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Data empathy - A Design Thinking approach to AI application development

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This presentation covers how we, at HIVERY, apply a Design Thinking Approach to Artificial Intelligence solution development. At HIVERY we build "Data Empathy" rather than User Empathy first in order to goal big problems.

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Data empathy - A Design Thinking approach to AI application development

  1. 1. Data Empathy Design Thinking Approach to AI
  2. 2. Who is...
  3. 3. ●  With small margins and retailers continually expecting more from their suppliers, the CPG/FMCG sector has never been more competitive. ●  The race for optimal shelf space is the most challenging problem in global consumer retail industry, costing suppliers and retailers $400 billion* in lost sales per year. ●  Outlets are grouped by location, size or other arbitrary measures using “human constructs”, typically at “cluster” level. ●  We have developed unique IP in AI & optimisation to provide “fingerprint recipes” at a per product SKU, per outlet level. Providing “machine constructs” view. Commercial In Confidence | Page 3*Source AI-powered, big data manages critical retail efficiency in real-time The problem is simple...
  4. 4. ....there are many companies who have this problem We help these companies... ….get the right mix in these outlets Commercial In Confidence | Page 4
  5. 5. We build tools that augment our customers thinking in new ways never possible before Commercial In Confidence | Page 5 Just as in pathology, we see AI+pathologist together make superior prediction. We are doing the same in retail with our product, space, price and promotion recommendations for our CGP/FMCG customers. Source: https://blogs.nvidia.com/blog/2016/09/19/deep-learning-breast-cancer- diagnosis/
  6. 6. Confidential & Privileged | Page 6 Recap on the Design Thinking approach STEP MISSING! Design principle to Ideate using Affinity Diagram/ Process Diagram
  7. 7. Confidential & Privileged | Page 7 Things like this... Process DiagramAffinity Diagram
  8. 8. Confidential & Privileged | Page 8 Design Thinking approach is actually used in HIVERY’s product development methodology. It’s part of our DNA VS
  9. 9. Confidential & Privileged | Page 9 Discovery is all about building empathy and defining the problem VS
  10. 10. Confidential & Privileged | Page 10 While in Experiment, it’s about developing the model; training, refining and testing it VS
  11. 11. Confidential & Privileged | Page 11 And in Deployment; it’s about getting the enterprise ready for new way of operating...
  12. 12. Confidential & Privileged | Page 12 But, I want to focus on this one first...
  13. 13. Confidential & Privileged | Page 13 There are tools to help us build user empathy...like this Persona Empathy Mapping Source: https://www.cooper.com/journal/2014/05/persona-empathy-mapping
  14. 14. Confidential & Privileged | Page 14 These tools help us gain empathy towards the segment we are trying to solve. Empathy through… Source: https://www.cooper.com/journal/2014/05/persona-empathy-mapping
  15. 15. Confidential & Privileged | Page 15 At HIVERY, we empathize with data not humans (at least initially). If we can't build the engine, no point building the car.
  16. 16. Confidential & Privileged | Page 16 In essence, we use a “Data” Empathy Mapping framework Persona Empathy Mapping: Think, Feel, and Do Data Empathy Mapping: Goals, Data, and Rules Rules:
  17. 17. Confidential & Privileged | Page 17 Data Empathy is about... DATA: RULES: GOALS: … gaining an understanding of how the data travels throughout the organisation...
  18. 18. Confidential & Privileged | Page 18 DATA: RULES: GOALS: ...how it is used, what system and processes support it; what actions are derived from it. Data Empathy is about...
  19. 19. Confidential & Privileged | Page 19 Let's go deeper... ...break down...
  20. 20. Confidential & Privileged | Page 20 What happens in “Goals” ●  Define the problem and AI goals (eg Japan water) ●  This provide team focus ●  Need to distinguish “automation problems” (i.e. human intensive 7000 planograms or 1 hr to optimize 1 vending machine) and “learning problems” (i.e. make actionable recommendations at outlet/shelf/store/SKU level). ●  Examples of good machine learning problems include predicting the likelihood that a certain type of user will click on a certain kind of ad or in our case, what predicting the likelihood that a certain type of product will sell in a specific outlet (vending machine/store) ●  Need to be clear if we are (or both) ○  creating machines that can automate work ○  obtaining insights into similarities & differences
  21. 21. Confidential & Privileged | Page 21 What happens in “Data” ●  Once we verify our customer’s problem and goals for machine learning application; ●  The next step is to evaluate whether we have the right data to train and solve it ●  Understanding data means: ○  Determine system sources (ie legacy systems) ○  How good is the data quality (ie integrity) ○  How good is the data quantity (ie at least 12 months?) ○  How good is the ongoing data streams/flows?
  22. 22. Confidential & Privileged | Page 22 What happens in “Rules” ●  Rules are important but often not considered business constraints but need to be design into the algorithmic model(s). ○  “Google Maps, A to B and avoid tolls” ○  “No coke in vending machines at schools” ●  Business constraints allow enterprises to adopt and operations AI recommendations ●  In the future, a properly designed goal achieving AI model allows humans to challenge assumptions via "What if" scenario as ML predicts impact of your assumption inclusion or exclusion.
  23. 23. Confidential & Privileged | Page 23 What happens in “Situation” ●  This gives clarity over: ○  the problem ○  the goals of what needs to be achieved ○  the opportunity/challenges (ie data is poor (eg China vending machines) ○  possible direction ○  Develop formalities: ●  stakeholder engagement, ●  project team, ●  sponsor, ●  communication plan, ●  work plans, ●  SoW etc
  24. 24. Confidential & Privileged | Page 24 ...Ideate, Prototype and Test in the Experiment Phase while informing Deployment Phase
  25. 25. Confidential & Privileged | Page 25 Experiment is about... ●  Forming and agreeing on a hypothesis ●  Formulating bulletproof experiment designs ●  Visualizing data insights/opportunities (eg Japan) ●  Refining the algorithmic models parameters as it learns (training test) ●  Validating (ie validating set) and iterating the model’s predictions/ recommendations both from a business value and operationalisation perspectives ●  Start thinking about possible MVP - the “car design”
  26. 26. Confidential & Privileged | Page 26 And lastly Deployment is about getting the enterprise ready for new way of operating...
  27. 27. Confidential & Privileged | Page 27 Deployment is about operationalising & project management ●  From Design Thinking methodology to Project Management methodology ●  Ensuring enterprise adoption of AI ●  Transition from MVP to Beta in an agile manner ●  Formulating the plans around change management and operationalization strategies ●  Agreeing on the ongoing commercial model
  28. 28. Confidential & Privileged | Page 28 Let's chat about two real examples ...break down...
  29. 29. Confidential & Privileged | Page 29 Example: Vending Analytics journey Does it work? Can we optimise a vending machine better? 60 machines experiment in Newcastle Make it smarter? Data scientists and client validation of model and understanding constraints Remove the pain? Understand what is critical to build to interact with model Build MVP Build MVP (else they still see a spreadsheet) Remove pain point
  30. 30. Confidential & Privileged | Page 30 Example: Promotional Effectiveness Data scientists and client validation of model and understanding constraints Make it smarter? Understand what model interacts are required by user for operationalising Remove the pain? Build MVP to learn, refine and build features based on validated learning Build MVP Can we use ML to predict future demand of a product? If so, how accurate? Better than human? Does it work?
  31. 31. The approach applied to our category management toolg Commercial in Confidence| Page 31 Does it work? Validate results Develop MVP Can we optimize 60 feet of shelf space and make it look pretty at the same time? Understand data, workflows, and operations. Validate assumptions. Make it smarter Co-design with strategic partner and build MVP. Conduct experiment to validate results and proof business case.
  32. 32. DATA HAS A BETTER IDEA Commercial In Confidence | Page 32 TM

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