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Building a Data Science Capability in Retail

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A talk I gave on the key strategic decisions to make when building a data science capability. Read this if you are looking to build a team or your team has been recently formed.

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Building a Data Science Capability in Retail

  1. 1. Guerrilla Analytics: Building a Data Science Capability in Retail ENDA RIDGE, PHD
  2. 2. What You Will Learn  A Data Science Capability  Why do this? The strategic advantage of a Data Science capability in Retail  What do you need? The 3 components of a capability  Where do you start? 5 steps to build a capability  How this will help you  C-Suite, Directors, Heads:  Understand the vision you’re setting out  Know the obstacles you will have to smash down  Define milestones and measures of success  Data Scientists:  The support you must lobby for  Your focus in year 1 Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 1
  3. 3. What I’ve Learned PhD ‘Design of Experiments for Tuning Algorithms’ Boutique Consultancy Forensic Data Analytics Senior Manager Professional Services Head of Algorithms Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net No matter the industry, doing agile data science always faces the same challenge… 2004 2008 2010 2012 2015 Organisations do not have the flexibility to accommodate data science 2
  4. 4. The Strategic Advantage of Data Science Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 3
  5. 5. Challenges Have we changed customer buying behaviour? Could we tell when our plant will fail? Can we improve getting stock on shelves? Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 4
  6. 6. Challenges What are sensible product groupings? Where do we next locate a store? What factors really influence dwell time? Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 5
  7. 7. Problem characteristics Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net Complex, interrelated, living systems 6
  8. 8. Problem characteristics  Uncertainty  Data  Process  Questions  Solutions Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 7
  9. 9. Problem characteristics  New data, ‘informal’ data sources  Disparate sources  Surveys  Web scrapes  Logs  3rd party Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 8
  10. 10. Problem characteristics  Huge variety of solutions to try out  Data joins  Visualizations  Algorithms Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 9
  11. 11. You’re not ready for the factory line Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 10
  12. 12. What is Data Science? “Data Science is the discipline of understanding and using data to improve your business” Mathematics Statistics Machine learning Visualization - Enhance products - Find opportunities - Increase efficiency Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 11
  13. 13. Strategic advantage? Have we changed customer buying behaviour? Could we tell when our plant will fail? How do we make our warehouse more efficient? Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net Experiment design Predictive modelling Operations Research 12
  14. 14. What Data Science is not… Big Data traditional Business Intelligence creating beautiful visualizations just because we can Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 13 https://vimeo.com/88093956
  15. 15. Are you doing Data Science? Frame a business problem Gather and generate data Analyse Confirm with experiment Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net Business operations Data-driven products 14 Best in class organisations integrate Data Science into everything they do
  16. 16. 3 Components of a Data Science Capability Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 15
  17. 17. Typical mistakes  Not knowing how Data Science really works in the trenches Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 16
  18. 18. Typical mistakes  Not knowing how Data Science really works in the trenches  Expecting magic Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 17
  19. 19. Typical mistakes  Not knowing how Data Science really works in the trenches  Expecting magic  Bundling with IT  or isolating from IT Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 18
  20. 20. Typical mistakes  Not knowing how Data Science really works in the trenches  Expecting magic  Bundling with IT  or isolating from IT  Too much structure / bureaucracy Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 19 http://workplacereport.com/
  21. 21. 3 Components of a Capability Data Science Leadership Data People and Technology Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 20
  22. 22. Component 1: Leadership  Set the direction and support it  Changes to BAU  Inefficiencies exposed  Opportunities to capitalise on  Pitfall:  Data Science very difficult  Results don’t get used Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 21
  23. 23. Component 1: Leadership  Set targets and measure progress  What’s a Data Science KPI?  # of Algorithms in products?  Improvements to bottom line?  # of Experiments completed?  How to cost?  Pitfalls:  Whimsical projects  Losing business focus Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 22
  24. 24. Component 1: Leadership  Prioritise the pipeline  Pitfalls:  No strategic focus Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 23
  25. 25. Component 2: People & Technology  Hype says you need geniuses  Reality:  Communication  Consulting and Influencing  Tenacity  Passion  Pitfalls  Failure to understand business context  Disillusionment at obstacles  Cannot answer the ‘so what’? Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 24
  26. 26. Component 2: People & Technology What you need Pitfall Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 25
  27. 27. Component 2: People & Technology  Data Science needs technology flexibility  Faced with  Overwhelming firewalls  Irrational fear of Open Source  IT SLAs for server builds  Ad-hoc IT support  Pitfalls  Premature tech governance  Technology dictated from above Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 26
  28. 28. Component 3: Data  Data Scientists need access to your data  In the early days  Focus on blockers to access, storage  Let the Data Scientists work the data  Pitfalls:  Not taking a strategic view on your data  Making a data dictionary a pre-requisite  Letting security perceptions be an excuse  Sticking to outmoded ideas of ‘production data’ Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 27
  29. 29. 3 Components of a Capability Data Science Leadership Data People and Technology Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 28 • Vision • Smash barriers • Priority targets • Access • Security • Service Ops • Coal face • Soft skills • Flexible tech
  30. 30. 5 Steps to Build a Capability Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 29
  31. 31. 5 Steps Build a customer base Assemble the right people Enable them Engage and Operate Work with product development Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 30
  32. 32. Step 1: Build a customer base  Find the low hanging fruit  Deliver quick wins  Educate the organisation  Market the team  Business benefit, business benefit, business benefit… Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 31
  33. 33. Step 2: Assemble the right people Data Science Data Scientists + Tech Support + Enlightened Customer Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 32
  34. 34. Step 3: Enable your people 1 Laptops 2 Database 3 Application Servers  Laptops  Powerful  Elevated privileges  Internet access  Database  Pick good enough general analytics database  Application Servers  Internet access  Plenty of RAM  Pick a good enough general analytics language Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 33
  35. 35. Step 4: Engage and Operate  Simple Engagement model  Short sharp studies  When are we done?  What does success look like?  What Data Science doesn’t do Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 34
  36. 36. Step 4: Engage and Operate  Simple Operating model  Track your projects  Simple conventions on data  Version control  Track deliverables Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 35
  37. 37. Step 5: Work with product development  Language incompatibility  Agile incompatibilities  What’s a Data Science sprint?  Influence for Data Science features  Data Scientists have user stories too!  Influence for Data Science data  Data Scientists have user stories too! Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 36
  38. 38. Building a Data Science Capability  The strategic advantage of Data Science  finding opportunities, efficiencies and product enhancements in data  3 components  Leadership & Targets  People and Technology  Data  5 steps  Build a customer base  Gather the right people  Enable them  Engage and Operate  Work with product development Copyright Enda Ridge 2016#GuerrillaAnalytics http://guerrilla-analytics.net 37

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