Data Modeling: Data Quality Analysis
Data quality analysis is critical in all of the solutions from Complete Data Quality, especially the data modeling solution. The data modelers are tasked with ensuring that the data is classified properly and the data quality team is tasked with ensuring that the classified values align to the data quality standards. These are normally two different sets of knowledge at the enterprise level.
The fact remains that no one is perfect, so the data modelers’ work must be validated before presenting the classification results to the data community for certification. The data quality assessment tool provides the exact same capabilities as the position analysis tool to the data quality analyst. The results are presented in a different order to align to the data quality analysis.
The data quality analyst first reviews the original value to understand the quality of the data being presented for classification. The next step is to validate that the data modelers classified the data properly by reviewing the fully redacted version of the value. The final step in the process is to validate the classified values themselves and ensure that the content meets organizational data quality standards.
The software tools are functionally the same, it’s the analysis intent and process that is different.
All CDQ data quality assessments require the assessment results to be validated by a lead data quality analyst. The lead verifies that the data quality analysts properly enforced the organizations data quality standards to all of the classified values.
The validated data quality assessment results are then released by the project manager for certification by the data community.
In the next article, we will review the certification of the classified values by the data community.
Clean Cloud is patent pending.
This is very basic. Many organizations fail to understand the need of Data quality assessment. They start many IT initiatives to modernize their IT landscape but bypass the data quality strategy coz cost and other inter dependencies. I mean before you start any Data strategy, any data management solutions, any data engineering, data science etc. you got to figure out what is the health of your data. Otherwise you are not going anywhere.