💥Data Quality is almost always done wrong!
Many organizations know the importance of the data quality. They invest so much money, time and resources in DQ with the hope that when they need to rely on data, it is accurate. The main issue is they look at the data in a #datadriven approach. That means the DQ becomes a project writing some validation rules around some field of information without any context.
Additionally, the rules of data validation are written in a generic programming language or an off-the-shelf product with a proprietary language, which will expose too many issues and challenges down the line:
🔸If done by coding, it is in the hands of IT and software development, which have no context on when and why this data is used
🔸When done with off-the-shelf products, vendor lock-in becomes a problem for strategic initiatives like Data Quality.
🔸finding talent to know a specific product or vendor well or training the existing staff increases the required investment time and budget.
Any #DataQuality #DQ #DataGovernance #DQM should be done in the context of business decisions and business concept models.
Why?
💣Any Data Quality project hopes to be used in decision-making in organizations, right? If not, why bother in the first place?
Looking into #data #quality for decision-making will not only solve the context-less DQ project but also it will open a new avenue for data quality in your organization.
💡Modeling business decisions has its open standard. Using Decision Model and Notation (DMN) in data quality and validation projects ensures:
🔹You won't lock into a specific vendor for validation
🔹Training staff becomes way easier on a widely acceptable standard
🔹Finding talent is quicker, and you will have access to a larger pool of talents rather than specific Data quality vendor professionals
More importantly, when the Data Quality is done using Decision Model and Notation (DMN)
🔹The DQ starts with the context, the decision model (Decision Requirement Diagram)
🔹The DQ uses the Fact Concept to specify the concept model of the business
🔹The DQ uses the standard rules modeling and business-friendly expression language (FEEL) as part of the DMN open standard
🚀When the Data Quality project is done in DMN, you can democratize it inside the teams, groups, functions, and operation and SME teams, with the context, the business decision. It becomes an iterative and continuous efforts that is based on decision model for improving decision-making.
Posted here.
Published July 18th, 2024 at 07:30 am
