Data & consent · Accountability lens
The Clean Data Imperative
What is clean data imperative?
The principle that organizations should verify data quality, establish permission and purpose, and maintain the information used for decisions. Unverified records, decaying contacts, and bot-inflated activity are infrastructure problems that require better systems and ownership.
The Clean Data Imperative: an illustrative example
A company discovers duplicate contacts, obsolete job titles, and records with no clear source or permission history in its CRM. It fixes the collection and maintenance process before launching the next campaign.
Sources and related reading
- Clean Data Alliance: The Weather of Data — related reading
This is a practical accountability lens. Examples illustrate the concept; they are not verified client results. Working definitions and accountability lenses are not claims of a universal standard or original coinage.
Related data & consent terms
- Clean data
Clean data combines quality with responsible use: accurate and verified where possible, collected for a clear purpose, used with appropriate permission, and maintained over time. Personal information is protected, and anonymous information is used where appropriate. A smaller, reliable audience can be more useful than a large list of suspect contacts.
- Dirty data
In data-quality work, dirty data is inaccurate, incomplete, duplicated, inconsistent, or outdated. Responsible data use also requires an established source, permitted uses, and permission history. Poor quality and unclear permission create different problems, and both deserve attention.
- Access control
Rules and technical measures restricting who can use information or systems.
- AI disclosure
Clear information about material AI involvement in a service or communication.