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Clean data starts with a business decision, not a cleanup tool. Decide who will use the data, what they need to do with it, and what level of quality that use requires. Then measure the gaps that could undermine the decision, address their causes, and tell users what limitations remain.
The phrase “Think Like a Billionaire: Think Clean Data” appears in a LinkedIn search-result snippet for David Robottom’s profile, but that result does not establish a standalone article, book, campaign, or argument by him. The practical approach here is grounded instead in the UK Government’s data-quality guidance, whose principles can also help business teams.
What does “clean data” mean for a business?
Data is fit for purpose when its quality meets the needs of the people and processes relying on it. There is no universal threshold that makes every dataset “good”: information adequate for one task may be inadequate for another. A broad customer contact list, for example, may tolerate gaps that would make the same records unusable for a process that depends on reaching every customer.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThe UK Government Data Quality Framework, published on 3 December 2020 for public-sector work, puts the principle plainly: “Data quality is more than just data cleaning.” Its broader approach includes governance, user needs, assessment across the data lifecycle, communication, anticipating change, and continuous improvement. It is guidance, not a binding standard for every business.
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Which data-quality dimensions should you measure?
DAMA UK’s six core dimensions, as presented in the Government framework, offer a useful vocabulary. Apply them to the fields and records that matter for the intended use; a score without a defined purpose is hard to interpret.
- Completeness: Are required values present?
- Uniqueness: Are records or values duplicated where they should be unique?
- Consistency: Do values agree across systems, records, or representations?
- Timeliness: Is the information current enough for the task?
- Validity: Does it conform to the required format, range, or rules?
- Accuracy: Does it correctly represent the real-world fact it is meant to capture?
These dimensions are distinct. A field can be fully populated yet contain incorrect values; completeness does not establish accuracy. Likewise, a value can be valid in format but stale, or consistent across systems but wrong in all of them.
How to improve data quality step by step
- Name the users and decision. Identify who uses the data, what process or decision it supports, and which tables or fields are essential. “Make it clean” is not a measurable goal until the use is clear.
- Define purpose-specific rules. State what acceptable completeness, uniqueness, consistency, timeliness, validity, and accuracy mean for the important fields. Document the rules and align them with user needs rather than treating them as universal standards.
- Measure a baseline. Test the data against those rules and record the scope, measurement date, method, and known caveats. The Government framework illustrates completeness with 294 emergency-contact responses among 300 students: 98% completeness for that field. This is a worked example, not a recommended target.
- Prioritize by consequence. Focus on gaps that materially affect users, operations, or decisions. A missing fax number may be low priority; a missing value that blocks a critical process may not be.
- Investigate causes and take action. Find where and how the issue is introduced, choose a corrective action, and document what remains unresolved. Where possible, address the cause at the point of collection or transformation instead of repeatedly patching symptoms downstream.
- Monitor and report. Reassess quality over time. Communicate what was measured, what changed, when the data was collected, and any relevant gaps, duplicates, inconsistencies, or cleaning decisions.
How should you set a “good enough” threshold?
Set the threshold by asking what could go wrong if a value is missing, late, duplicated, inconsistent, invalid, or inaccurate. The acceptable level depends on the intended decision and the consequences of an error. Do not assign equal effort to every field simply because each can be measured.
There can also be a real trade-off between speed and quality: releasing data sooner may mean it is less complete or accurate than a later release. Make that trade-off visible, describe its effect on the intended use, and let users judge whether the earlier data is adequate for their decision.
What should a data-quality report tell users?
A useful report makes a quality measure interpretable rather than presenting a number without context. Include the dataset or fields assessed, the rules applied, the scope and date of measurement, and material limitations. Explain known missing records, duplicates, inconsistencies, collection timing, and cleaning choices when they affect how the data can be used.
For teams assessing a process or tool, useful evaluation questions include whether it fits the intended use, covers the relevant quality dimensions and data lifecycle, helps identify causes rather than only symptoms, supports monitoring and communication, and makes the cost of timeliness-versus-completeness trade-offs clear. These are assessment criteria, not a ranking of products.
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Guidance for identifying data-quality issues
The GOV.UK data-quality issues framework, updated 16 April 2026, describes identifying, prioritizing, and addressing issues in a data asset. Businesses can use that approach as a reference, while adapting its application to their own users, processes, and obligations.
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