Dirty Data
Get Dirty Data essential facts below. View Videos or join the Dirty Data discussion. Add Dirty Data to your Like2do.com topic list for future reference or share this resource on social media.
Dirty Data

Dirty data, also known as rogue data,[1] is inaccurate, incomplete or erroneous data, especially in a computer system or database.[2]

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data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database. It can be cleaned through a process known as data cleansing.[3]

Following the definition of Gary T. Marx, Professor Emeritus of MIT, there are four types of data.[4]


  • 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.

See also


  1. ^ Spotless version 12 out now
  2. ^ Margaret Chu (2004), "What Are Dirty Data?", Blissful Data, p. 71 et seq., ISBN 9780814407806 
  3. ^ Wu, S. (2013), "A review on coarse warranty data and analysis", Reliability Engineering and System, 114: 1-11, doi:10.1016/j.ress.2012.12.021 
  4. ^ "Notes on the discovery, collection, and assessment of hidden and". web.mit.edu. Retrieved . 

  This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.



Top US Cities