AI adoption & integration

Data governance

When AI adoption stalls, the problem is usually not the model but the data: where it lives, who uses it, and whether it can be trusted. Sort out those three first.

Who it’s for

The same data has different versions in different departments, and the numbers don’t match
You want to adopt AI or BI reporting but aren’t sure your data is usable
You’re not sure what personal data the company holds, where it is, or who can see it
You’re planning to connect legacy systems and want your data in order first

Engagement cycle 3 months, 6 months or a year
Pricing Estimated per engagement

Email us

Scattered data

ERP, Excel, department systems

Catalog and standards

Location, owner, field definitions

Quality and access

Regular checks, personal data classes

Data you can trust

For reports and for AI

Know where your data is, who owns it and whether it can be trusted, and AI and reporting have a reliable foundation.

What we do

Data inventory

We list what data the company has, which system it’s in, who is responsible for it and who uses it.

Standardization

We unify field definitions, codes and naming so the same record matches across systems.

Quality monitoring

We set quality rules (completeness, accuracy, timeliness) and a routine for checking them.

Access and personal data

We set role-based access, flag personal and sensitive data, and check it against the requirements of Taiwan’s Personal Data Protection Act.

How it works

1. Inventory

Interviews and a review of your systems, resulting in a data catalog.

2. Set the rules

Define standards, quality rules and access principles.

3. Implement

Put them into your systems and processes, and fix the most critical data problems.

4. Monitor and hand over

Set up regular checks and hand documentation to the staff responsible.

Deliverables

Data catalog (location, owner, users)
Data standards and naming rules
Quality rules and how they are checked
Access and personal data classification
Governance documentation and handover notes

Engagement cycle

Each cycle runs three months, six months or a year, depending on scope. At the end of each cycle we sit down with you and compare the results against the goals set at the start, then decide what the next cycle should cover, or whether to stop there.

Pricing

Each engagement is estimated on its own: how many systems and how much data are involved, the people and time needed, and how long the cycle runs. It comes in two steps. First we measure the baseline — before any quote, and priced separately — recording where things stand now. Then we estimate the rollout from the scope we measured, rather than quoting a price and fitting the scope to it. Write to us about where you are and we’ll explain what measuring the baseline involves.

Common questions

Do small and medium-sized businesses need data governance?

Yes, but the scope can be small. Start with the few datasets used most and wrong most often, such as customer, product and order data, rather than the whole company at once.

What does data governance have to do with adopting AI?

AI can only use data it can reach, and that data has to be correct. If data is inconsistent or permissions are unclear, the AI’s answers won’t be reliable either. Governing the data first gives adoption something to stand on.

Do we have to replace our current systems?

Usually not. Data governance is mostly about setting rules and organizing data and processes, and most of it can be done on the systems you already have.

Tell us where you are

Email us about where you are
We reply with what could work
Baseline first, then scope and estimate