Dataracity
Retail · Canada · Microsoft Power BI

From Manual Reporting to Retail Intelligence

Thirty-plus manual reports replaced by one centralized data source — sales, inventory, and customer behaviour visible on the same day the business happens.

IndustryRetail
Company11–50 employees
CountryCanada
FrameworkSTEAM
Results
30+Reports consolidatedInto one centralized source
1Central data modelSales, products, and inventory
Real timeTrend visibilityProduct and customer behaviour
Company-wideDashboard adoptionUsed to run meetings
Software used
Azure Data LakeData LandingTableauKPI and measure logicMicrosoft FabricData preparation
Related services
The engagement, move by move

Out of thirty-plus reports, into one source of truth.

Five moves, framed by the problem they answered, took the business from manual spreadsheets and instinct to dashboards the whole company runs meetings on. Follow the pipe.

Scroll to walk the build
Where it started30+ manual, fragmented reports
01
01The challenge · Fragmented reporting

The business could not see what was happening day to day.

Sales and inventory data lived across more than thirty fragmented reports, most maintained by hand, with no consolidated view of performance across locations and product lines.

30+ reports, largely maintained manuallyNo consolidated view across locations or product linesNo automated tracking of customer or product performance
Before · thirty-plus reportsFragmented
Sales by store
Stock counts
Product lists
Customer sheets
Incoming orders
…and 25 more
No consolidated view across locations or product lines

Each report maintained by hand, none of them agreeing with the next.

02
02The context · What the gap cost

Inventory decisions were made on instinct, and opportunities were missed.

Slow, manual reporting prevented timely insight, so stock decisions came down to guesswork. Leadership had no visibility into what was selling, what was slowing down, or when to reorder.

Inventory decisions based on guessworkNo view of buying patterns or product trendsOpportunities missed for want of timely insight
Before · the reorder decisionGuesswork
Reports arrive late, by hand
No view of buying patterns
Slow movers spotted too late
DecisionWhat to reorder, and when
Evidence behind itInstinct

The decision still got made — just without anything underneath it.

03
03The approach · A single structure

Sales, products, and inventory structured into one model.

A central data model brought sales, product, and inventory data together — prepared in Power Query, shaped for reuse, and replacing the thirty-plus reports each team had been maintaining alone.

Sales, products, and inventory in one modelPower Query preparation in place of manual handling30+ reports consolidated into a single source
Approach · central data modelOne source
FactSales · inventory · movement
ProductLocationCustomerCalendarSupplierStock

30+ reports consolidated into a single model everything else builds on.

05
05The approach · KPIs that decide things

KPIs written to answer the reorder question.

DAX measures highlight best sellers, slow movers, and inventory turnover — defined once in the model so every dashboard reports the same figure, and chosen to answer the questions the business actually acts on.

Best sellers and slow movers surfaced automaticallyInventory turnover measured, not estimatedEvery KPI defined once, in DAX
Approach · KPIs in DAXDefined once
Best sellersranked
Slow moversflagged
Inventory turnovermeasured
Reorder timingsignalled

Every dashboard reports the same figure, because there is only one definition.

06
06The approach · Stock, in view

Stock levels, aging inventory, and demand on one screen.

Visuals were designed around the stock question — what is moving, what is sitting, and what needs reordering now — with aging inventory separated out rather than buried in a total.

Current stock levels against demandAging inventory surfaced, not buriedReorder timing driven by the data
Approach · stock and agingSeparated out
Moving
Healthy stockTurning at the expected rate
Aging
Slowing downSitting longer than it should
Demand
Reorder nowDemand ahead of stock on hand
Stock levels, aging, and demand on one screen

Aging inventory shown on its own, rather than buried inside a stock total.

07
07The solution · Performance

Performance tuned so the reports get used.

Power BI performance tuning kept load times short as the data grew, because a report nobody waits on is a report people actually open — and these are now opened across the company.

Power BI performance tuning throughoutFast loads that scale with the dataReports opened company-wide, not requested
Solution · performanceFast and scaling
Report load timefast
Query folding in Power Queryapplied
Model tuned for growthscales
Opened across the companydaily

A report nobody waits on is a report people open.

Where it landedOne Power BI source the business runs on
The people who built it

Team responsible.

A small delivery team, named and accountable from kickoff to go-live.

  • Luke Matthews, Co-Founder, Head of Project Delivery & Data Architecture at Dataracity

    Luke Matthews

    Co-Founder, Head of Project Delivery & Data Architecture

How we build executive dashboardsReporting leadership actually opens
Still counting stock from a spreadsheet?

Reordering should be a read, not a guess.

Bring us the reports your team rebuilds every week. We'll map the model that replaces them, the KPIs worth measuring, and the dashboards your meetings can run on.

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