This is Laurens van der Drift's practical take on the Metro Model. Drawing on his experience building data management software and practical projects for government organizations and others, Laurens shares his insights on data quality within the Metro Model. The discussion is peppered with practical examples and sharp observations on how organizations can manage data more effectively.
Data quality as a strategic added value
Laurens argues that data quality is more than just meeting compliance requirements. It's about setting internal requirements for data that align with the organization's mission and processes. He distinguishes three dimensions of data quality:
- Source quality: Is the data recorded correctly?
- Transaction/transport quality: How does data move through systems (data lineage)?
- Contextual quality: Does the data match the user's goal?
From data lineage to business rules
A key theme is data lineage: understanding the path data takes from source to use. Laurens explains how errors arise along the way and advocates for a sharp eye on critical data objects. From these objects, you then build business rules, preferably based on automated analysis such as machine learning.
Example: An analysis of youth care data revealed a 44-year-old "youth care client." This was an exception, caused by the fact that unborn children were not registered in the Personal Records Database (BRP) and therefore their data was stored with the mother. Such insights require reflection on both technology and policy.
Impact in euros: why it matters
Laurens emphasizes that data quality has a direct financial impact. Think of double-paid invoices, incorrect subsidies, or misclassified transactions. And while some effects are difficult to quantify in euros, the costs of bad data are undeniable. The unnecessary work for BI teams, sometimes requiring only 70% of their time, also constitutes a strong business case for structural improvement.
Governance and ownership
Without internal knowledge and ownership, improvements remain superficial. Laurens is critical of outsourcing data and IT expertise. Organizations, especially government bodies, should appoint their own people such as data stewards and CDOs to manage data quality and ensure continuity.
Start small, build up
Finally, Laurens van der Drift advocates a bottom-up approach. Start with a specific pain point, map critical data objects, create simple business rules, and measure the results. Demonstrate the improvement and gradually expand. Data quality should become a habit, just like exercise!
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