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#Futureshaper

The data conductor

In order to develop artificial intelligence (AI) for use in medical technology, our research scientists need one thing above all: colossal quantities of all kinds of correct and secure medical data. Ren-Yi Lo, Head of our Big Data Office, is in charge of collecting, preparing, and organizing these data. So, what does all that have to do with music? Learn that and more in part six of our #Futureshaper series.

8min
Katja Gäbelein
Published on March 6, 2023

A "data lake" is, in principle, exactly what the term suggests: a giant "lake" of semi-structured data. Based in Princeton, New Jersey, USA colleague Ren-Yi Lo manages one such data lake together with her international team. 

Photograph portrait of from Ren-Yi Lo taken from behind her. She stands on the shore of a lake, looking to her left, smiling.

The quality of the data is decisive

Big data: Invisible, yet essential

Portrait of a smiling Ren-Yi Lo.

I sometimes feel a bit like I'm a 'Jill of all trades*. But I suppose that's really exactly what I have to be for my job.

Ren-Yi Lo
Head of Big Data Office, Siemens Healthineers

The right data cohorts

Complex standardized process

Graphical representation of the data lifecycle process with its three superordinate steps "Develop new partners", "Ingest data", and "Manage data".

Process overview of the data lifecycle

Anonymization is essential

Sherlock, the AI supercomputer

The big opportunities of digital models

By Katja Gäbelein

Katja Gäbelein works as an editor in corporate communications at Siemens Healthineers, and specializes in technology and innovation topics. She writes for text and film media. 

Assistant editor: Guadalupe Sanchez

  1. Sources

  2. Al-Mekhlal, Monerah; Khwaja, Amir Ali (2019): A Synthesis of Big Data Definition and Characteristics. In: IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). pp. 314-322. Available online: https://ieeexplore.ieee.org/abstract/document/8919591

  3. Gethmann, Carl Friedrich; Buxmann, Peter; Distelrah, Julia; Humm, Bernhard G.; Lingner, Stephan; Nitsch, Verena; Schmidt, Jan C.; Spiecker (Döhmann), Indra (2022): Künstliche Intelligenz in der Forschung – Neue Möglichkeiten und Herausforderungen für die Wissenschaft. (Artificial intelligence in research – New opportunities and challenges for science) Berlin: Springer, p. 8.

  4. https://ischool.illinois.edu/degrees-programs/courses/is547#:~:text=Data%20curation%20is%20the%20active,for%20re-use%20over%20time

  5. https://www.dzif.de/de/glossar/kohorte

  6. https://gdpr.eu/tag/gdpr/

  7. https://www.fda.gov/

  9. Disclaimer
  10. The presented information is based on research results that are not commercially available. Its future availability cannot be ensured.
  11. The figures shown here are as of February 2023.