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DGIQ-W23: Pioneering Governed Data Quality

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Pioneering Governed Data Quality

As a 175-year-old successful global technology leader, Zeiss owns very large and constantly growing data volumes. Its Data & Analytics unit was created to support the company’s ambition to advance data as a strategic asset and become a hyper-intelligent data-driven organization. The mission of the Enterprise Data Quality team within this unit is to lay the foundation for trusted high-quality data and ensure that it is consistently measured, assessed, and monitored against business expectations.

The case study brings forward first-hand experience and learnings from initiating a program to supporting the rollout of a centralized data quality toolkit and governance framework across a large organization, including:

Why build a data quality toolkit in-house instead of buying from a vendor
How to profile large volumes of structured and unstructured data from device logs in near-real-time
How to create a data quality governance framework from scratch
How to define a quality onboarding process in a data mesh environment
How to create and monitor qualitative and quantitative indicators to demonstrate the value and success of the program

Speakers: Régis Deshayes and Stefan Hilbert

Régis Deshayes

Régis Deshayes is the Head of Data Quality & Data Standards at Zeiss Digital Partners where his team’s mission is to establish an enterprise data quality governance framework and support the roll-out of data standards across the organization. Prior to that, he represented Carl Zeiss Meditec in various healthcare Standards Development Organizations (DICOM, IHE, HL7). Earlier on, he ensured high data quality onboard seismic vessels for marine geophysical surveys at CGG and across European biological laboratories at Bio-Rad Laboratories.

Stefan Hilbert

As Product Owner for Data Quality and Observability at the Data & Analytics unit of Zeiss Digital Partners, Stefan Hilbert helps develop and operate data quality tools and services for Zeiss. Prior to that, he worked at Erium GmbH (a Munich-based data science startup) on data science projects in manufacturing and automation and also developed software for Bayesian machine learning. Before that, he worked for many years as a researcher in astrophysics and cosmology with a focus on high-performance computing, numerical simulations, and the analysis of large and complex datasets.