(EMEA) VIDEO | Even the Best Data Sets Are Valueless If They Cannot Be Found — Zeiss Group Head of Data Quality

Regis Deshayes, Head of Data Quality, and Stefan Hilbert, Data Scientist at Zeiss Group, speak about the future of data quality and data literacy, the role of AI in automating data quality, adopting FAIR principles, driving data literacy, and strategies to implement successful data programs.

Regis Deshayes, Head of Data Quality, and Stefan Hilbert, Data Scientist at Zeiss Group, speak with Derek Strauss, Chairman at Gavroshe, and Editorial Board Member, CDO Magazine, in a video interview about the future of data quality and data literacy, the role of AI in automating data quality, adopting FAIR principles, driving data literacy, and strategies to implement successful data programs.

Zeiss is a 177-year-old German manufacturing company that specializes in industrial metrology, medical device manufacturing, vision care, and semiconductor manufacturing technology.

When asked about the future of data quality and literacy, Hilbert states that data quality requires extra thought to date. He wishes it to become a common part of data and metadata in the future.

Further, Hilbert points towards automating data quality and applying it to the data all along its lifecycle. He calls it data observability and hopes it becomes an intrinsic part of data quality in the future.

Moving forward, Hilbert asserts that artificial intelligence will play an integral part in automating data quality aspects such as generating data quality rules and finding anomalies automatically. Additionally, he says that to rely on AI, entities must ensure that AI understands data very well and there is a close connection between data quality and the semantics of the data.

In coherence with Hilbert, Deshayes adds that it is critical to measure the quality of the metadata. He notes that even if the data set is of excellent quality, it will be of no value if it cannot be found or shared, and is documented poorly.

Therefore, Zeiss is expanding data quality to encompass metadata quality by adopting the FAIR principles, says Deshayes. He maintains that FAIR stands for Findable, Accessible, Interoperable, and Reusable, and is being applied to the industry.

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Highlighting the future of data literacy, Deshayes states that people will remain central to the success of data quality programs regardless of the automation and support of AI. Further, he recommends organizations adopt a multi-threaded approach to data literacy programs by combining traditional training and online educational material to ensure scalability.

The future of data literacy will feature more adoption of gamification, says Deshayes. He mentions experimenting with this approach by incorporating a quiz into training, which has aided in more engaged sessions.

Furthermore, Deshayes says that although Zeiss is focused on the tools part while implementing the data program, he would recommend organizations start with people and processes. He also suggests investing in developing an in-house APAC Wiki.

In conclusion, Hilbert states that while data is essential for business now and in the future, data quality is inevitably essential. However, reaching that bright future with data quality will require a systematic approach and hard work.

CDO Magazine appreciates Regis Deshayes and Stefan Hilbert for sharing their insights and data success stories with our global community.

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