data projects

Today we are talking to Dr. Anna Melbinger, theoretical physicist and managing consultant at Xenium in Munich. She had been conducting research in Munich, Paris and San Diego and is now supporting and accompanying both IT and Data projects for several years. Anna will try to paint a vivid picture for the listener on how IT and data projects differ while challenging her ideas with Rainer Raupach, Founder of Novedas, Satya Dharnapuram, Consultant at IBM, Dr. Johannes Nehrkorn, Chief product owner for AI Services Siemens Industries and CEO of Goldblum Consulting, Ansgar Bittermann. 

Data Science in a commercial setting should be a no-brainer, right? Firstly, data is becoming ubiquitous, with gigabytes being generated and collected every second. And secondly, there are new and more powerful data science tools and algorithms being developed and published every week. Surely just bringing the two together will deliver success...

In this episode, we explore why so many Data Science projects fail to live up to their initial potential. In a recent Gartner report, it is anticipated that 85% of Data Science projects will fail to deliver the value they should due to "bias in data, algorithms or the teams responsible for managing them". There are many reasons why data science projects stutter even aside from the data, the algorithms and the people.
We discuss six key technical reasons why Data Science projects typically don't succeed based on our experience and one big non-technical reason!

And being 'on the air' for a year now we'd like to give a big Thank You to all our brilliant guests and listeners  - we really could not have done this without you! It's been great getting feedback and comments on episodes. Do get in touch jeremy@datacafe.uk or jason@datacafe.uk if you would like to tell us your experiences of successful or unsuccessful data science projects and share your ideas for future episodes.

Further Reading and Resources

• Article: "Why Big Data Science & Data Analytics Projects Fail" (https://bit.ly/3dfPzoH via Data Science Project Management) 
• Article: "10 reasons why data science projects fail" (https://bit.ly/3gIuhSL via Fast Data Science) 
• Press Release: "Gartner Says Nearly Half of CIOs Are Planning to Deploy Artificial Intelligence" (https://gtnr.it/2TTYDZa via Gartner) 
• Article: "6 Reasons Why Data Science Projects Fail" (https://bit.ly/2TN3sDK via ODSC Open Data Science) 
• Blog: "Reasons Why Data Projects Fail" (https://bit.ly/3zJrFeA via KDnuggets)

Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.

Recording date: 18 June 2021

Intro music by Music 4 Video Library (Patreon supporter)

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