Russoms (2006) article talks ab forbidden the consequences of poor- flavor info and the advantages of high-quality data. In your view, to what boundary are the data-quality statistics in Figures 1 through 4 in the article consistent with your organizations data quality mail service? prove at least two different ways that database prudence software like MicrosoftÂ® AccessÂ® can support an organization avoid or reduce data-quality problems mentioned in the articleRussom (2006) points out that thither was a trend toward paying more oversight to the quality of data being used in the piece of work in the midst of 2001 and 2005 following a change in responses to whether this data abnormal ?losses, problems or costs?, which brightens sense. Data is ample to have, but if you?re working with data of poor quality, so your statistics will be off and thus unreliable. One of the points touched(p) on by Russom (2006) that struck home for me in monetary value of my organization is l osing credibility due to poor data quality. As HRIS for the entire Alaska region, we affirm quite a bit of data on our employees. If we make data entry mistakes (figure 1), statistics will be off on the discussion sectional level, the location (process level) level, the regional level, and across the entire organization, not to mention just for the employee who logs in to check their information.
Let?s take a mere(a) data entry of an paygrade score. We enter performance evaluations on employees, which then generates their merit give rise for the year. If we score them preceding(prenominal) or below their actua l rate (data entry error) and they sw in a! llow an incorrect raise, that affects the employee (paid less or more), the department (the budget was wedge by less or more), and payroll department (they indispensability to retro pay or take binding money)?all from one error. We have remedied much... If you want to get a overflowing essay, order it on our website: OrderCustomPaper.com
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