Presented to a forum of strategy and technology consultants specialising in the media sector, this presentation highlights the difference between horizontal and vertical innovation. It draws parallels between business intelligence and big data science in that context. It showcases examples of successful implementation of prescriptive analytics.

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Data Disruption
Chandan Rajah
@ChandanRajah
by Vertical Innovation in
Media

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Opportunities for Vertical Innovation

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Horizontal Innovation
Improvement to Status Quo
VerticalInnovation
StepChangetoStatusQuo
Horizontal & Vertical Innovation

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Horizontal Innovation
Improvement to Status Quo
VerticalInnovation
StepChangetoStatusQuo
Business Intelligence & Big Data Science
Analytic
s
Business
Intelligence
Big Data
Science
Strong Shift in Value Drivers
• Operation Efficiency
• Competitive Agility
• Customer Engagement
• Customer Retention

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Big Data
Big Data ≠ Data Volume
Big Data = Oil Transport
Think of data like ‘Crude Oil’
Big Data is about extracting ‘crude oil’; transporting it in ‘pipelines’; storing it in ‘mega tanks’

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Data Science
Data Science ≠ Statistical Analysis
Data Science = Oil Refinery
Data science is about ‘treating’ data; applying ‘science’ to the data;
Refine the data ‘results’; and combine to form ‘insight’

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data you know
data you don’t know
questionsyou’reasking
questionsyou’renotasking
Data Analysis
Data Science
reporting & description
discovery & prediction
DATA MODELLING
Y  F( X, random noise, parameters)
ALGORITHMIC MODELLING
Y  [ BLACK BOX ]  X
Data Modeling versus Data Science

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Data Science and the Data Value Chain
generation transport knowledge insight action
Big Data Science

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Showcase of Value Creation

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Opportunities for Big Data Science
1.
Operational Efficiency
“do what we do but better by reducing cost or increasing revenue opportunities”
2.
Competitive Agility
“increase speed to respond to market change and capability to carve out bigger slice”
3.
Customer Engagement
“make product relevant and available at the right time in the customer journey”
4.
Customer Retention
“proactively identity and address customer concerns and issues to increase satisfaction”

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Analytics Value Scale
R
REPORT
I
INFORM
K
KNOW
P
PRESCRIBE
I
INFER
PAST FUTURE
Substantial Value Increase

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Examples of Prescriptive Value Creation

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Prescriptive Fraud Detection and Intervention

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Proactive Fault Prevention

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Proactive Fault Prevention

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Automatic Risk Hedging

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Engagement through Understanding

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Retention through Satisfaction

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Thank You