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There is a nine-step guide for organizations that wish to improve data quality: [3] [4] Declare a high-level commitment to a data quality culture; Drive process reengineering at the executive level; Spend money to improve the data entry environment; Spend money to improve application integration; Spend money to change how processes work
Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.
In computer science, garbage in, garbage out (GIGO) is the concept that flawed, biased or poor quality ("garbage") information or input produces a result or output of similar ("garbage") quality. The adage points to the need to improve data quality in, for example, programming. Rubbish in, rubbish out (RIRO) is an alternate wording. [1] [2] [3]
The benefits of data profiling are to improve data quality, shorten the implementation cycle of major projects, and improve users' understanding of data. [9] Discovering business knowledge embedded in data itself is one of the significant benefits derived from data profiling. [ 5 ]
ISO 8000 is the international standard for Data Quality and Enterprise Master Data.Widely adopted internationally [1] [2] [3] it describes the features and defines the requirements for standard exchange of Master Data among business partners.
The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms), normal imputation is needed. [111]
While data governance initiatives can be driven by a desire to improve data quality, they are often driven by C-level leaders responding to external regulations. In a recent report conducted by CIO WaterCooler community, 54% stated the key driver was efficiencies in processes; 39% - regulatory requirements; and only 7% customer service. [6]
"Information quality" is a measure of the value which the information provides to the user of that information. [1] " Quality" is often perceived as subjective and the quality of information can then vary among users and among uses of the information.
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