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Prepping your Analytics organization for the Artificial Intelligence eraNov 2016
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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.
Intended for Knowledge Sharing only
Quick recap of what it is
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Artificial Intelligence (AI), you say?
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Quick recap of what it is
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https://memegenerator.net/instance/73000475https://imgflip.com/memegenerator/44304514/R2-D2
TWO EXTREME EMOTIONS…
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Quick recap of what it is
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…BUT WE MAY END UP HELPING EACH OTHER SOLVE THE BIGGEST PROBLEMS OF LIFE!
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Selfie Stick
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Quick recap of what it is
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Popular misconceptions on AI vs. Analytics
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Quick recap of what it is
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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?
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Quick recap of what it is
Intended for Knowledge Sharing onlyhttp://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…
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Difficulty shoots up too- how to
program Creativity,
Common Sense, Analogy?
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Quick recap of what it is
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http://thrumyeye.deviantart.com/art/LeapFrogging-Lamb-293063465http://data-informed.com/the-end-of-analytics/
EMOTION|GREED: LET’S LEAPFROG ANALYTICS DIRECTLY TO AI?
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Quick recap of what it is
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FACT: RELIABLE DATA PIPELINE & ANALYTICS ARE THE FOUNDATION FOR AI
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DATA ANALYTICS AI
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I PROMISE, I AIN’T MAKING STUFF UP!
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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
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Quick recap of what it is
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MATURITY OF ANALYTICS NECESSARY BEFORE GRADUATION TO AI…
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https://memegenerator.net/instance/73067076http://www.gartner.com/it-glossary/predictive-analytics/
DATA ANALYTICS AI
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…AND AI ISN’T ONE MONOLITHIC ENTITY EITHER
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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_iconhttp://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 SEOLevel 5
Autonomous Cars
Google Now?Exam
ples
BOTTOMLINE:AI WILL FOLLOW OTHER STEPS, BUT WILL OPTIMIZE THOSE STEPS TOO
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AI will not be “dumb” automation but an intelligent optimizer…
• Consequence
• Goals• Methodolo
gy
Strategic Question
ANI 1
• Processing• Platformin
g• Preparatio
n
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
EMOTION|CONFUSION: IS AI CHEAP?
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Artificial Intelligence is intended to optimize for cost efficiency not cost…
http://weiss.photoshelter.com/image/I00002rII0wvKc3E
EMOTION|ASSUMPTIONS: CAN AI DO EVERYTHING?
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Artificial Intelligence is a lot but not “everything for everything”…
https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_research_areas.htm
FACT: THE SPECTRUM OF APPLICATIONS TODAY
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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
EMOTION|IGNORANCE: ARTIFICIAL INTELLIGENCE IS JUST CURVE FITTING!
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https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_research_areas.htm
FACT: REAL DECISION MAKING NEEDS ADDITIONAL REASONING BEYOND ANALYTICS
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Strategic
GoalsActions
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…
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Quick recap of what it is
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Okay, okay! Where is it really useful?
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LOT OF STRENGTHS, BUT REQUIRES SYSTEM EVOLUTION & POLICY ACCEPTANCE
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•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
THREATS OPPORTUNITIES
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Quick recap of what it is
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Interesting, so how can we leverage it?
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MANAGING INNOVATION PLAYBOOK
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STRATEGY EXECUTION TRANSFORMATION
Source:23
• AI (Narrow, General, Super) • AI as a service or a product solution
STRATEGIC VISION
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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
STRATEGIC PLANNING CHECKLIST - TEMPLATE
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Sl. No. Component Details
1The elevator pitch (Fit with Strategic
Goals) “Algorithmic customer lifecycle management will improve relevance,
timeliness & conversion by 10%”
2Problem 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 checklistAutomation 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
MANAGING INNOVATION PLAYBOOK
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STRATEGY EXECUTION TRANSFORMATION
Source:26
EXECUTION
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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
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MANAGING INNOVATION PLAYBOOK
Intended for Knowledge Sharing onlywww.theadanswer.com www.flaticon.comwww.aetholdings.com
STRATEGY EXECUTION TRANSFORMATION
Source:28
CHANGE MANAGEMENT
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PEOPLETECHNOLOGY
PROCESS CULTURED
ifficu
lty
Returns 29
CHANGE MANAGEMENT: PEOPLE & TECHNOLOGY
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Decision Focus: newer forms of scenario simulationsDesign Thinking: Repeatability, Portability, ModulationAdvanced Programming: end-to-end compatible codingAdvanced 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 OfferingReal TimeInternet of Everything
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CHANGE MANAGEMENT: PROCESS & CULTURE
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Human-Machine-Machine Interaction Protocols: Start/Stop/Alert/Approve/InterveneOperating boundariesRegulations, privacy, governanceLiability managementWaterfall->Agile->CIP->??
PROCESS
CULTURE Corporate culture & values: Human and machineGoal & incentive structures?Protect machines from human abuse & bias?AI performance reviews?
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Quick recap of what it is
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The parting words…
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SUMMARY
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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.
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Analytics will be less service and more modular product offering (API) and will be the “intelligence” layer in AI.
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Quick recap of what it is
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If all hell breaks loose?
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Quick recap of what it is
Intended for Knowledge Sharing onlyhttp://bitterempire.com/facebook-knows-better-know/
WE HAVE THE TERMINATOR
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Quick recap of what it is
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Appendix
THANK YOU!
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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
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