Harbour Financial
Financial Services · 340 employees · target under evaluation
410,000
Records analysed
4
Connected systems
9
AI blockers found
Capital at risk if acquired as-is: €4,079,204/yr - the data exposure the buyer inherits on day one, before the value-creation plan even starts.
The finding that lands
Counts computed from the data9,355 customers exist as two different companies across Microsoft Dynamics and Oracle.
€11,811,448 of revenue can't be reconciled
The same account is recorded under different names, IDs or addresses in each system. Each one is clean in isolation, so every single-system audit passes them.
Broken for AI: Any AI keyed on customer identity (pricing, churn, lifetime value, next-best-action) trains on a contradiction and ships confident, wrong answers.
16,683 of 73,800 customers in Microsoft Dynamics have no matching record in Oracle.
€11,811,448 of revenue sits in a cross-system blind spot
Forecasting and revenue AI can only see half the picture, so projections are built on a partial book.
3,690 internal employees from ADP are sitting in the Microsoft Dynamics contact list.
3,690 records mislabelled as customers
Segmentation, scoring and outreach AI treat employees as prospects, polluting every model trained on the list.
Material data risk - budget €97K-€178K and 9-10 months before AI is on the table.
Harbour Financial's data foundation cannot support AI without significant remediation. Price and structure the deal accordingly.
How good is the data?
41/ 100 · C
Are they AI-ready?
Not yet
Requires remediation first.
How long & how much?
9-10 months
€97K-€178K remediation
Risks the buyer inherits
CRM, ERP and finance systems don't reconcile to a single customer - expect post-close integration, reporting and synergy capture to slip while identities are resolved.
No traceable data lineage - AI and analytics outputs won't be auditable or defensible to the IC, regulators or an eventual exit buyer.
Formats and definitions diverge across systems - blocks a unified view and clean post-close consolidation.
Key fields are missing on a material share of records - models and segmentation will be blind to parts of the book.
High duplicate/error rates - every downstream model, forecast and report inherits and amplifies them.
Weak ownership, stale records and thin audit trails - a material compliance and accountability gap the buyer assumes on day one.
Conditions to proceed
- De-duplicate the client master - 18% duplicate rate on key fields makes any risk model unreliable.
- Bring Connectivity (currently 30) to an acceptable threshold before relying on AI in this area.
- Bring Provenance & Origin Coverage (currently 32) to an acceptable threshold before relying on AI in this area.
Deal & price consideration
Factor the remediation investment (€97K-€178K) and 9-10 months to AI-ready into your model and 100-day plan. Cross-system reconciliation is the critical path on post-close integration. This reflects required remediation and timeline - not a blanket valuation discount.
Evaluating a real target?
Run this exact diligence on your acquisition pipeline - before you commit capital.