The availability of big data in the Digital Era enables new generation industries to create novel business models and automate their operations. It also assists them in developing innovative technology solutions that lead to new commercial opportunities. Sensors, machinery, social media, Web sites, and e-commerce portals all create large amounts of data. Any organization’s success is determined by the quality of the data it collects, stores, and uses to derive insights, and quality data is the foundation of any business and is found at the bottom of the information hierarchy. Data quality can be defined as a trait that makes data fit for its intended use, as well as a characteristic that allows data to accurately represent the genuine picture it is designed to portray.
By following the criteria in the several disciplines of data cleansing, data integration, and metadata, any Data Quality Tool can often accomplish data cleansing, data integration, master data management, and metadata. These DQ solutions provide processes and procedures to generate quality data at the source, in addition to purifying the data as it is being created.
What are the seven essential features that define data quality tools?
Legitimacy and Validity: This characteristic’s boundaries are defined by data-related requirements. Gender, ethnicity, and nationality, for example, are often limited to a range of possibilities on surveys, and open responses are not permitted. Based on the survey’s requirements, any additional responses would not be considered genuine or authentic. This is true for the majority of data, and it must be taken into account when judging its quality. The requirements must be used when evaluating data quality since employees in each area of an organization understand what data is valid to them.
Precision and Accuracy: This attribute refers to the data’s precision. It must not contain any inaccuracies and must convey an accurate message without being deceptive. This precision and accuracy have a component that is related to the intended usage. It’s possible that ensuring accuracy and precision will be off-target or more expensive than necessary if you don’t know how the data will be used.
Timeliness and Relevance: To justify the effort necessary, there must be a valid cause to gather the data, which also means it must be collected at the proper time. Data gathered too early or too late may misrepresent a situation and lead to erroneous…
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