Your data gets cleaned — and then it gets dirty again

A data project without governance is a temporary fix. Quality improves, then degrades — because the conditions that created the problem in the first place haven't changed. Does any of this sound familiar?

You've invested in a data cleansing or cataloging project, but within a year the same problems are creeping back.

New data enters your ERP or CMMS through multiple routes, and most of it is never validated before entry.

Suppliers deliver data in whatever format they choose, and no one is enforcing a standard.

Your team spends time correcting data problems in the system instead of using the system to manage operations.

There's no clear ownership of data quality in your organisation — everyone is responsible, so no one actually is.

Data governance is what makes good data last

A data cleansing or cataloging project gets your data to the right state. Data governance keeps it there. Governance means defining who owns data quality, what the rules are, how incoming data is validated, and what suppliers are required to deliver. Without it, data quality is a constant battle. With it, quality is maintained as a normal part of operations — not a recurring emergency.

From experience: organisations without governance structures reclean their data every three to five years — at the same cost, with the same disruption, and the same result.

WHATS INCLUDED

The service includes:

Data ownership model — clear roles, responsibilities, and decision rights for data quality

Supplier onboarding validation — how incoming data is checked before entry into the system

Data quality policies — rules defining what 'correct' data looks like in your ERP or CMMS

Data entry standards and training for internal teams

Data quality KPIs and monitoring framework — how quality is measured, tracked, and reported

Integration with ERP, CMMS, and procurement workflows

Supplier data requirements specification — what suppliers must deliver and in what format

Periodic governance audit and reporting

Our 5-step governance process

1

Current state review

Our governance process is designed to produce a framework your team can actually operate — not a policy document that sits in a folder.

2

Governance design

We define the ownership model, quality rules, and supplier requirements suited to your operations — practical, workable, and aligned to your existing systems and workflows.

3

Policy documentation

We produce the governance framework documentation your teams can actually work from — clear, specific, and written for the people who will use it.

4

Implementation support

We support your team in rolling out the governance framework across relevant workflows and supplier relationships — including training where needed.

5

Review and optimisation

We conduct a governance review after a defined period to assess effectiveness, identify gaps, and refine the framework where needed.

WHAT YOU GET

Data quality that's maintained as a standard — not recovered as an emergency

Quality that lasts

Governance structures that prevent degradation — so your data stays at the standard you paid to achieve.

Clear ownership

Everyone knows who is responsible for data quality, what the rules are, and what to do when something is wrong.

Supplier accountability

Data requirements that are defined, communicated, and enforced — so incoming data meets your standard before it enters your system.

A measurable quality baseline

Standards-aligned data produces better results from cleansing, enrichment, migration, and AI — because everything is built on a consistent structural base.

FAQ