Data are classified as “dirty” when the database contains errors that render the data inaccurate. This compromises the data integrity. Dirty data may result from human errors in entering data, such as misspelling a name or incorrectly entering an ID number. Dirty data may also result from viruses, worms, or other bugs installed into a system. Hackers may enter a system and alter or remove data. Hardware and software may fail, corrupting or destroying data.
3 thoughts on “Dirty Data / Nursing Informatics Reading and Sharing”
Great article. It is rather unfortunate that over the last years, the travel industry has already been able to to deal with terrorism, SARS, tsunamis, flu virus, swine flu, as well as first ever true global tough economy. Through all this the industry has really proven to be strong, resilient in addition to dynamic, getting new strategies to deal with misfortune. There are always fresh challenges and chance to which the industry must yet again adapt and react.
Great beat ! I would like to apprentice at the same time as you amend your web site, how could i subscribe for a blog web site? The account helped me a acceptable deal. I had been tiny bit familiar of this your broadcast provided vivid clear idea
This blog does not need to be subscribed