The Due Diligence Gap: What Your Deal Model Doesn’t Say About the Target’s Data
Your financial due diligence is flawless. Your legal due diligence is complete. But what does your deal model say about the target’s data? In most modern M&A transactions, technology systems are reviewed only at a high level: what platforms are in use, what the licenses…
The Semantic Trap: Why Clean Data Still Breaks Daily Operations
Your data migrated cleanly. It passed every programmatic check and validation test. And yet, the moment your business teams log in, everything is wrong. Why does clean data break daily operations? Because post-acquisition migration failures rarely stem from raw data corruption. They stem from unresolved…
Most Post-Acquisition Data Migrations Don’t Fail on Day 1. They Fail on Day 101.
Most post-acquisition data migrations don’t fail on day one. They fail on day 101. By then, the deal has closed, the press release is old news, and the hard part starts: making two companies’ systems actually work as one. Gartner has found that 83 percent…
The Five Questions That Actually Reveal a Data Migration Partner’s Capabilities
When picking a data migration partner for an M&A or joint-venture integration, most deal teams ask the wrong question. They ask which firm has the best credentials or the lowest rate card. The question that actually matters is whether that firm possesses the specific operational,…
What “On Track” Actually Looks Like: Realistic Timelines for Post-Acquisition Data Migration
Plenty of enterprise data migrations get declared a success at go-live, only to be quietly admitted as failures six months later. The cutover banner goes up, the integration team disbands, and everyone celebrates. Then reality sets in. Financial reports fail to reconcile, business units lose…
The Inheritance Gap: What You Actually Take On When You Close an Acquisition
When you are evaluating an acquisition, initial discovery reveals systems and licenses: which ERP, which CRM, how many seats. What it doesn’t show you is what’s actually inside the data underlying those systems: years of workarounds, customizations, and decisions baked in by people who left…