Without a clean data foundation, integration stalls. But trying to consolidate everything at once is a trap.
After a merger in the glass, ceramics, or materials space, integrating operational data can feel overwhelming. You’re looking at different ERP systems, mismatched SKUs, conflicting vendor codes, and inconsistent financial reporting structures. Yet the longer you delay, the more confusion you create—for sales, operations, finance, and leadership.
The key is to start small, move strategically, and align data priorities with business outcomes.
Here’s how to begin data consolidation post-merger—without grinding your team to a halt.
1. Start With a Data Triage: What’s Business-Critical?
Don’t attempt a full-blown data warehouse build on Day One. Identify the top 3–5 datasets that are essential to running the combined business:
Customer master list
Product catalog / SKU hierarchy
Open orders and backlog
Vendor master file
Chart of accounts
These are your “Tier 1” assets—consolidate them first to enable quoting, billing, reporting, and supply chain continuity.
2. Use Business Processes to Prioritize Systems
Let the business lead the data—not the IT roadmap.
Ask:
What systems are needed to close the month?
Which ones power daily order fulfillment?
What tools do field reps rely on for quoting?
This reveals which ERP modules or warehouse systems must be aligned early—and which can wait.
3. Appoint Data Owners by Function, Not Just by System
Successful consolidation happens when:
Finance owns financial data
Sales owns CRM and account alignment
Ops owns SKU and inventory hierarchies
Avoid letting IT “own” data quality in isolation. Functional teams must be accountable for inputs, structure, and reconciliation logic.
4. Align on Definitions Before Merging Anything
What one company calls “Standard Clear Tempered IGU 6mm” might be “T6 CLR IGU STD” in another system.
Before any merge:
Agree on naming conventions
Normalize UOM (units of measure)
Establish attribute-level standards (e.g., tint, coatings, fabrication processes)
Otherwise, you’ll import garbage—and create new friction.
5. Set a 100-Day Data Governance Framework
This includes:
A shared data dictionary
A master data request process
A cadence for exception reviews and clean-up sprints
Your goal isn’t perfection—it’s functional clarity. Clean enough to sell, ship, and report. Refine as you scale.
Data consolidation isn’t a tech project—it’s a business transformation enabler. Start with what matters. Fix what’s broken. And always let operations drive the roadmap.
When the data reflects the business, the integration accelerates.
