The same item, three stock counts
One SKU exists in the POS, the warehouse system, and e-commerce — with three different on-hand figures.
Use case · Retail & Commerce
One order. Five systems. One number you can trust.
A customer checks out today. Scroll, and follow that order from the register, through the five systems that hold your business today, to the trading report and the margin number — and watch what changes when it's only captured once.
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Why retail
Retail doesn’t have a data shortage. It has a distance problem.
Every sale, item, and customer is data. The gap is the distance between the register where a number is born and the trading report the board steers by.
Reporting runs a week behind
Trading reports are assembled by hand. By the time sell-through is compiled, the season has moved on.
Margin surprises at month-end
True product margin is pieced together on request — too late to reprice or reorder.
Ranging know-how in a few heads
Supplier terms, seasonal precedent, and how to range a category belong to senior buyers — and leave with them.
The journey
Seven steps. One number.
The health of the business lives in sales, stock, and margin — born at the register, steered by in the boardroom. The seven steps below walk one order along that route, each removing a place where the number breaks today. Follow the line.
Data FoundationBusiness IntelligenceAI & Automation
Point of saleE-commerceInventory / WMSLoyalty / CRMERP / finance
Where the item is scattered today
One central platformMicrosoft Fabric
This is where the number's journey changes. Instead of being re-counted in the POS, the warehouse system, and e-commerce separately, it lands once — here — and flows everywhere it's needed. Three stock counts become one.
Step 01 · Data Capture & Quality
01The number is born
A customer checks out. Today the product and order data starts in setup screens filled in differently by every buyer and gets patched store by store — whether anyone ever trusts it is decided right here, at the point of sale.
Our approach
We help define what each product, order, and customer record must carry — SKU, category, cost, channel — and enforce quality at entry, so the record is right at the source.
Who feels it
MerchandisingOne clean setup — no downstream data-fix across stores.
FinanceMargin built on real costs, captured at source.
StoresFewer price and category errors at the register.
The payoff — Product and order data is complete from day one — remediation shrinks, and every margin and stock number gets cleaner.
Step 02 · System Integration
02It stops being re-counted
That item used to exist three times — in the POS, the warehouse system, and e-commerce — each with its own on-hand figure.
Our approach
We help map how product and stock data should flow and build a single stock master — so a sale in-store and a sale online draw down the same real figure on their own.
Who feels it
OperationsOne stock truth across every channel — fewer oversells.
MarketingA complete customer view — in-store and online together.
ITGoverned integrations on one platform, not point-to-point exports.
The payoff — Hours of reconciliation disappear, and every channel works from the same stock figure and the same customer.
Step 03 · Business Intelligence Roadmap
03It reaches the report by itself
Trading reports are still assembled by hand each week — so sell-through questions take days, long after the season has moved on.
Our approach
We help automate the reports you already produce and stand up self-serve margin and stock views — refreshed on demand, while there’s still stock to move.
Who feels it
TradingSales, margin, and stock visible on demand, not once a week.
MerchandisersThe export day disappears; the work becomes analysis.
BuyersSell-through monitored live, same definitions everywhere.
The payoff — Answers to “what’s working?” arrive in minutes instead of days — while there’s still stock and season left to act on.
Step 04 · Data as a Product
04Everyone agrees what it means
“Gross margin” is calculated one way by finance and another by trading — and “active customer” means something different to loyalty and e-commerce.
Our approach
We help the business settle on one definition per number and publish each KPI with an owner and a source — findable by anyone, from the store to the boardroom.
Who feels it
New hiresRamp on the numbers in days, not months.
AnalystsFewer “can you pull this for me” requests.
LeadershipOne agreed definition per number, across trading and finance.
The payoff — Self-service that’s actually self-serve — the KPI library becomes the shared language of the whole business.
Step 05 · AI & Automation Governance
05It stays safe around AI
Teams are already pasting customer and order data into AI tools for personalization — customer PII and payment data need guardrails before that spreads, and payment standards will ask.
Our approach
We help classify data by sensitivity and set up a governed AI environment — a recommendation model can read purchase history while payment details stay ring-fenced, with every use logged.
Who feels it
ExecutiveAI adoption with a defensible privacy and payment story.
ComplianceCustomer and payment data ring-fenced from AI tooling.
EveryoneA clear answer to “can I use AI for this?”
The payoff — AI gets adopted broadly and safely — on governed data, with a trail that stands up to a payment or privacy review.
Step 06 · Data Culture & Engagement
06The floor shapes what’s built next
Store managers and merchandisers know exactly what’s broken about the data they work with — but workarounds and back-room spreadsheets multiply instead of fixes.
Our approach
We help put a visible request channel in place, from idea to shipped — so the people closest to the sale keep shaping how the data is captured and used.
Who feels it
StoresTheir input visibly shapes the tools — so they keep giving it.
LeadershipPain points surface early, with context, instead of festering.
The orgAdoption compounds — each shipped request builds trust in the next.
The payoff — The business keeps getting more data-driven after the engagement ends — improvement becomes routine, and visible.
Step 07 · Centralized Knowledge Base
07It becomes know-how
Supplier terms, seasonal precedent, and how to range a category live in a few senior buyers’ heads — and walk out the door with each departure.
Our approach
We help stand up a central knowledge base with AI-assisted capture — so a retiring buyer’s ranging precedents inform the next buy, not a departure loss.
Who feels it
BuyersRanging rules and supplier terms at hand for every buy.
New staffOnboarding from a knowledge base, not from shadowing alone.
AI toolsThe context they need to be accurate about your business.
The payoff — Onboarding and AI both get faster and more accurate — merchandising judgment stops walking out the door.
The same number, everywhereRegister to boardroom
Margin & inventory dashboardsKPI portalGoverned AIKnowledge base
The order rung up this morning is the margin figure on the trading report — on agreed definitions, on one stock truth. One capture, one truth — trading visible while there’s still stock and season to act on.
Benefits realized
What changes when the strategy lands.
The shifts below are what the seven focus areas add up to in a retail business — register to boardroom, sale to season plan.
BeforeThe same item counted in three systems
AfterOne stock master, one on-hand figure
BeforeTrading reports assembled by hand each week
AfterAutomated flow, refreshed on demand
BeforeSell-through answered in days
AfterSell-through by store in minutes
Before“Ask the analyst” for every report
AfterSelf-serve KPI portal with definitions
BeforeAI experiments near customer and payment data
AfterGoverned AI with a compliance-ready trail
BeforeMerchandising rules in a few senior heads
AfterA searchable, living knowledge base
How we engage
Strategy and execution, delivered together.
You don't get a slide deck and a goodbye. Every focus area starts with discovery and is implemented as requirements firm up — a pilot store runs the new stock model live.
Discovery & Assessment
We learn how your business actually runs — from the SKU setup screen to the trading report on the board table.
Interviews across trading, stores, e-commerce, operations, and finance Inventory of POS, e-commerce, warehouse, loyalty, and ERP systems Gap map: where product, order, and customer data break down Audit of existing reports and the manual effort behind them
Mostly conversations — no disruption to trading. You end the phase with a map of every place the same item or customer is re-keyed or reconciled, and what that costs.
Analysis & Framework Design
We design the central platform and start building — capture, connections, and guardrails, as requirements firm up.
Central platform and customer/vehicle data model on Microsoft Fabric / Azure / Databricks Capture-at-entry design: guided SKU and order setup with built-in checks Integrations ranked by ROI — product and stock master to POS and e-commerce first Sensitivity tiers and AI guardrails for customer PII and payment data
The first automated reports replace manual ones mid-phase. A pilot store runs the new stock model live, and their feedback shapes the design.
Roadmap & Validation
You leave with a phased roadmap and a substantially implemented platform — a plan and working capability, together.
Phased roadmap covering every data initiative Priority dashboards and system connections live KPI portal and knowledge base stood up Wish-list platform open to stores and merchandising
The trading report already assembles itself. There’s one place to find every number — and a prioritized plan for what comes next.
Phases can be resequenced to fit your priorities. Engagement model and investment are tailored per organization — book a meeting and we'll scope it to you.
Start here
Bring us your channels and your stock.
We'll show you how your data should flow, identify the biggest opportunities for improvement, and define a phased engagement that fits your business.
30 minutes No obligation Microsoft Fabric partners