ORGANISING YOUR ADVANCED ANALYTICS PROJECTS FOR SUCCESS - Big Data Expo 2019
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The document discusses the importance of advanced analytics and data science in driving business value, emphasizing the need to identify the right problems to solve. It highlights the roles of data scientists, analytics translators, and data engineers in creating data-driven solutions, while also addressing the challenges of hiring and retaining skilled professionals. The text encourages collaboration with business stakeholders and iterative deployment to validate assumptions and ensure effective outcomes.
![[..] — a solid foundation in math, statistics, probability, and computer science —
and certain habits of mind. He wants people with a feel for business issues and
empathy for customers. Then, he says, he builds on all that with on-the-job training
and an occasional course in a particular technology.
If “sexy” means having rare qualities that are much in demand, data scientists are
already there. They are difficult and expensive to hire and, given the very competitive
market for their services, difficult to retain. There simply aren’t a lot of people with
their combination of scientific background and computational and analytical skills.
Data Scientist: The Sexiest Job of the 21st Century
by Thomas H. Davenport and D.J. Patil
From the October 2012 Issue](https://image.slidesharecdn.com/dag2presentatie7-201909bigdataexpopresentation-190926073224/85/ORGANISING-YOUR-ADVANCED-ANALYTICS-PROJECTS-FOR-SUCCESS-Big-Data-Expo-2019-20-320.jpg)
![[..] — a solid foundation in math, statistics, probability, and computer science — and
certain habits of mind. He wants people with a feel for business issues and empathy
for customers. Then, he says, he builds on all that with on-the-job training and an
occasional course in a particular technology.
If “sexy” means having rare qualities that are much in demand, data scientists
are already there. They are difficult and expensive to hire and, given the very
competitive market for their services, difficult to retain. There simply aren’t a lot of
people with their combination of scientific background and computational and
analytical skills.
Data Scientist: The Sexiest Job of the 21st Century
by Thomas H. Davenport and D.J. Patil
From the October 2012 Issue](https://image.slidesharecdn.com/dag2presentatie7-201909bigdataexpopresentation-190926073224/85/ORGANISING-YOUR-ADVANCED-ANALYTICS-PROJECTS-FOR-SUCCESS-Big-Data-Expo-2019-21-320.jpg)
ORGANISING YOUR ADVANCED ANALYTICS PROJECTS FOR SUCCESS - Big Data Expo 2019
- 1. MAKE YOUR DATA-DRIVEN AMBITIONS COME TRUE WHEREVER YOU ARE IN THE WORLD 1 Organising Advanced Analytics for Success
- 2. Robin van den Brink Analytics Translator Lead of Xomnia X-Force old sci-fi books
- 3. Your big data partner We are a team of Data Scientists, Big Data Engineers and Analytics Translators empowering organisations to create maximum value out of data Robin van den Brink Analytics Translator Lead of Xomnia X-Force old sci-fi books
- 4. “We have a team of data scientists” “I run it every week in a notebook and send the output by mail” “We should use ML” “I want to use deep learning” “We had to productionize a POC with 3000+ lines of code and 6+ months of effort”
- 5. “We have a team of data scientists” “I run it every week in a notebook and send the output by mail” “We should use ML” “I want to use deep learning” “We had to productionize a POC with 3000+ lines of code and 6+ months of effort”
- 6. “We have a team of data scientists” “I run it every week in a notebook and send the output by mail” “We should use ML” “I want to use deep learning” “We had to productionize a POC with 3000+ lines of code and 6+ months of effort”
- 7. “We have a team of data scientists” “I run it every week in a notebook and send the output by mail” “We should use ML” “I want to use deep learning” “We had to productionize a POC with 3000+ lines of code and 6+ months of effort”
- 9. To enable a data driven future your company needs to overcome the “Credibility” phase of Data Science
- 10. START WITH FINDING THE RIGHT PROBLEM
- 11. A FEW REASONS WHY FINDING THE RIGHT PROBLEM IS IMPORTANT Fully understand the problem to find a solution from an end users perspective Machine Learning is a tool not a goal don’t add unnecessary uncertainty Adding business value no buy in for hobby projects Good Data Scientists are rare and expensive challenge them to reach full potential
- 12. A FEW REASONS WHY FINDING THE RIGHT PROBLEM IS IMPORTANT Fully understand the problem to find a solution from an end users perspective Machine Learning is a tool not a goal don’t add unnecessary uncertainty Adding business value no buy in for hobby projects Good Data Scientists are rare and expensive challenge them to reach full potential
- 13. A FEW REASONS WHY FINDING THE RIGHT PROBLEM IS IMPORTANT Fully understand the problem to find a solution from an end users perspective Machine Learning is a tool not a goal don’t add unnecessary uncertainty Adding business value no buy in for hobby projects Good Data Scientists are rare and expensive challenge them to reach full potential
- 14. A FEW REASONS WHY FINDING THE RIGHT PROBLEM IS IMPORTANT Fully understand the problem to find a solution from an end users perspective Machine Learning is a tool not a goal don’t add unnecessary uncertainty Adding business value no buy in for hobby projects Good Data Scientists are rare and expensive challenge them to reach full potential
- 15. IDEATE EXPERIMENT DEFINE BUILD EVALUATE LEARN DATA INSIGHTS STRATEGIC TOPICS OPERATIONAL PROBLEMS INNOVATIVE DISRUPTIONS DEPLOY EMPATHIZE DEFINING THE PROBLEM SOLVING THE PROBLEM MOVE ON
- 17. HOW CAN WE PREDICT DELAYS TO MITIGATE THE IMPACT ON THE OPERATIONS?
- 19. HOW WE DEFINE THE WORK OF DATA SCIENTISTS DOMAIN KNOWLEDGE COMPUTER SCIENCE MATH AND STATISTICS DATA SCIENCE Machine Learning Software Engineering Traditional Research
- 20. [..] — a solid foundation in math, statistics, probability, and computer science — and certain habits of mind. He wants people with a feel for business issues and empathy for customers. Then, he says, he builds on all that with on-the-job training and an occasional course in a particular technology. If “sexy” means having rare qualities that are much in demand, data scientists are already there. They are difficult and expensive to hire and, given the very competitive market for their services, difficult to retain. There simply aren’t a lot of people with their combination of scientific background and computational and analytical skills. Data Scientist: The Sexiest Job of the 21st Century by Thomas H. Davenport and D.J. Patil From the October 2012 Issue
- 21. [..] — a solid foundation in math, statistics, probability, and computer science — and certain habits of mind. He wants people with a feel for business issues and empathy for customers. Then, he says, he builds on all that with on-the-job training and an occasional course in a particular technology. If “sexy” means having rare qualities that are much in demand, data scientists are already there. They are difficult and expensive to hire and, given the very competitive market for their services, difficult to retain. There simply aren’t a lot of people with their combination of scientific background and computational and analytical skills. Data Scientist: The Sexiest Job of the 21st Century by Thomas H. Davenport and D.J. Patil From the October 2012 Issue
- 22. SO, WHAT IF WE MISINTERPRETED THIS VENN DIAGRAM? DOMAIN KNOWLEDGE COMPUTER SCIENCE MATH AND STATISTICS
- 23. COMBINE SKILLS INSTEAD OF HUNTING FOR UNICORNS DATA SCIENCE ANALYTICS TRANSLATOR DATA ENGINEER DATA SCIENTIST
- 24. ANALYTICS TRANSLATOR DATA ENGINEER DATA SCIENTIST CONNECT WITH IMPORTANT PARTNERS IN YOUR ORGANISATION IT BUSINESS DATA
- 25. Example DEVELOPING DATA DRIVEN PRODUCTS Unreleased video from Shipping Technology
- 26. ITERATE AND SHIP AS OFTEN AS POSSIBLE
- 27. ADVANCED ANALYTICS PROJECTS ARE UNCLEAR AND UNCERTAIN Clear Unclear Certain Uncertain Requirements Technology
- 28. WE WANT TO VALIDATE OUR ASSUMPTIONS IN THE REAL WORLD Start simple Prioritise model deployment early Force the validation of business value Move on if assumptions aren’t met Anticipate on unexpected consequences of your models interference
- 29. WE WANT TO VALIDATE OUR ASSUMPTIONS IN THE REAL WORLD Start simple Prioritise model deployment early Force the validation of business value Move on if assumptions aren’t met Anticipate on unexpected consequences of your models interference
- 30. WE WANT TO VALIDATE OUR ASSUMPTIONS IN THE REAL WORLD Start simple Prioritise model deployment early Force the validation of business value Move on if assumptions aren’t met Anticipate on unexpected consequences of your models interference
- 31. WE WANT TO VALIDATE OUR ASSUMPTIONS IN THE REAL WORLD Start simple Prioritise model deployment early Force the validation of business value Move on if assumptions aren’t met Anticipate on unexpected consequences of your models interference
- 32. Start simple Prioritise model deployment early Force the validation of business value Move on if assumptions aren’t met Anticipate on unexpected consequences of your models interference WE WANT TO VALIDATE OUR ASSUMPTIONS IN THE REAL WORLD
- 33. IDEATE EXPERIMENT DEFINE BUILD EVALUATE LEARN DATA INSIGHTS STRATEGIC TOPICS OPERATIONAL PROBLEMS INNOVATIVE DISRUPTIONS DEPLOY EMPATHIZE DEFINING THE PROBLEM SOLVING THE PROBLEM MOVE ON
- 35. TAKE AWAYS Build multi-disciplinary teams Combine the skills of an analytics translator, data scientist(s) and data engineer(s) to accelerate the business Collaborate with the (end) users Work closely with business stakeholders to achieve maximum value and adoption Iterate fast and ship often Get your solution in the real world to find the right solution and adjust accordingly Start with the problem Be critical and find a problem where ML/AI adds value. Don’t find a problem for ML/AI. These are all wrong.
- 36. QUESTIONS?
- 37. Feel free to reach out and start a discussion about your or our projects! robin.vandenbrink@xomnia.com