Data & decisions

Trust is built into the data.

Reliable reporting starts before the dashboard. Nemely helps you find consequential data issues, agree who owns them and introduce quality checks that your teams can keep using.

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Power QueryMicrosoft data platformsData ownership

A few organizations we've worked with

Make quality visible—and make it actionable.

Focus on the fields that affect a decision, a customer interaction or an important process. A practical rule needs a defined check and someone who can respond when it fails.

From finding to response

Which error is worth fixing first?

An empty reference can prevent a record from joining correctly. An inconsistent status can change a management measure. We prioritise issues by their business effect and agree a rule, a threshold and a response with the team that maintains the source.

Name the field, its purpose and the condition it must meet.
Assign an owner with a way to correct or escalate the issue.
Track recurring failures and address how they are introduced.

See how quality supports customer identity

Inside Power Query

Look at the values before trusting the total.

Power Query profiling reveals column quality, value distributions and statistics. These views help a data specialist locate missing values, errors or unexpected patterns before they reach a report. They are a starting point for investigation, not a complete assessment of business correctness.

Microsoft documentation example of Power Query profiling tools. This view highlights sample column statistics; it does not certify the accuracy or completeness of a business data set.

Source: Microsoft Learn

Ownership that works

Give the definition a place to live.

People need to know where a measure comes from, who approves a change and who can use the information. We help you establish a concise glossary, a review routine and proportionate access practices around the data your business depends on.

Connect governance to the platform

How Nemely helps

Improve the data that matters first.

We work with a bounded data domain and a specific downstream problem. The result is an agreed set of checks and responsibilities that can be put into operation.

Data & Solution Audit System Integration Training and Workshops

Identify consequential fields

Trace a disputed report or broken process back to its source fields and agree what acceptable data looks like.

Profile and prioritise

Inspect representative records and the full population where appropriate. Separate formatting issues from identity, completeness and definition problems.

Put correction into practice

Agree owners and response steps, implement the selected checks and review whether repeated issues are reducing over time.

Planning notes

Making governance practical

Quality monitoring after a cleanup

A cleanup addresses today’s records. A sustainable approach also checks the next import or update, identifies repeated failures and gives the source owner a route to resolve them. We design those checks around the processing cycle and the team’s capacity to respond.

Access and accountability

Document who owns the data, who maintains the solution and which audiences need access. Review these responsibilities when processes or personnel change. The technical controls depend on the specific Microsoft services used.

FAQs

Your questions, answered.

Can we start without a dedicated data governance team?

Yes. Begin with a small domain, a named business owner and a person responsible for the technical checks. The operating routine should fit the people available to maintain it.

Is a valid field necessarily correct?

No. A date can have the right format and still be wrong for the transaction. Technical profiling needs to be combined with rules that reflect the field’s meaning and the business process.

Will governance slow down our reporting team?

Unclear definitions and unresolved source errors already consume time. We keep the first set of rules focused on consequential issues and review whether the process helps people resolve them more quickly.

What affects the cost and timing of a data-quality review?

The data domains, source access, volume of exceptions and number of owners involved shape the scope. We agree a priority business decision and the quality rules it needs before estimating work. Correcting historical records and establishing ongoing monitoring are separate activities to plan.

How do we know which data issues to address first?

Start with errors that interrupt work, distort an important measure or lead to repeated manual corrections. Ask the people using the data to explain the consequence and identify a business owner for the rule. This gives the first improvement a clearer purpose than cleaning every field at once.

Explore the Microsoft capabilities

Build for today. Keep a view of what comes next.

Microsoft is moving Dynamics 365, Power Platform and Dataverse updates to the AI at Work roadmap from September 2026. We use the current feature status when planning your solution, and evaluate previews separately before proposing them for production.

Explore Microsoft’s roadmap approach

Capabilities reviewed 8 September 2026. Licensing, configuration, language and regional availability vary by feature.