Presentación del evento de Harvard Business Review sobre Analítica y Big Data
(15 de Octubre 2013)
"Featuring analytics expert Tom Davenport, author of Competing on Analytics, Analytics at Work, and the just-released Keeping Up with the Quants" 
Analytics 3.0 Measurable business impact from analytics & big data
1. Analytics 3.0: Measurable Business
Impact From Analytics & Big Data
Featuring analytics expert Tom Davenport, author of
Competing on Analytics, Analytics at Work, and the
just-released Keeping Up with the Quants
OCTOBER 15, 2013
2. Questions?
To ask a question
… click on the
“question icon” in
the lower-right
corner of your
screen.
OCTOBER 17, 2012
5. Analytics 3.0: Measurable Business
Impact From Analytics & Big Data
Today’s Speaker
Tom Davenport
President’s Distinguished Professor,
Management & IT, Babson College
Author, Keeping Up with the Quants
OCTOBER 15, 2013
6. Analytics 3.0
Measurable Business Impact From Analytics & Big Data
Tom Davenport
Babson/MIT/International Institute for Analytics
Harvard Business Review/SAP Webcast
15 October 2013
7. The Rise of Big Data
More Words on Big Data?
Working wonders for
Google, eBay, & LinkedIn
…but what about
everyone else?
Big data begins
at online firms
& startups
No technical or
organizational
infrastructure to
co-exist with
Findings show evolution
of a new analytics
paradigm
What happens in
big companies when
IT & analytics are
well-entrenched?
23. Analytics 3.0│Everything’s Much Faster!
► In-memory analytics
► From 2-3 hours to prioritize customers
at Hilti to 2-3 seconds
► From 22 hours to optimize all prices at
Macy’s to 20 minutes
► In-database processing
► Propensity scoring for all customers in
seconds, not weeks, at Cabela’s
► From 30 variables to 5000 in model
predicting revenues for
InterContinental Hotels Group
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24. Analytics 3.0│Everything’s Much Cheaper!
► Some organizations using big
data technologies just to save
money
Cost/Performance
► Hadoop useful as short-term
“persistence layer” or “discovery
platform”—but requires
expensive and specialized skills
► Not directly comparable yet to
data warehouses in terms of
hygiene
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30. Problematic Issues 3.0
• Labor intensiveness of data science work
• Privacy/security implications
• How to get to more sophisticated
analytics with big data
• Integration with processes and systems
• Need for integrated architectures,
governance, transition processes
• Implications of people shortage (if there
is one) and ways to address it
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