A mid-sized data consultancy headquartered in the UK, specialising in data migration and transformation for financial-services clients — retail and commercial banks, insurers and asset managers. Around 120 staff across delivery, architecture and advisory, running a rolling portfolio of 15 to 20 migration and integration engagements a year, much of it repeat business from a core group of long-standing clients.

The firm’s reputation rested on deep, hard-won knowledge of its clients’ data — the mappings, business rules and design decisions accumulated over years of engagements. But that knowledge lived in people’s heads, and when experienced consultants moved on, it left with them. Every new project effectively started from a blank page: senior consultants spent the first weeks rediscovering mappings and re-deriving rules that they, or a departed colleague, had already worked out before. Meanwhile clients pushed for lower day rates even as data estates grew more complex, and the cost of hiring experienced data consultants kept climbing. Leadership recognised that the firm’s core advantage was quietly eroding, because it was never captured anywhere durable.

The firm already documented its work diligently — in spreadsheets, Visio diagrams, email threads and consultants’ own notebooks. The problem was that documentation captured the what but rarely the why. A new consultant could read a mapping sheet without understanding the reasoning behind a decision, so they re-litigated it from scratch to be safe. Documentation also went stale the moment a source system changed, and no amount of process discipline made static artefacts reusable across engagements. Knowledge management was treated as an overhead to be minimised under fee pressure, not as an asset to be compounded.

The consultancy adopted DataSync as the standard way it analyses and documents every migration. On each engagement, DataSync’s agents profile the client’s source systems, generate field-level mappings to the target model, and document the business rules, transformation logic and — critically — the rationale behind each decision. Data-quality issues are surfaced early, and the output is a validated specification the delivery team builds from. Because the knowledge is captured in DataSync rather than in individuals, every project now enriches a reusable, searchable knowledge base. When a returning client’s systems change, DataSync updates the affected mappings and documentation instead of the team rebuilding them, so consultants shifted from rediscovering data to advising on it.

• Delivery timelines on comparable engagements fell by roughly half — a migration that previously ran to around nine months now completes in four to five.
• Consulting effort per engagement dropped by an estimated third to 40%, as the analysis-and-documentation phase compressed from weeks to days.
• Project margins improved, giving the firm room to stay price-competitive without discounting its expertise.
• New consultants reached productive contribution far sooner, onboarding against a living knowledge base instead of tribal knowledge.
• Delivery became more consistent across clients, reducing key-person risk on every engagement.

DataSync is an AI platform for data migration and integration projects. Its agents analyse source systems, generate and maintain field-level mappings and transformation logic, surface data-quality issues, and produce the validated specification, governance and lineage documentation that engineering teams build from — turning work that once took months of manual analysis into weeks.

DataSync is a product of Blackstone DataSync Ltd, registered in England & Wales. To see how it could accelerate your next data project, visit datasync-ai.com or contact the team for a demonstration.