I’ve faced the significant decisions before and I have the power to share my own insights with you in articles. More importantly, perhaps I’ve made mistakes and I have those experiences to share. In other words, I know the mistakes that others should avoid. As an expert in data integration and data quality niche, I would like to showcase what I know that would help you to build a 100% pure or defect free 340B data quality solutions.
The quality of the data is the back bone of a company’s success and only such company will be doing well in terms of revenue growth, effective customer relationship, deeper strategic acquisition, hiring more human resources, etc., On the other hand, an organization won’t excel in customer relationship or revenue growth if the quality of data is poor. Based on my experience, we must look at the data in many dimensions. They areCompleteness – What data is missing? For example, Are PAYER data missing with PAYER CONTROL NUMBER? Are Drugs missing NDC details? If so, you must able to assess the completeness of the data which will answer these questions and act as an indicator of the data quality issue.
Conformity – What data is stored in non-standard formats? Do the columns contain the same data or different types of data? For example, does a field labeled as COVERED ENTITY contain only the name of covered entity or value of other columns as well? This information must be analyzed or profiled while assessing the data quality.
Consistency – What data gives conflicting information? Are values that have the same meaning represented in a number of different ways? For example: the location information of the PRESCRIBER data or value of the COVERED ENTITY ZIP such as FL, FLA, FLORIDA, etc.,
Accuracy – How accurate the data in the FEE table or is it out of date? Facts of deeper knowledge or insights about the data is required to determine the accuracy.
Integrity – What data is not referenced or missing? For example, does the PRODUCT_SERVICE_ID in the FEE table matches with the NDC in the DRUG table? You must make sure that the integrity of the data is intact prior to load this information into a structured or unstructured objects.
Once you have the answers, you must be able to identify the areas of the business is affected by the data quality issues and talk with the stakeholders of your data for the mitigation plan. By assessing the data in various dimensions and address the issues, one must have a definite success in business.