dimension.
land raw: Copying a source’s data into the warehouse unchanged before any mapping.
estate: Everything one vendor sells you. Each analytics product’s reading limit.