Advancing the analytics maturity curve at your organization

Intended for Knowledge Sharing only
Advancing the Analytics Maturity in
your organization
Sep 2017

> México | Ciudad de México
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.
Director, Insights at Visa, Inc.
Enable Decision Making at the
Executives/ Product/Marketing level via
actionable insights derived from Data.
RAMKUMAR RAVICHANDRAN

Do these memes ring a bell?

IGNORANCE: I GOT DATA SCIENTISTS FOR PREDICTIONS, JUST GIVE ME MY REPORTS!

IDENTITY CRISIS: REPORTING, DATA ANALYSES, STATISTICS, ALGORITHMS, TESTS???

ACCOUNTABILITY: DATA BAD, ANALYTICS SCREWED UP! NUMBERS UP, “WE” DID IT!

BUDGET 101: COST SHOULD BE KEPT LOW & REVENUE SHOULD BE INCREASED!

AND YEAH - LET’S FIRE ANALYSTS AND GET ARTIFICIAL INTELLIGENCE!!!

Analytics is the bellweather for an organization

ANALYTICS IS A LIVING, BREATHING & GROWING ORGANISM…
https://www.intel.com/content/dam/www/public/us/en/documents/guides/analytics-planning-guide.pdf

A TYPICAL EXAMPLE OF A “MATURE” ANALYTICS ORGANIZATION…
From Business Operations side
• Every major decision has been quantified for
impact (expected incremental over run-rate),
supported with context (user demand),
validated in-market and any historical
precedent.
• Optimal paths planned out for next ‘x’ moves
– Leading indicators monitored and response
paths worked out (Stop/Scale/Change).
• Delivery model for insights and Response
model customized for stakeholders.
• Agility of the business to respond to industry
events, competitor actions, customer
demands highest, since the business drivers
and ownership have been modulated and
multi variate tested & mapped - Pivoting, new
Product/UX, Pricing, campaigns.
• A knowledge bank/idea marketplace for
employees to quickly prototype, iterate and
innovate at scale.
From the Analysts side
• Analytics is a “Strategy” function not just
Support or Product - everyone knows the
“why” and “what will happen” and “what if
we don’t”.
• A driver of Organizational Culture –
Accountability/Transparency,
Collaboration/Assistance, Innovation/Change
Management.
• Doesn’t need to justify investment in
learning, but everyone knows why Analytics is
in their self-interest!
• Analytics is so fine-tuned that it can be
packaged and sold out as a Product to the
industry, e.g., Hive/Google 360!
• Cognitive ready

Interesting, but can you pull it off really?

CHANGE MANAGEMENT PLAYBOOK|STRATEGY
www.theadanswer.com www.flaticon.comwww.aetholdings.com
STRATEGY EXECUTION TRANSFORMATION
Source:

FIRST STEP IS TO UNDERSTAND, WHY & WHAT WILL END STATE LOOK LIKE…
• Cognitive Function
• Productization/Monetization
• Brand Capital
COMPONENTS DETAILS
Goals
• Expected outcome: Business Agility, Strategic, Engagement
• KPI: End-to-end speed, cost efficiency, ability to handle scale,
Stakeholder NPS
Success Criteria • Time bound, Program RoI, NPS (Transitionary), Direct Revenue
Readiness
Assessment
• Baselining the current maturity level
• Biggest bottlenecks across BUs
• Customer “adopt”-ability
• Capability sizing (People-Process-Technology-Culture)
Evaluation Criteria
for Use cases/BU
• Data reliability: Sufficiency, complexity, pipeline reliability,
signal noise/chaos
• Brand Value of Partner BU (Executive Buy-in)
• Need: Repetitiveness/portability, Scale, Speed, Complexity?
• Speed & Resources
• Cognitive readiness
End State

…FOLLOWED BY A QUICK AUDIT EXERCISE
Sl. No. Component Details
1 Estimated benefit sizing
“x% strategic decisions based on run-rates and caused us to delay the launch of a major
business line. By launching it earlier, we can increase Revenue by 10%”
2 Problem Statement
“Different BUs use different measurement framework, data repositories and lack of
understanding of system dynamics causes us to repeat ideas that had failed in the past”
3 Analytics Audit
Input (data size/reliability/noise), Need(Automation, Prediction, Prescription, AI), Tech
Stack, Estimated Opportunity missed, Engagement model, Delivery Framework
4 Partner BU Nirvana Automation, Self Serve, Cognitive, Productization, Branding
5 Partner BU Readiness People, Process, Technology, Culture, Strategic Vision
6 Support Executive, Leadership, Line Managers
7 Competitive benchmarking Can the current set-up work with some changes? Does it need transition?
8 What if we don’t? If we let it be way it is, does it impact big picture by much?
9
Change/Integration
Management
Costs/Speed/Dependencies & RoI
10 Project Management
Delivery & Deployment steps, Milestones, Success Criteria, RASCI assignments, Executive
Sponsors, Communications Management

…TO COME UP WITH AN ACTION PLAN TO PROVE BENEFIT OF UPLEVELING
PICK
✓ 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
PROVE
✓ Create action plan – methodology, technology, timelines, expected outcome
template, success criteria
✓ SWAT team – Partner BU rep, Analyst & Technologist
✓ Check-ins & documentation of what worked and did not, do’s/don’ts, challenges
& nuances and their workarounds
✓ Insights communication & Impact estimation
✓ Champion vs. Challenger measurement
SELL
✓ Highlight victories and call out incremental benefits
✓ Ramp plans: hiring, cost, time, other areas where it can be used
✓ Branding – Internal, and if possible, external too, make it ‘cool’ and desirable

CHANGE MANAGEMENT PLAYBOOK|EXECUTION
Source:

ANALYTICS VALUE CHAIN: STRATEGY DRIVES EVERY INITIATIVE & ANALYTICS
MEASURES ITS EFFECTIVENESS!
Analytics provides insights into “actions”, Research context on “motivations” & Testing
helps verify the “tactics” in the field and everything has to be productized…
Strategy
Data
Tagging
Data
Platform
Reporting
Analytics
Research
Cognitive
Iterative
Loop Key benefits
Focus on Big Wins
Reduced Wastage
Quick Fixes
Adaptability
Assured execution
Learning for future
initiatives
Optimization

…WITH PROGRESSIVE SOPHISTICATION, THE COMPLEXITY OF QUESTIONS & IMPACT
OF ANALYTICS SHOULD BE BIGGER & MORE INTEGRAL TO THE ORGANIZATION…
60%
20%
10%
5% 5%
20%
30%
15%
10%
5%
20%
25%
25%
25%
20%
25%
25%
20%
15%
25%
20%
20%
20%
20%
15%
YEAR 1 YEAR 2 YEAR 3 YEAR 4 YEAR 5
Primary source of insights for Decision Making along the Analytics
Maturity Curve
Reporting Data Analytics User Research A/B Testing Advanced Analytics/Machine Learning Data Products Cognitive Analytics
ILLUSTRATIVE

…& PROGRESSIVELY MORE VALUABLE MILESTONES BE MADE POSSIBLE
Dimensions Year 1 Year 2 Year 3 Year 4 Year 5
Major
deliverable
indicating
success at
this phase
Foundational
Data Lake
• Operational Analytics, i.e.,
Automated workbooks for
analytics (Self Serve)
• Test & Learn Process and
Framework
• Predictive Model for
KPIs and consequent
simulations (prescriptive
recommendations)
• Outcome Focused
Framework for Portfolio
Management
• Early Data Science
Platform work
• Data Science
Platform
• Cross integration of
Analytics, Research
and Testing at App
Layer (API)
• Streaming Analytics
• Cognitive User
Facing Product
• Monetizeable
products
Learning
objectives
met
• Data knowledge
Repository: Metrics,
lineage, governance
• Strategic KPIs and their
definitions
• What worked vs. not:
Initiatives impact, Testing
results/learning
• Engagement Model,
delivery framework and
who to go to
• Champion Challengers
• Experimentation Results
• Driver relationships
• Data Driven Design
• Innovation Scaling/
Strategy Testing
• System Dynamics
• Champion-
Challenger on Data
Products and
Cognitive solutions
• Customer
receptiveness
Business
objectives
met
• Benchmarking insights:
Top Movers & Shakers,
Segments, Conversion
Funnel, User Journey,
Engagement Trends,
NPS, Brand Awareness,
Customer Influencers,
Risk, Platform
Performance Metrics
• High level Drivers
identification: Growth
segments/levers, Causation
vs. Correlation, Cohort
Maturity Curve, Value
Migration Matrix, Networks
& Influencers, Leading
Indicators, Risk factors
• Data Driven (Analytics,
Research & Testing) impact
• Conversion Rate
Optimization
• Lifecycle Management
• Growth Hacking
• Targeted Campaigns
• Influencer Growth
• Adaptive Models
adoption across
Business Units
• Modular Self Serve
Framework for
Business
• Impact from
Streaming Analytics
• Additional source
of Revenue from
Analytics
• Scalability cost
reduction from AI
• Customer NPS
improvement from
AI
Analytics KPI
Speed of insights,
%availability of reports,
interactive dashboards
Progressive Operational KPIs
(YoY), Success rate from
Testing Program, Business
Impact measurement
Progressive Operational KPIs, Business KPI impact, Stakeholder NPS,
Employee Engagement, Progressive high Returns per Analytics team
member
ILLUSTRATIVE

CHANGE MANAGEMENT PLAYBOOK|TRANSFORMATION
Source:

FLIP THE PRODUCT DEVELOPMENT FLOW WITHIN ANALYTICS FUNCTION…
5
Change Analytics
delivery model
from
“Software
Development”
to…
• (70%)
Stakeholders
requests what is
needed and how
• (30%) Analytics
interviews on
numbers/insights
Performs Analytics,
Create Reports
UAT with
stakeholders
Stakeholders sign-
offs
…to a Product
Development
flow!
• “Persona”
interviews
• Need
Identification
(speed, detail,
frequency, where,
visuals/numbers)
• Analytics
savviness
• Possible Tradeoffs
• Affluence/
Influence
• Customer Rating
• POC – Output
Delivery System,
Engagement Model,
etc.
• Identify fit,
Satisfaction,
• Business need met
• Iterate
• Impact, Usage
• Drop-offs, Funnel,
Repeat Usage
• Platform
performance
• Next Best Products
• Premium Support
• Case Studies
• Optimize & up-level
offering
• Brownbags
• Productize/
Cognitive
IDEATION DESIGN ROLLOUT GROW
…and switch between Agile, Kanban, Continuous based on type of project

…& PAIR UP WITH BEST PRACTICES TO CREATE A SUSTAINABLE TRANSFORMATION
PEOPLE
• Embed Analytics Maturity Curve Graduation as an Analytics Leadership KPI
• Analytics Maturity Curve Roadmap & Job Family foundational documents
• Design Thinking Focused
• Cross Functional ownership
• 101 Trainings – Marketing, Product, Sales
PROCESS
• Borrow best practices from TPM world: Agile, Kanban and Continuous Delivery
• Iterative Learning & Co-development of Analytics
• Documentation: Impact Dashboards, Project Tracker, Knowledge Discovery
Portal, Brand Home Page (Whitepapers, case studies, brownbag),
• Formalized Customer Feedback Channel and Management Strategy
• SMART Goals
TECH
• Data Science Platforms: Data Pipeline/ETL (e.g., UNIFI), Model development,
deployment (Data Science Workbench) and Post Deployment Monitoring,
Management and API framework (Thinkdeep)
• Project Management Trackers
CULTURE
• Business Enablement
• Customer Needs Focused
• Entrepreneurial

Why this, Why now, why here?

BIGGER TRENDS THAT ARE SHAKING UP THE ANALYTICS WORLD FROM INSIDE OUT…
Demand Pressures: Complexity and nature of problems and their solutions,
type of audience & consumption framework evolving
Monetization opportunities- Direct, Indirect, Recurring
Artificial Intelligence, IoE and “Smart”ening of devices/systems faster than
expected.
Evolution of input data sources and integration of multiple insights sources
into decision making (A/B Testing, Research, Predictions/Scores from other
models)
Evolution from Service to Product to Platform (Build Once, Use Everywhere
APIs)
…and without Analytics Maturity Curve progression as top priority, we will lose out!

The parting words…

KEY TAKEAWAYS
Analytics is a bellweather of business maturity. More advanced the
analytics function, more aware and agile is the organization.
Insights (Analytics, Research & Testing) is a Strategy function that should
drive execution and business transformation.
Mature organization have realized that lack of “real” analytics education
drives low engagement and under-utilization. This led to creation of
education programs to best realize full potential of data assets.
Mature organizations with analytics within the DNA are poised to reap
benefits of Data Productization, Incremental Monetization, Industry
beating performance and Artificial Intelligence revolution.
Analytics leadership should chart out the Progression plan for the
organization, lay out milestones/timelines, expected impact and keep the
leadership focused on this transition. This is needed to help them
understand the need for continuous investment and nurturing.

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

Advancing the analytics maturity curve at your organization

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