Improve your data quality yourself with the Metro Model

You can watch the first episode about improving your data quality yourself with the Metro Model on YouTube, or listen to it on Spotify and Apple Podcasts.

Improving Data Quality with the Metro Model

Data quality is becoming increasingly crucial for organizations that want to be data-driven. But how do you ensure that data is not only accurate and complete, but also used effectively? In an inspiring conversation with Marco and Peter, we discuss the Metro Model: a framework that helps structure and improve data quality management. This model, based on ISO 9001, provides organizations with guidance and helps them improve data quality at the strategic, tactical, and operational levels.

DAMA Netherlands and the Data Quality Working Group

Marco Heij and Peter van Nederpelt are active in DAMA NL, part of the international DAMA network. This network facilitates knowledge sharing around data management. The data quality working group focuses specifically on developing standards and tools, such as the Metro model, so that organizations don't have to reinvent the wheel every time.

What is the Metro Model?

The Metro Model visualizes data quality management as a metro network with thirty "stations," or elements, that together form a complete management system. The four layers of the model are:

• Objectives: The core of the model is that data quality meets requirements and that users are satisfied.
• Strategic level: This is where the main points are set out, such as drawing up a data quality policy and management involvement.
• Tactical level: This level focuses on governance, stakeholder management and translating strategy into concrete measures.
• Operational level: This concerns the daily implementation, such as data cleansing and monitoring.

The model is designed to offer organizations flexibility: you can join at any desired level and determine which elements are most important at that moment.

Why is ownership crucial?

One of the biggest challenges in data quality is ownership. Who is responsible for data quality? According to Marco and Peter, it's essential that this is properly established at all levels. This is integrated into the Metro Model by assigning a clear responsibility for each element. This prevents data quality from remaining a floating issue without concrete action points.

Your organization and the Metro Model

By using the model as a starting point, various departments within your organization can work on data quality in a uniform manner. This leads to better coordination between teams and a more effective approach to data governance.

The next step: From Data Quality to Data Utilization

Data quality is not a goal in itself, but a means to actually utilize data. This is the next step the data quality working group is focusing on. How do you ensure that

How high-quality data is used for data-driven work, AI, and dashboards? This is a topic that will be explored further in future discussions.

Stay informed!

Want to learn more about the Metro Model and how you can improve data quality in your organization? Subscribe to our newsletter via The Sonny Side of Life. We'll share valuable materials like the Metro Model for free, and you'll stay up-to-date on future episodes!