Data · 6 min

Data Quality Framework

Data quality is settled in this order: agree definitions, assign ownership, validate at entry, then monitor. Tooling applied before the first two encodes the disagreement rather than resolving it.

Data engineers reviewing automated validation test results and data pipeline completeness metrics — Data quality frameworks enforce schema validation, deduplication, and automated anomaly alerting.

How it works

Definitions first, because most data quality disputes are definition disputes wearing a technical costume.
What counts as an active customer, when an order is complete, which date is used for reporting. Until those are agreed, two systems will disagree and both will be correct by their own rules.
Then ownership.
Each field or record type gets someone accountable for its accuracy: a business owner, not a database administrator. Data without an owner degrades because nobody is responsible for the decisions that keep it consistent.
Then validation at the point of entry, which is where quality is cheapest to enforce.
A required format, a constrained list, a duplicate check at creation. Cleaning bad data later costs many times more than preventing it, and it recurs.
Then monitoring: completeness, duplication rates, and values outside expected ranges, checked on a schedule with someone notified.
Data degrades continuously as sources change, and undetected degradation is what turns a dashboard into something nobody trusts.
Resist starting with a cleansing project.
A one-off clean of data that keeps being created badly buys a few months of accuracy and teaches the organisation that quality is a project rather than a practice.

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Frequently Asked Questions

Where Should We Start With Data Quality?

With definitions, and it is a business conversation rather than a technical one. Most disputes about numbers turn out to be two teams applying different, individually reasonable rules.

Is A Data Cleansing Project Worth It?

Only alongside fixing how the data is created. Cleaning without validation at entry buys a few months and teaches the organisation that quality is a project rather than a practice.

What Does This Cover?

Data quality is settled in this order: agree definitions, assign ownership, validate at entry, then monitor. Tooling applied before the first two encodes the disagreement rather than resolving it.

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Data Quality Framework

Definitions first, because most data quality disputes are definition disputes wearing a technical costume.

Then ownership.

Then validation at the point of entry, which is where quality is cheapest to enforce.

Then monitoring: completeness, duplication rates, and values outside expected ranges, checked on a schedule with someone notified.

Resist starting with a cleansing project.

Frequently Asked Questions

Where this applies

Related Services

Read next

Apply this to your own situation

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Data Quality Framework

Definitions first, because most data quality disputes are definition disputes wearing a technical costume.

Then ownership.

Then validation at the point of entry, which is where quality is cheapest to enforce.

Then monitoring: completeness, duplication rates, and values outside expected ranges, checked on a schedule with someone notified.

Resist starting with a cleansing project.

Frequently Asked Questions

Where this applies

Related Services

Read next

Apply this to your own situation