Actionability of insights
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Transcript of Actionability of insights
Intended for Knowledge Sharing only
Actionability of Insights
Text Analytics Summit
Text Analytics Summit | June 2015
Intended for Knowledge Sharing only
Disclaimer: Participation in this summit is purely on personal basis and not representing VISA in any form or matter. The talk is based on learnings 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.
Help Executives/Product/Marketing
with actionable insights
RAMKUMAR RAVICHANDRAN
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Quick recap of what is it?Quick recap of what it is
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What makes an insight actionable?
WHAT IS IT AFTER ALL??
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Specific answer to the question
Easy to understand
Timely & available (whenever, wherever & however needed)
Trustworthy & reliable
Scalable & Repeatable
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Quick recap of what is it?Quick recap of what it is
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Seems easy enough?
SEEMS EASY, SO WHERE IS THE PROBLEM?
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Sky high expectations (Data Scientists the new “Alchemists”)
Philosophical differences
Vague Questions/Undefined
Constraints (Data, Time, Resources unavailable)
Heartbreak Syndrome “not what I expected you know, answer”
9
CUSTOMER EDUCATION ON MULTIPLE VALUE PROP WOULD HELP
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Size behaviors
with KPIs and high level drilldowns (Sizing)
InformInvestiga
tePredict Optimize Mine
Root cause analysis:
Hypotheses testing via
data drilldowns (Business Analytics)
Determine Causal
relationships (Advanced Analytics)
Experiments on options to verify which one works
(A/B Testing)
Automated relationship
discovery and Data Products
(Machine Learning)
10
SEEMS EASY, SO WHERE IS THE PROBLEM?
Intended for Knowledge Sharing only
Sky high expectations (Data Scientists the new “Alchemists”)
Philosophical differences
Vague Questions/Undefined
Constraints (Data, Time, Resources unavailable)
Heartbreak Syndrome “not what I expected you know, answer”
PHILOSOPHICAL? WHAT DO YA MEAN?
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"Analysts are from Mars and their customers are from Venus…
ANALYSTS CUSTOMERS
Language they speak
Numbers, graphs, lifts, accuracy, insights
If we do this, then we get this...
PHILOSOPHICAL? WHAT DO YA MEAN?
Intended for Knowledge Sharing only
Analysts are from Mars and their customers are from Venus…
ANALYSTS CUSTOMERS
Language they speak
Numbers, graphs, lifts, accuracy, insights
If we do this, then we get this...
What excites them Brilliance of approach Simplicity of the answer
PHILOSOPHICAL? WHAT DO YA MEAN?
Intended for Knowledge Sharing only
Analysts are from Mars and their customers are from Venus…
ANALYSTS CUSTOMERS
Language they speak
Numbers, graphs, lifts, accuracy, insights
If we do this, then we get this...
What excites them Brilliance of approach Simplicity of the answer
How they think Detail oriented Big picture
PHILOSOPHICAL? WHAT DO YA MEAN?
Intended for Knowledge Sharing only
Analysts are from Mars and their customers are from Venus…
ANALYSTS CUSTOMERS
Language they speak
Numbers, graphs, lifts, accuracy, insights
If we do this, then we get this...
What excites them Brilliance of approach Simplicity of the answer
How they think Detail oriented Big picture
What they can compromise on Time for accuracy Perfection for timely
action
PHILOSOPHICAL? WHAT DO YA MEAN?
Intended for Knowledge Sharing only
Analysts are from Mars and their customers are from Venus…
ANALYSTS CUSTOMERS
Language they speak
Numbers, graphs, lifts, accuracy, insights
If we do this, then we get this...
What excites them Brilliance of approach Simplicity of the answer
How they think Detail oriented Big picture
What they can compromise on Time for accuracy Perfection for timely
action
Biggest difference Scientists who deal with facts Artists who deal with gut
SEEMS EASY, SO WHERE IS THE PROBLEM?
Intended for Knowledge Sharing only
Sky high expectations (Data Scientists the new “Alchemists”)
Philosophical differences
Vague Questions/Undefined
Constraints (Data, Time, Resources unavailable)
Heartbreak Syndrome “not what I expected you know, answer”
SEEMS EASY, SO WHERE IS THE PROBLEM?
Intended for Knowledge Sharing only
Sky high expectations (Data Scientists the new “Alchemists”)
Philosophical differences
Vague Questions/Undefined
Constraints (Data, Time, Resources unavailable)
Heartbreak Syndrome “not what I expected you know, answer”
HOW TO MANAGE CONSTRAINTS
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•Tactical Prioritization: Classify the requests into “Firefights, Urgent and important, Important but not urgent, Good to have” based on factors like Requestor, Urgency, Impact and availability of resources solve it.
•Pre-Analysis work: Strategic prioritization (Outcome Focused), Gap analysis on data/proxy, Various approaches and the Sizing of ETA for each, final output templates.
•Expectations setting: Discuss with requestors, the output from Pre-Analysis and decide together on next steps. Set up Milestones/regular check-ins.
•Execution, Communication, Fine tuning & Course-correction (if necessary)
•Automate if necessary
STRATEGIC PRIORITIZATION (ILLUSTRATIVE)
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Sl. No.
Ask Why is it needed? How will it be used?
Fit with the high level business Strategy/KPI
impactedPlan B?
1What are the Consumers
saying?
Redesigned the website and need
to know the customer reaction
Roll back/ Ramp up based on feedback
Customer Satisfaction & Engagement via better website UX
A/B Test findings only
2Thematic
Extraction of Tweets
Investigate why Site Engagement
(#Page Views/Visit) down WoW
Feedback will inform where issues were and need to
be addressed
Maintain Product uptime for the users
Pathing Analysis/ Heatmap
Analysis only
OUTPUT CUSTOMIZED TO CONTEXT OF THE USER (NEED, TIME, MINDSET)
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“In mail”
Recommendations with supporting
graphs, tables, etc.
“Story Deck”
Full deck with the pitch and supporting arguments, numbers,
graphs, charts
“On-the-go”
-Mobile App, On the Cloud,
Subscriptions-Reports,
Dashboards, Infographics
Algorithm/Model
Ready to be deployed
How to decide? Customer needs; Turnaround Speed;
One time/reuse; Deployment on Front end; Strategic Doc;
Quick read/research doc
CUSTOMER DRIVEN ANALYTICS
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Pre-work & Kickoff1 Analyst, Customer
Translation to Analytical Framework2 Analyst, Researcher, Data Instrumentation, & Data Manager,
Developer, Data Scientist
Data Collection and Preparation3 Analyst, Data Manager, Data Scientist
Analysis, Validation & Verification4 Analyst, Data Scientist, Customer and SME, Researcher
Actionable insights and impact sizing5 Analyst, Customer, Leader
A/B Testing6 Analyst, A/B Testing, Customer, Developer
Rollouts7 Customer, Leadership & Executives
ResponsibleSteps
SEEMS EASY, SO WHERE IS THE PROBLEM?
Intended for Knowledge Sharing only
Sky high expectations (Data Scientists the new “Alchemists”)
Philosophical differences
Vague Questions/Undefined
Constraints (Data, Time, Resources unavailable)
Heartbreak Syndrome “not what I expected you know, answer”
FINALLY SOMETHING ON TEXT ANALYTICS
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Complex and takes time to execute(Data collection/standardization/ cleaning up/Preparation too long the first time)Chance of Model/Analysis not revealing anything (junk data)Chance of Model/Analysis giving false positives (since sparse data issue)Low RoI Exercise (too much effort for little incremental benefit)Dependency on others – researchers, instrumentation, etc.
Specific Risks with Text Analytics…
Rigorous pre Analytics assessment and expectations setting.Success Criteria to be changed from Significance to Consistence/raw counts.Impact Sizing and Vetting the impact with A/B Testing.
…so what can be done about it
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Quick recap of what is it?Quick recap of what it is
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Wrapping it up
WHY IS THE TOPIC SO POPULAR NOW?
Evolution in the value prop of Analysts: What/where/how much -> what can happen ->what should we do ?
Audience has broadened (A numbers middle man -> Front line Managers)Luxury of time has evaporated
Nature of questions have drastically changed (Expectation of being able to connect the dots in “Data Lake” world).
Overselling potential before getting “there”
REALLY WRAPPING IT UP, I PROMISE…
• “Know” that not all Analytics is supposed to be actionable.
• “Must have” User Experience Design (UED) Strategist for the Analytics practice
• “Ensure” Deeper Stakeholder involvement in Analytics development & Test & Learn approach must
• “Develop” Outcome Focused Approach for Analytics
• “Prepare” for ever more increasing ask for analytics and related actionability issues
Putting it all together…
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Quick recap of what is it?Quick recap of what it is
Intended for Knowledge Sharing only
Appendix
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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