Governance

Data Quality

What it is, why it matters for businesses, and key questions to ask.

What it is

Data quality means data is accurate, complete, consistent, and fit for purpose. For AI, it also means data is representative, free of bias where possible, and properly structured for the model.

Why it matters for businesses

Garbage in, garbage out. AI models learn from the data you feed them. Poor data leads to poor outputs: hallucinations, wrong answers, or biased decisions. Cleaning and curating data before AI is often the highest-impact step you can take.

Example workframe

Best practice

Profile data before AI: accuracy, completeness, consistency
Remove or anonymise sensitive data where it's not needed for the use case
Standardise formats: dates, units, identifiers so the model can learn
Sample for representativeness: does your data reflect real-world diversity?
Establish a baseline: measure quality before and after AI changes

Areas to explore

Source systems: where does the data come from and how reliable is it?
Gaps and duplicates: what's missing or duplicated that could skew results?
Temporal drift: does older data still reflect current reality?
Bias in training data: could historical patterns perpetuate unfair outcomes?
Label quality: if using labelled data, how accurate are the labels?

Suggestions

Run a data quality audit before any AI project
Define data quality metrics and track them over time
Invest in data cleaning before scaling AI—it's often the highest ROI step

Key questions to ask

Is our data accurate and up to date?
Are there gaps or duplicates that could skew results?
Does our data represent the real-world scenarios we care about?
Have we removed or anonymised sensitive data where appropriate?
Do we have a process to monitor and improve data quality over time?