Dirty data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database. They can be cleaned through a process known as data cleansing.
Dirty Data (Social)
Following the definition of Gary T. Marx, Professor Emeritus of MIT, there are four types of data:
- Nonsecretive and nondiscrediting data:
- Routinely available information.
- Secretive and nondiscrediting data:
- Strategic and fraternal secrets, privacy.
- Nonscretive and discrediting data:
- sanction immunity,
- normative dissensus,
- selective dissensus,
- making good on a threat for credibility,
- discovered dirty data.
- Secretive and discrediting data: Hidden and dirty data.
- Spotless version 12 out now
- Margaret Chu (2004), "What Are Dirty Data?", Blissful Data, p. 71 et seq, ISBN 9780814407806
- Wu, S. (2013), "A review on coarse warranty data and analysis" (PDF), Reliability Engineering and System, 114: 1–11, doi:10.1016/j.ress.2012.12.021
- "Notes on the discovery, collection, and assessment of hidden and". web.mit.edu. Retrieved 2017-02-17.
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