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The Data Value Chain: Modeling Data-driven Value Streams

Data teams are contributing to a variety of value streams, as they are delivering value to a variety of stakeholders. The value streams are often not well-supported and the involved teams are facing constant challenges like Data Quality and Data Ownership. Also, data products often rely on the same data point for build the product and for measuring its success - so a lack of data quality leads to poor product quality and weak measurability at the same time. These challenges become exponentially harder, the larger the organization has grown. We propose a way of conceptualizing and visualizing the process of building data products, using the concept of the data value chain.

Vorkenntnisse

  • Experience with developing/building/maintaining data and analytical products, ML-based solutions etc. and/or experience with leading/supporting teams that own data products, like Data Science/ML teams, Analytics teams or Product teams with a strong data component

Lernziele

  • Understand how to decompose the process of building data products, how to visualize value streams and their contributing components, and how use these tools to align data-driven product development in your organization
  • The focus is on enabling/empowering teams and people by providing them an organizational environment that is designed to maximally support their work

Speaker

 

Stefan Kühn
Stefan Kühn beschäftigt sich seit vielen Jahren mit Data Science, Machine Learning und mathematischer Grundlagenforschung. Ihn interessiert insbesondere, wie man Kompetenzen in den Bereichen Data, Data Science, ML und AI innerhalb von Organisationen aufbauen und produktiv nutzen kann.

Gold-Sponsoren

INFOMOTION
denodo

Silber-Sponsor

inovex

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