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AI & AnalyticsData Foundation

USA · Industrial Equipment

BlastOne International

Two geographies. Two CRMs. Zero shared view of the business.

Fragmented sales and operations data across two geographies consolidated into unified dashboards - giving leadership a single operational view across North America and Australia for the first time.

Key Results

−40%

Reporting effort

Manual hours per cycle

2 regions

Unified view

US East + West, one dashboard

Live

Margin by product

Was days of manual assembly

Monday AM

WBR auto-delivered

Was manually built, always late

Tech Stack

Microsoft FabricAzure Data FactoryPower BIPower AutomateDelta LakeMaster Data Management

The Situation

BlastOne International distributes and services industrial blasting and coating equipment across North America and Australia. Two regions, two teams, two operational rhythms - and until recently, two completely separate data realities that never talked to each other. Regional managers knew their own numbers. The executive team knew neither in real time. Pulling a consolidated view of pipeline, bookings, revenue, and margin required manual data extraction from both CRM instances, a reconciliation exercise to handle duplicate customers and overlapping products, and a final spreadsheet that was already a week old by the time it was presented. And the margin picture - critical for a business running complex, multi-part service engagements - was the number that took longest and was trusted least.

For multi-geography distribution and industrial services businesses, this pattern is very common:

  • Your two regions run separate systems and leadership has no real-time consolidated view across both

  • Pipeline and revenue reporting requires someone to manually extract from two CRMs and reconcile

  • The same customer appears twice - once per regional system - with different contact records and order histories

  • Margin by product or service line is a calculation that takes days, not a number you can read on a dashboard

  • Weekly business reviews use data that's already a week old before the meeting starts

  • Finance and commercial leadership are working from different revenue figures - and neither is confident theirs is right

If three or more of these describe your operation, you're looking at the right case study.

The Root Problem

  • 1

    North America and Australia operated on separate CRM instances with no consolidated cross-regional data layer

  • 2

    Sales pipeline, bookings, and revenue could only be viewed by region - no unified executive view existed

  • 3

    Duplicate customer records across regions created reconciliation problems in any attempt to aggregate data

  • 4

    Margin by product line and service type required multi-day manual assembly - it was never a live number

  • 5

    Weekly business reviews relied on data that was 5–7 days stale before the meeting began

How We Fixed It

01

Unify the customer and product master data first

Before building any reporting layer, we resolved the entity duplication problem. We built a master data reconciliation process that identified duplicate customers across both regional CRMs, applied deterministic matching logic (company name, domain, address), and created a single unified customer ID that both regional systems could reference. This was the foundation - without it, any aggregated report would double-count.

You cannot have a unified sales view without a unified customer master. We fixed that first.
02

Build a multi-geography integration layer on Microsoft Fabric

CDC-enabled Azure Data Factory pipelines pull deal, booking, and revenue data from both regional CRM instances on a defined schedule into a Microsoft Fabric Lakehouse. Customer and product dimensions are joined using the unified master data. The result is a single, clean dataset covering both geographies - updated automatically, no manual extraction required.

03

Executive and regional Power BI dashboards

Delivered two dashboard surfaces: an executive view showing consolidated pipeline, bookings, and margin across both regions, and regional views for each geography. Same underlying data, same definitions - different lens. The executive team now opens a dashboard, not a spreadsheet, to see how the business is performing globally.

04

Automated weekly business review pack

The manual weekly report assembly was replaced by a Power BI subscription delivering a formatted performance summary to leadership every Monday morning - pulling live data automatically. The 5–7 day data lag became a same-day number. The meeting now discusses what to do, not what happened last week.

The meeting now discusses what to do - not what happened last week.

Measured Outcomes

MetricBeforeAfter

Cross-regional visibility

None - manual extraction per division

Unified executive dashboard, real-time

↑ Key win

Manual reporting effort

Multi-day assembly per cycle

−40% across finance and operations

↑ Key win

Margin by product line

Unavailable without days of work

Live Power BI view

Customer master data

Duplicate records across divisions

Unified - single customer ID

Weekly business review

Manually assembled, 5–7 days stale

Auto-delivered Monday, live data

What This Means For You

What this means for multi-geography industrial and distribution businesses

The two-region visibility problem is almost always a master data problem first and an integration problem second. Until you have a single definition of "customer" and "product" that both regions agree on, any aggregated reporting is fundamentally untrustworthy. Once the master data is clean, the integration layer is straightforward - and the reporting layer on top of it is fast to deliver. If your executive team is still receiving manually assembled regional packs at the start of every week, the underlying problem is solvable in a single project quarter.

Next Step

Is this your situation?

Book a 30-minute call. No slides, no pitch. We'll look at your specific setup, tell you what's causing the problem, and what a realistic fix looks like - including timeline and cost range.