prepping the analytics organization for artificial intelligence evolution

Intended for Knowledge Sharing only

Prepping your Analytics organization for the Artificial Intelligence eraNov 2016


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.


Quick recap of what it is


Artificial Intelligence (AI), you say?

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https://memegenerator.net/instance/73000475https://imgflip.com/memegenerator/44304514/R2-D2

TWO EXTREME EMOTIONS…

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https://memegenerator.net/instance/73000475

https://imgflip.com/memegenerator/44304514/R2-D2



Intended for Knowledge Sharing onlyhttp://www.beheadingboredom.com/hasta-la-vista-selfie/

…BUT WE MAY END UP HELPING EACH OTHER SOLVE THE BIGGEST PROBLEMS OF LIFE!

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Selfie Stick

http://www.beheadingboredom.com/hasta-la-vista-selfie/




Popular misconceptions on AI vs. Analytics

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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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https://www.pinterest.com/fuzzybear4217/robot/

https://www.cnet.com/news/samsung-teases-robotic-vacuum-cleaner-with-a-twist/

https://www.google.com/selfdrivingcar/



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?

http://www.huffingtonpost.com/wait-but-why/the-ai-revolution-the-road-to-superintelligence_b_6648480.html




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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http://thrumyeye.deviantart.com/art/LeapFrogging-Lamb-293063465

http://data-informed.com/the-end-of-analytics/




FACT: RELIABLE DATA PIPELINE & ANALYTICS ARE THE FOUNDATION FOR AI

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DATA ANALYTICS AI

Intended for Knowledge Sharing onlyIntended for Knowledge Sharing only

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




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

https://memegenerator.net/instance/73067076

http://www.gartner.com/it-glossary/predictive-analytics/

Intended for Knowledge Sharing onlyIntended for Knowledge Sharing only

…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

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

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• 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?


Artificial Intelligence is intended to optimize for cost efficiency not cost…

http://weiss.photoshelter.com/image/I00002rII0wvKc3E

http://weiss.photoshelter.com/image/I00002rII0wvKc3E

EMOTION|ASSUMPTIONS: CAN AI DO EVERYTHING?


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


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

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!




FACT: REAL DECISION MAKING NEEDS ADDITIONAL REASONING BEYOND ANALYTICS


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…




Okay, okay! Where is it really useful?

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LOT OF STRENGTHS, BUT REQUIRES SYSTEM EVOLUTION & POLICY ACCEPTANCE


•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




Interesting, so how can we leverage it?

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MANAGING INNOVATION PLAYBOOK

Intended for Knowledge Sharing onlywww.theadanswer.com www.flaticon.comwww.aetholdings.com

STRATEGY EXECUTION TRANSFORMATION

Source:23

http://www.theadanswer.com/services/branding/

http://www.flaticon.com/free-icons/road_2

https://www.aetholdings.com/transformation/

• AI (Narrow, General, Super) • AI as a service or a product solution

STRATEGIC VISION


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


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




Source:26




EXECUTION


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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Source:28




CHANGE MANAGEMENT


PEOPLETECHNOLOGY

PROCESS CULTURED

ifficu

lty

Returns 29

CHANGE MANAGEMENT: PEOPLE & TECHNOLOGY


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


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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The parting words…

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SUMMARY


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.




If all hell breaks loose?

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Intended for Knowledge Sharing onlyhttp://bitterempire.com/facebook-knows-better-know/

WE HAVE THE TERMINATOR

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http://bitterempire.com/facebook-knows-better-know/




Appendix

THANK YOU!


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

https://twitter.com/decisions_2_0











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

prepping the analytics organization for artificial intelligence evolution

Data & Analytics