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Key to a successful Data Science project

Colours:. #4BBDAD Rgb : 75,189,173. #FFC100 Rgb : 255,193,0. #FF9A00 Rgb:255,154,0. #0C1938 Rgb : 12,25,56. Key to a successful Data Science project. #FF7400 Rgb : 255,116,0. #848483 Rgb : 132,132,131. Kasia Kulma, PhD Senior Data Scientist. #DDDAE1 Rgb : 221,218,225. #FF4D00

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Key to a successful Data Science project

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  1. Colours: #4BBDAD Rgb: 75,189,173 #FFC100 Rgb: 255,193,0 #FF9A00 Rgb:255,154,0 #0C1938 Rgb: 12,25,56 Key to a successful Data Science project #FF7400 Rgb: 255,116,0 #848483 Rgb: 132,132,131 Kasia Kulma, PhD Senior Data Scientist #DDDAE1 Rgb: 221,218,225 #FF4D00 Rgb: 255,77,0

  2. Image set to 80% transparency Layered on top of Gradient Goal of Data Science? empower business to make better, evidence-based decisions

  3. Business case Goal: churn model for a telecom company Preliminary Result: 3 months in, model outperforms current retention specialists Big Meeting: silly questions… Roll-Out: useless spreadsheets Next steps: Data Engineer required & 3 more months of work needed Source: https://www.predictiveanalyticsworld.com/patimes/empathy-and-data-science-a-fable-of-near-success0903153/6250/

  4. Technical but not Business success • Stakeholder engagement • Language • Delivery • New Solution VS Existing Process

  5. Image set to 80% transparency Layered on top of Gradient Definition of success

  6. Definition of success Completed on time and to budget With all features and functions initially specified Achieves its business goals End  product  is used frequently High quality & understandable information SatisfactoryIT service support Source: Alfaadelet al. (2012), Recent Researches in Applied Computers And Computational Science

  7. Why do they fail? • PEOPLE-RELATED RISKS • Lack of top management support • Weak project manager • No stakeholder involvement and/or participation • Weak commitment of project team • Team members lack requisite knowledge and/or skills • Subject matter experts overscheduled • PROCESS-RELATED RISKS • Lack of documented requirements and/or success criteria • Ineffective schedule planning and/or management • Communication breakdown among stakeholders • Resources assigned to a higher priority project • No change management process • No business case for the project Kappelman LA et al. (2006) Early warning signs of IT project failure: the dominant dozen, Information Systems Management

  8. What do we recruit for? • Education (PhD & MSc in STEM/ Computer science) • Python/R Programming & SQL experience • Big Data architecture • Cloud solutions • Data engineering • Machine/Deep/Reinforcement Learning • Data visualization • Software development • Unstructured data

  9. Image set to 80% transparency Layered on top of Gradient What is Empathy?

  10. Content slide Image to right (sent to back) What is empathy Capacity to understand what another person is experiencing from within their frame of reference

  11. No one is simply born with empathy • Empathy increases with age (Dymond et al. 1952) • Empathy levels can be affected by environmental factors, e.g. high workload or stress (Baillie, 1996) • Empathy levels of hard working nurses declines over time (Ward et al. 2011)

  12. There is not just one type of Empathy • Empathy as: • a human trait • a professional state • a communication process • caring • a special relationship KunykD. &  Olson JK (2001) Clarification of conceptualizations of empathy, JAN Leading Global Nursing Research

  13. There is not just one type of Empathy • Empathy as: • a human trait • a professional state • a communication process • caring • a special relationship KunykD. &  Olson JK (2001) Clarification of conceptualizations of empathy, JAN Leading Global Nursing Research

  14. Empathy can be practiced and developed • Things that help improve empathy: • ‘Human’ conversations • Focused discussions on experiences • Rehearsal, feedback & imagery La Monica (1983),Empathy Can Be Learned, Nurse Educator

  15. Image set to 80% transparency Layered on top of Gradient Where does empathy fit into the Data Science process?

  16. Do not move logo position Full image slides, colour logo Data Science is NOT just Source: https://ismayc.github.io/poRtland-bootcamp17/

  17. Do not move logo position Full image slides, colour logo Data Science is more like Source: http://www.anovaanalytics.com/data-science-consulting/

  18. Do not move logo position Full image slides, colour logo Where empathy fits into the DS process • Identifying big/quick wins • Understanding Business context & processes • Pitching ideas to / gaining support of senior stakeholders • Understanding data • Assessing data quality • Do model predictions/predictors make sense? • Can we trust the model? • Operationalize & refine solution Source: http://www.anovaanalytics.com/data-science-consulting/

  19. My personal hacks framework What do you want to achieve? How to achieve it? How to make sure you achieve it?

  20. Image set to 80% transparency Layered on top of Gradient What do you want to achieve? AGILE User Stories

  21. AGILE User Story As a < type of user >, I want < some goal > so that < some reason >

  22. AGILE User Story Fsjh Dg Dg Dg Dsgg Dgsg Dsg Sdg Dg Gds Gd F F JD LongEconomist & Cocktail Party Host

  23. Image set to 80% transparency Layered on top of Gradient How to achieve it? Think like a function

  24. Think like a function. Be a function. OUTPUT INPUT FUNCTION

  25. Big picture WHAT/WHO DO I NEED TO ACHIEVE IT? HOW TO DO IT? WHAT DO I WANT TO ACHIEVE? Build a Churn model that is successfully used by Retention Team • Sign-off of Head of Analytics • Sign of and collaboration with Retention Team • Budget sign-off, • Suitable infrastructure, • Available Data Scientist, … • Finish current project / hire new Data Scientist, • Query available tools & data architectures, etc. • Meet Heads of Analytics & Retention re the budget, etc.

  26. Outputs become inputs WHAT/WHO DO I NEED TO ACHIEVE IT? HOW TO DO IT? WHAT DO I WANT TO ACHIEVE? Budget sign-off Write detailed and well researched Budget & timeline Convince the Head of Analytics Convince the Head of retention, … Comprehensive Budget & Timeline Head of Analytics sign-off Head of Retention sign-off

  27. Day-to-day (e.g. meetings) WHAT/WHO DO I NEED TO ACHIEVE IT? HOW TO DO IT? WHAT DO I WANT TO ACHIEVE? Understand current process of identifying customers most likely to churn 1. Ask relevant questions, e.g. Customer journey Churn algorithm Why like this? 2. Understand the current politic Meet the Retention team AND supporting Analysts

  28. Image set to 80% transparency Layered on top of Gradient How to make sure you achieve it? Checklists

  29. Checklists • A job aid used to reduce failure by compensating for limits of human memory and attention • In Aviation: standard procedure in various situations: pre-flight, landings, take-offs, malfunctions, and emergencies • In Medical Surgery: post-surgical deaths drop by 22% (McCarthy2017, BMJ)

  30. Checklists in empathetic Data Science • Why is this solution important? • How would you use it? • What does your current solution look like? • What other solutions have you tried? • Who are the end-users? • Who else would benefit from this solution? • …

  31. Final thought (explicit) expectations drive change.

  32. Take home • No communication & empathy – No success • Empathy can be developed • What do you want to achieve? Agile user Stories • How to achieve it? Think like a function • How to make sure you achieve it? Checklists

  33. Kasia Kulma @KKulma Thank you! Any questions? Let’s have a chat – now or later kkulma@mango-solutions.com

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