MDM — Governing Data Quality

Data Governance and MDM are more of an organisational issue than a tool issue. The technologies assist, but you still need humans to create and manage the definitions, resolve conflicts, and so on. It’s not really a data engineering item either, as most of the conversation has a commercial context, though it does deal with source definitions as well.

A controlled approach to data quality governance entails adopting a systematic and proactive framework to monitor, assess, and enhance data quality inside an organisation.

The following are the important steps in such an approach:

• Define Data Quality Standards: Begin by defining clear and quantifiable data quality standards that are in line with the organization’s goals. These criteria should specify what is expected of data in terms of correctness, completeness, consistency, timeliness, and validity. They serve as standards for evaluating data quality.
• Appoint Data Stewards: Data stewardship is the responsible and ethical handling of data inside an organisation throughout its lifespan. Data stewards serve as subject-matter experts, ensuring that data quality requirements are met and encouraging improvements.
  — It entails monitoring, protecting, and governing data assets to guarantee their quality, integrity, and compliance with applicable legislation and policies.
  — A…