Prepping the Analytics organization for Artificial Intelligence evolution

Intended for Knowledge Sharing only
Prepping your Analytics
organization for the
Artificial Intelligence era
Nov 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?
3

https://memegenerator.net/instance/73000475
https://imgflip.com/memegenerator/44304514/R2-D2
TWO EXTREME EMOTIONS…
4

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

Popular misconceptions on AI vs. Analytics
6

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

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?

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

FACT: RELIABLE DATA PIPELINE & ANALYTICS ARE THE FOUNDATION FOR AI
10
DATA ANALYTICS AI

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

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

…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

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

EMOTION|CONFUSION: IS AI CHEAP?
Artificial Intelligence is intended to optimize for cost efficiency not cost…
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

EMOTION|IGNORANCE: ARTIFICIAL INTELLIGENCE IS JUST CURVE FITTING!
https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_research_areas.htm

FACT: REAL DECISION MAKING NEEDS ADDITIONAL REASONING BEYOND ANALYTICS
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…

Okay, okay! Where is it really useful?
20

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
THREATSOPPORTUNITIES

Interesting, so how can we leverage it?
22

MANAGING INNOVATION PLAYBOOK
www.theadanswer.com www.flaticon.comwww.aetholdings.com
STRATEGY EXECUTION TRANSFORMATION
Source:
23

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

STRATEGIC PLANNING CHECKLIST - TEMPLATE
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

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
27

Source:
28

CHANGE MANAGEMENT
PEOPLETECHNOLOGY
PROCESS CULTURE
Difficulty
Returns 29

CHANGE MANAGEMENT: PEOPLE & TECHNOLOGY
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

CHANGE MANAGEMENT: PROCESS & CULTURE
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

The parting words…
32

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.
33
Analytics will be less service and more modular product offering (API) and
will be the “intelligence” layer in AI.

If all hell breaks loose?
34

http://bitterempire.com/facebook-knows-better-know/
WE HAVE THE TERMINATOR
35

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

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

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