AI how-to guides Technology and data
How to improve data quality with AI
Profile your critical datasets, turn freshness, completeness and validity rules into automated checks with named owners, and use AI to propose rules, spot anomalies, classify sensitive columns and suggest fixes. Lineage shows where each problem comes from and what it affects. People decide the rules, own the data and approve corrections.
What AI does
- Profiles datasets and proposes quality rules from the patterns it finds.
- Detects anomalies in volume, distribution and schema that fixed rules would miss.
- Finds and classifies personal and sensitive data across tables and pipelines.
- Matches duplicate records and suggests standardised values for names, addresses and codes.
- Traces lineage so each failing check points to its source and to the reports and models it affects.
What stays with people
- Defining what good data means for each use: the rules, thresholds and service levels.
- Owning each dataset and fixing problems at the source system, not only downstream.
- Approving merges, corrections and masking policies that change data.
- Deciding which data may be used to train or feed AI, under consent and regulation.
How to put it in place
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Pick the data that matters
Start from the reports, models and processes where bad data hurts most. List the datasets behind them, who owns each one and what the consumers expect in freshness, completeness and accuracy.
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Profile and find the gaps
Run automated profiling to see nulls, duplicates, outliers and format problems, and classify sensitive columns. Share the results with owners so the baseline is agreed, not imposed.
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Write quality checks as code
Turn the agreed expectations into checks that run in the pipelines, versioned like any other code. Let AI propose rules from the profiles, and have owners accept or adjust them.
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Capture lineage at column level
Collect lineage from the pipelines themselves, using an open standard where possible, so you can follow each column from source to dashboard or model. When a check fails you then know the cause and who is affected.
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Alert owners and fix at the source
Route failures to the dataset owner with context and a suggested fix, and stop bad data from reaching consumers when the impact is high. Track recurring issues back to the system or process that creates them.
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Extend by domain and report
Add domains one at a time and publish quality status where analysts work. Produce lineage, quality and consent reports for audit from the same metadata.
What you need
- Access to the warehouse, lake and pipelines where the data lives.
- Named data owners and stewards for the critical datasets.
- A business glossary or at least agreed definitions for key fields.
- A place to run checks inside the pipelines and an alerting channel owners actually read.
- Data protection input on personal data, consent and retention.
When it is not worth it
- Data nobody uses for decisions: remove or archive it instead.
- When no one will own the datasets: checks without owners just generate alerts nobody reads.
- One-off migrations, where a manual cleanup is cheaper than an operating layer.
Risks and how to control them
- Alert fatigue: too many checks and noisy anomaly alerts; start with the critical datasets and tune thresholds with owners.
- Automated fixes that corrupt data: keep suggested corrections under approval and log every change.
- Masking in some pipelines and not others: apply personal data policies at ingest and at serving, and verify them with lineage.
- Fixing symptoms downstream: track recurring issues to the source system and its owner.
- AI built on unsuitable data: for high-risk AI systems the AI Act sets data governance requirements for training, validation and test data; involve your legal team.
How Thinkia does it
We start by inventorying critical data assets, lineage gaps, personal data exposure and regulator requirements. Quality service levels, lineage scope, the consent model and the ownership map are then defined with your data, AI and risk teams.
We instrument the pipelines, populate the catalogue, enable policy enforcement and deliver a first audit-ready report, as a layer that plugs into your existing warehouse, lake and BI tools. Coverage then expands domain by domain, handed over to your data governance team.
The Thinkia solution behind this guide
- Data quality, lineage & governanceGovernance AI actually needs
- Data warehouse modernizationModern warehouse, modern velocity
Thinkia products involved
Frequently asked questions
How can AI improve data quality?
By profiling data and proposing rules, detecting anomalies that fixed rules miss, matching duplicates, classifying sensitive fields and suggesting corrections. The rules, ownership and approval of changes stay with people.
What are the dimensions of data quality?
The usual ones are accuracy, completeness, consistency, timeliness or freshness, validity and uniqueness. Which ones matter, and how much, depends on how the data is used.
What is data lineage and why does it matter?
Lineage records where data comes from, how it is transformed and where it goes. It lets you find the cause of a quality problem, see which reports and models it affects and answer audit questions.
Should we fix the data before starting with AI?
Not all of it. Fix the data the first use case depends on and put checks in place so it stays fixed. Waiting for all data to be clean usually means never starting.
Does this help with the EU AI Act?
It helps produce evidence on data origin, quality and personal data handling, which the regulation asks for in high-risk systems. Whether those obligations apply depends on the system; check with your legal team.
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- Fix the data first or start with AI now: which comes first?
- Synthetic data vs real data: what should you train, tune and test your AI on?