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 organizations 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 characteristics 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 surveys 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 datas 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. Its possible that ensuring accuracy and precision will be off-target or more expensive than necessary if you dont 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 conclusions.
Reliability and consistency: Many systems in todays contexts use and/or collect the same source data, ensuring reliability and consistency. It cannot contradict a value stored in a separate source or collected by a different system, no matter what source collected the data or where it exists. There must be a consistent and reliable process for collecting and storing data that is free of inconsistency and unjustified variation.
Completeness and Comprehensiveness: Incomplete and erroneous data are both detrimental. Due to gaps in data collection, only a portion of the entire picture is displayed. Uninformed actions will occur if you dont have a clear picture of how operations are going. Its important to comprehend the complete set of requirements that make up a full set of data to decide whether or not the requirements are being fulfilled.
Granularity and Uniqueness: The level of detail at which data is collected is critical, as it can lead to misunderstanding and incorrect conclusions. Data that has been aggregated, summarized, and altered may have a different meaning than data that has been implied at a lower level. To make enough uniqueness and distinctive traits obvious, an adequate level of granularity must be defined. This is a prerequisite for operations to go smoothly.
Availability and Accessibility: Due to legal and regulatory limits, this attribute can be difficult to achieve at times. Individuals require the appropriate level of data access to accomplish their work, regardless of the obstacle. This assumes that the data exists and that access to it may be allowed.
Data quality tools are determined by a number of factors, each of which can be prioritized differently by different organizations. Prioritization may shift depending on an organizations stage of development or even its current business cycle. When reviewing data, keep in mind that you must establish what is most important to your firm.
