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Work Methods for Data Scientists
A lack of methodology and a focus on technical factors rather than communication and interpersonal issues are cited as reasons for struggling with gaining value from analytics
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"Why Project Management Methodologies Matter in Data Science Implementation"
Organizations are struggling to get value from analytics, with over 85% of analytics projects never making it into production. A lack of methodology and a focus on technical factors rather than communication and interpersonal issues are cited as reasons for this, and the author suggests that data science professionals need to consider adopting a more formal approach to project management to improve implementation in analytics.
Discussion triggers
- Do you use a certain method when implementing data science projects?
- What is the role of communication and interpersonal issues in the success of data science projects?
- Why do only 25% of data science professionals follow a formal project implementation methodology, according to a recent survey?
- How might the way data science is taught at universities contribute to the lack of formal methodology in data science projects?
- Can the adoption of a formal project management methodology improve the implementation of data science projects, or are there other factors at play?
Meet the panel
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Cristina Perez
AI Customer Advisory, SASTwitter: @CrisPerezSAS
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David Weik
Solutions Architect, SASTwitter: @weik_david
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Eduardo Hellas
Statistician, Data Scientist , SASTwitter: @Pinduzera
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Melissa Jantjies
Analytics Professional, SASTwitter: @MelissaJantjie4
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