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Prepping the Analytics organization for Artificial Intelligence evolution

This is a discussion document to be used at the Big Data Spain at Madrid on Nov 18th, 2016. The key takeaway from the deck is that AI is reality and much closer than we realize. It will impact our Analytics Community in a very different way vs. an average Consumer. We can shape and guide the revolution if we start preparing for it now - right from our mindset, design thinking principles and productization of Analytics (API-zation). AI is a need to address the problems of scale, speed, precision in the world that is getting more and more complex around us - it is not humanly possible to answer all the questions ourselves and we will need machines to do it for us. The flow of the story line begins with a reality check on popular misconceptions and some background on AI. It then delves into all the ways it can optimize the current flow and ends with the "Managing Innovation Playbook" a set of three steps that should guide our innovation programs - Strategy, Execution & Transformation, i.e., the principles that tell us what we want to get out of it, how to get it done and finally how much the benefits permanent and consistently improving.

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Prepping the Analytics organization for Artificial Intelligence evolution

  1. 1. Intended for Knowledge Sharing only Prepping your Analytics organization for the Artificial Intelligence era Nov 2016
  2. 2. Intended for Knowledge Sharing only Disclaimer: Participation in this summit is purely on personal basis and is not meant to represent VISA’s position on this or any other subject and in any form or matter. The talk is based on learning from work across industries and firms. Care has been taken to ensure no proprietary or work related information of any firm is used in any material.
  3. 3. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only Artificial Intelligence (AI), you say? 3
  4. 4. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only https://memegenerator.net/instance/73000475 https://imgflip.com/memegenerator/44304514/R2-D2 TWO EXTREME EMOTIONS… 4
  5. 5. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only http://www.beheadingboredom.com/hasta-la-vista-selfie/ …BUT WE MAY END UP HELPING EACH OTHER SOLVE THE BIGGEST PROBLEMS OF LIFE! 5 Selfie Stick
  6. 6. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only Popular misconceptions on AI vs. Analytics 6
  7. 7. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only https://www.pinterest.com/fuzzybear4217/robot/ https://www.cnet.com/news/samsung-teases-robotic-vacuum-cleaner-with-a-twist/ https://www.google.com/selfdrivingcar/ EMOTION|FEAR: WILL ALL OF US BE JOBLESS? 7
  8. 8. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only http://www.huffingtonpost.com/wait-but-why/the-ai-revolution-the-road-to-superintelligence_b_6648480.html FACT: SO MUCH RUNWAY IN FRONT OF US Not every problem needs an AI and AI may not be able to solve every problem… 8 Difficulty shoots up too- how to program Creativity, Common Sense, Analogy?
  9. 9. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only http://thrumyeye.deviantart.com/art/LeapFrogging-Lamb-293063465 http://data-informed.com/the-end-of-analytics/ EMOTION|GREED: LET’S LEAPFROG ANALYTICS DIRECTLY TO AI? 9
  10. 10. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only FACT: RELIABLE DATA PIPELINE & ANALYTICS ARE THE FOUNDATION FOR AI 10 DATA ANALYTICS AI
  11. 11. Intended for Knowledge Sharing only Intended for Knowledge Sharing only I PROMISE, I AIN’T MAKING STUFF UP! 11 DATA ANALYTICS AI Reliability of data feed: timely, quick, real-time (cloud refresh frequency) Privacy concerns and residence of data (local or cloud) Guard machine against getting overwhelmed with unnecessary or noisy data Guard against irrationality, alerting mechanism Data homogenization: Multiple data forms, sources, signal processing
  12. 12. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only MATURITY OF ANALYTICS NECESSARY BEFORE GRADUATION TO AI… 12 https://memegenerator.net/instance/73067076 http://www.gartner.com/it-glossary/predictive-analytics/ DATA ANALYTICS AI
  13. 13. Intended for Knowledge Sharing only Intended for Knowledge Sharing only …AND AI ISN’T ONE MONOLITHIC ENTITY EITHER 13 https://techcrunch.com/2016/06/04/artificial-intelligence-is-changing-seo-faster-than-you-think/ https://www.iconfinder.com/icons/297729/check_list_manage_plan_schedule_task_icon http://www.clipartkid.com/person-icon-cliparts/ https://www.iconfinder.com/icons/736888/cape_fly_flying_hero_super_human_super_powers_superman_icon Artificial Narrow Intelligence (ANI) “One specific task” Artificial General Intelligence (AGI) “many things like a human” Artificial Super Intelligence (ASI) “more than what a human can” Capability Terminator Movie Killer Drones Terminator Skynet Real Life Google SEO Level 5 Autonomous Cars Google Now? Examples
  14. 14. BOTTOMLINE:AI WILL FOLLOW OTHER STEPS, BUT WILL OPTIMIZE THOSE STEPS TOO Intended for Knowledge Sharing only 14 AI will not be “dumb” automation but an intelligent optimizer… • Consequence • Goals • Methodology Strategic Question ANI 1 • Processing • Platforming • Preparation Data Operations ANI 2 • Analytics • Research • Testing Insights ANI 3 • What-ifs Scenarios ANI 4 • Act • Learn • Improve Actions ANI 5 All these could feed into an “uber ANI” or AGI? from question to action
  15. 15. EMOTION|CONFUSION: IS AI CHEAP? Intended for Knowledge Sharing only 15 Artificial Intelligence is intended to optimize for cost efficiency not cost… http://weiss.photoshelter.com/image/I00002rII0wvKc3E
  16. 16. EMOTION|ASSUMPTIONS: CAN AI DO EVERYTHING? Intended for Knowledge Sharing only 16 Artificial Intelligence is a lot but not “everything for everything”… https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_research_areas.htm
  17. 17. FACT: THE SPECTRUM OF APPLICATIONS TODAY Intended for Knowledge Sharing only 17 Many big names have their skin in the game… http://eng.hi138.com/computer-papers/internet-research-papers/201511/464594_analysis-aidriven-app-gold-rush-is-coming.asp#.WCN2VfkrI2w
  18. 18. EMOTION|IGNORANCE: ARTIFICIAL INTELLIGENCE IS JUST CURVE FITTING! Intended for Knowledge Sharing only 18 https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_research_areas.htm
  19. 19. FACT: REAL DECISION MAKING NEEDS ADDITIONAL REASONING BEYOND ANALYTICS Intended for Knowledge Sharing only 19 Strategic Goals Actions Data Instrumentation Reporting Analytics Research Data Platforming A/B Testing Data Products  Focus on bigger wins  Reduced wastage  Quick fixes  Adaptability  Reasoned execution  Learning for future initiatives Analytics provides insights into “actions”, Research context on “motivations” & Testing helps verify the “tactics” in the field…
  20. 20. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only Okay, okay! Where is it really useful? 20
  21. 21. LOT OF STRENGTHS, BUT REQUIRES SYSTEM EVOLUTION & POLICY ACCEPTANCE Intended for Knowledge Sharing only 21 • Scale • Speed • Efficiency • Precision • Brutal Focus (no emotions, politics) • Tech evolution • Fit awareness (use cases) • Customer knowledge • Fuzzy Logic handling • Digital Signal ->Data Instrumentation • Regulation, privacy concerns • Globalisation capabilities • Hacking • Moral/emotional issues/Common sense/Irrationality • Investment • Sufficient data • Fixed structure • Infra Maturity: Tech, Cloud & internet • Device Intelligence bandwidth SWOT STRENGTHS WEAKNESSES THREATSOPPORTUNITIES
  22. 22. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only Interesting, so how can we leverage it? 22
  23. 23. MANAGING INNOVATION PLAYBOOK Intended for Knowledge Sharing only www.theadanswer.com www.flaticon.comwww.aetholdings.com STRATEGY EXECUTION TRANSFORMATION Source: 23
  24. 24. • AI (Narrow, General, Super) • AI as a service or a product solution STRATEGIC VISION Intended for Knowledge Sharing only 24 COMPONENTS DETAILS Goals • Expected outcome: Better, faster, cheaper or something else? • KPI: End-to-end speed, cost efficiency, ability to handle scale, have human intervention only for more complex problems Success Criteria • Stop Criteria • Learning goals Readiness Assessment • Barriers to current operating goals • Analytics Maturity Curve • Customer “adopt”-ability • Capability sizing (People-Process-Technology-Culture) Evaluation Criteria for AI use cases • Repetitiveness/portability • Need for Scale, Speed, Complex problems • Data reliability: Sufficiency, complexity, pipeline reliability, signal noise/chaos • Boundaries: Constraints, Regulations, Politics, Process issues Type of AI required
  25. 25. STRATEGIC PLANNING CHECKLIST - TEMPLATE Intended for Knowledge Sharing only 25 Sl. No. Component Details 1 The elevator pitch (Fit with Strategic Goals) “Algorithmic customer lifecycle management will improve relevance, timeliness & conversion by 10%” 2 Problem statement & estimated benefit sizing “Current data flow, algorithm dev, QA, scoring & execution has 15 steps - costly, slow, rigid & reactive. Algorithm will improve speed by 30% and improve program RoI by 50%” 3 AI-able checklist Automation or AI, Input (data size/reliability/noise), Use case(Repetitive), Tech (Cloud), Estimated Opportunity & RoI, Need (Speed, Precision, Scale), Barriers 4 Type of AI required for the use cases ANI, AGI or ASI 5 Readiness People, Process, Technology, Culture, Customer, Data 6 Stakeholder business unit Product, Marketing, Sales, Operations, Technology 7 Competitive benchmarking Can the current product suite solve with some changes? Why not alternatives? 8 SWOT analysis With future goals & vision in mind 9 Change/Integration Management Costs/Speed/Dependencies & RoI 10 Project Management Delivery & Deployment steps, Milestones, Success Criteria, RASCI assignments, Executive Sponsors, Communications Management
  26. 26. MANAGING INNOVATION PLAYBOOK Intended for Knowledge Sharing only www.theadanswer.com www.flaticon.comwww.aetholdings.com STRATEGY EXECUTION TRANSFORMATION Source: 26
  27. 27. EXECUTION Intended for Knowledge Sharing only PICK PROVE SELL • Interview: Stakeholder discussions to find out pressing questions • Evaluate: Per the checklist in the previous slide • Prioritize: Requester; Urgency; Impact (RoI); Investment • Choose “highest PR potential” problem for POC • Create action plan – methodology, technology, timelines, expected outcome template, success criteria • SWAT team – Stakeholder rep, Analyst & Technologist or Data Scientist • Check-ins & documentation of what worked and did not, do’s/don’ts, challenges & nuances • Insights communication & Impact estimation • Champion vs. Challenger measurement • Highlight victories – underdog story, winning against the odds, challenges faced, etc. • Ramp plans: hiring, cost, time, areas where it can be used • Branding – Internal, and if possible, external too, make it ‘cool’ and desirable 27
  28. 28. MANAGING INNOVATION PLAYBOOK Intended for Knowledge Sharing only www.theadanswer.com www.flaticon.comwww.aetholdings.com STRATEGY EXECUTION TRANSFORMATION Source: 28
  29. 29. CHANGE MANAGEMENT Intended for Knowledge Sharing only PEOPLETECHNOLOGY PROCESS CULTURE Difficulty Returns 29
  30. 30. CHANGE MANAGEMENT: PEOPLE & TECHNOLOGY Intended for Knowledge Sharing only Decision Focus: newer forms of scenario simulations Design Thinking: Repeatability, Portability, Modulation Advanced Programming: end-to-end compatible coding Advanced Math & Statistics (Non Linear Programming) PEOPLE TECHNOLOGY Full Suite: Data Capturing (Signal, Cookie- less), Processing, Reporting, Analytics, Testing, Research, Machine Learning & Artificial Intelligence, e.g., Google 360? Cloud Offering Real Time Internet of Everything 30
  31. 31. CHANGE MANAGEMENT: PROCESS & CULTURE Intended for Knowledge Sharing only Human-Machine-Machine Interaction Protocols: Start/Stop/Alert/Approve/Intervene Operating boundaries Regulations, privacy, governance Liability management Waterfall->Agile->CIP->?? PROCESS CULTURE Corporate culture & values: Human and machine Goal & incentive structures? Protect machines from human abuse & bias? AI performance reviews? 31
  32. 32. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only The parting words… 32
  33. 33. SUMMARY Intended for Knowledge Sharing only AI, in our daily lives, is closer than we can imagine. Our roles as both customers and analysts will evolve. Corporate Culture, Value System, Liability Management will undergo a tectonic shift in years to come. Regulations, policies and privacy considerations (cookie-free, data walled) will undergo a fresh review. Analysts will be enablers of this revolution, but need to prepare for it from today or be ready to be steam rolled. 33 Analytics will be less service and more modular product offering (API) and will be the “intelligence” layer in AI.
  34. 34. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only If all hell breaks loose? 34
  35. 35. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only http://bitterempire.com/facebook-knows-better-know/ WE HAVE THE TERMINATOR 35
  36. 36. Intended for Knowledge Sharing only Quick recap of what it is Intended for Knowledge Sharing only Appendix
  37. 37. THANK YOU! Intended for Knowledge Sharing only Would love to hear from you on any of the following forums… https://twitter.com/decisions_2_0 http://www.slideshare.net/RamkumarRavichandran https://www.youtube.com/channel/UCODSVC0WQws607clv0k8mQA/videos http://www.odbms.org/2015/01/ramkumar-ravichandran-visa/ https://www.linkedin.com/pub/ramkumar-ravichandran/10/545/67a RAMKUMAR RAVICHANDRAN
  38. 38. Intended for Knowledge Sharing only Disclaimer: Participation is purely on a personal basis and does not represent VISA,Inc. in any form or matter. The talk is based on learning from work across industries and firms. Care has been taken to ensure no proprietary or work related info of any firm is used in any material. Director, Insights at Visa, Inc. Enable Decision Making at the Executives/ Product/Marketing level via actionable insights derived from Data. RAMKUMAR RAVICHANDRAN

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