Dirty data, also known as rogue data,[1] are inaccurate, incomplete or inconsistent 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data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database. They can be cleaned through a process known as data cleansing.[3]
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Dirty Data- Why You Should Care
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School Data - A Comedy
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Sources of Dirty Data - Data Wranging with MongoDB
Transcription
Hi I'm Jared Hillam, If there’s one thing that’s surprised me through my years in the data and information management industry, it’s how little companies spend on data quality. At one point I even determined that Executives just don’t care about their data. Well today I’m going to share why you SHOULD care, and how dirty data can be a hazardous issue to ignore We’re going to start by entering into the operation of a company Lets follow an order through this company with dirty data We’ll imagine this is a large manufacturer of expensive industrial parts, and that a sales rep finds and closes a deal with a brand new customer. Now early on in the sales process the sales rep carelessly entered in 401 6th Street as the customers street address instead of the right address which is 401 9th Street. This is no more than a simple typo on a computer keypad, but it happens all the time. The order enters into the organization and the process carries its course fulfilling the customer request. However, it’s not just the flow of the process that gets penetrated, this information propagates into the various systems that support this process. Now not everybody in the company falls for the dirty data. In fact in this case and astute Billing Department confirms the correct billing address and enters it into their system. So the bill arrives without a hitch, but the product never reaches the customer We can see here that your data is fully linked your company’s process. Dirty Data can be the source of consistent train wrecks. And what’s more Dirty Data can make it far more difficult to make accurate strategic decisions. Intricity Specializes in delivering data and information management solutions which take dirty data into account, and synchronize the data that supports your various processes, which in turn makes for happy customers. I encourage you to talk with one of their Specialist, so that we can get in the business of making your customers happy.
Dirty Data (Social Science)
In sociology, dirty data refer to secretive data the discovery of which is discrediting to those who kept the data secret. Following the definition of Gary T. Marx, Professor Emeritus of MIT, dirty data are one among four types of data:[4]
- Nonsecretive and nondiscrediting data:
- Routinely available information.
- Secretive and nondiscrediting data:
- Strategic and fraternal secrets, privacy.
- Nonsecretive 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
References
- ^ 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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