Industry pros say that "What is not measured isn't handled", and invariably what isn't handled makes it hard to maintain and improve. Data quality ought to be regarded as a measurable metric with quantifiable scores and well-defined data collections.
Lacking accessible tools it's perhaps not possible to guarantee quality and also the necessary industry impact that is required. You can get to about the best data quality tools via https://www.ringlead.com/.
The Data Quality (DQ) scorecard is a good method of pinpointing your data DNA. The scorecard will help to keep a check into quality dimensions such as accuracy, completeness, consistency, and compliance. In order to develop an insightful DQ Scorecard the next dimensions Will need to be considered:
Data Quality Dimensions
• Available and Complete – To make sure that available records are missing and complete data is added.
• Accurate and Recent – To establish whether it is upgraded and correct.
• Consistent – To see if they have been related to other elements within the data set and also coherent.
• Compliance with Standards – To verify whether they comply with industry standards.
• Definition – Diagrams must have well-defined targets of users and caliber rules.
• Applicable – Metrics should define how it improves operation and also have a small business context.
• Measurable – dents need to be quantifiable and measurable within a certain range.
• Controllable – Metrics have to specify a controllable facet of business processes.
• Traceable – There must be described as a time series' to track and trace the outcome so as to quantify progress and provide insights.
Therefore, via an organized and recognized approach, appropriate evaluations and relevant developments can be created, to build a robust foundation for companies, that is Quality Data!