One reliable version of your business data, wherever it’s used.

When the same products, customers, suppliers or prices live in several systems, each with its own version, teams can work from conflicting information. We plan the data model with you, agree which system should own each kind of record, then clean, match and consolidate the data into it and connect that source to your website, search and other tools. AI can assist with matching and tidying; people make the decisions and sign off the result.

Discuss your data clean-up
Illustrative

What this can include

  • Data audit and ownership

    Where each kind of record lives today, how the versions differ, and which system should own it from now on.

  • Data model and rules

    Agreed fields, categories, units and naming, with clear rules for resolving conflicts between systems.

  • AI-assisted matching and de-duplication

    Records that describe the same thing under different names, codes or formats are matched, and uncertain matches go to a person.

  • Clean-up and enrichment

    Consistent attributes, descriptions and categories, drafted where AI helps and approved by the people who know the data.

  • Consolidation and connected channels

    Approved records move into the owning system, which then feeds your website, search, marketplaces and internal tools, so changes are made once.

Illustrative workflow

From two conflicting systems to one source of truth

  1. Two systems, such as a business system and an online store, each hold their own version of the same records.

  2. Together we agree the data model, which system owns each kind of record and how conflicts are resolved.

  3. AI proposes matches, merges and clean-ups; anything uncertain goes to a person.

  4. Approved records are consolidated into the owning system, with a reconciliation report.

  5. From then on, that system feeds the store, site search and other tools.

What you get

  • A data audit and an agreed map of which system owns what
  • A documented data model with naming, category and conflict rules
  • Cleaned, de-duplicated and consolidated records, with a reconciliation report
  • Connections that keep your website, search and other tools in step with the owning system
  • Validation rules and guidance so the data stays organised

Integration and data considerations

  • Data volume, history and quality, and how much of it needs a person to decide.
  • Which system should own each kind of record, and what changes for the people who edit it.
  • Cut-over timing, backups and a rollback plan suited to the systems and changes in scope.

How people stay in control

  • The data model, ownership and merge rules are agreed with your team before anything moves.

  • Uncertain matches go to a person; nothing is merged silently.

  • Changes are logged, with a backup and rollback plan agreed for the systems in scope. What can be restored, and how, is established before changes are applied.

Questions about data consolidation and clean-up

Is this an AI project?

Partly. AI can assist with matching, tidying and enrichment, but much of the work is in the planning: agreeing the data model, which system owns what and how conflicts are resolved. People make those decisions and sign off the result.

Do we need to replace one of our systems?

Usually not. The right answer is often to choose which existing system owns each kind of record and connect the others to it.

What happens to our existing data?

We agree a backup, reconciliation and rollback plan before applying changes. Merges and corrections are logged for traceability; the plan defines which changes can be restored and how, rather than assuming a log makes every change reversible.

Discuss your data clean-up.

Describe the process, the systems involved and where the work gets stuck. You don’t need a finished technical brief.

Discuss your data clean-up