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" 

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

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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?

8. “Big Data in Big Companies” Study
How new? “Not very” to many;
continually adding data over time
 UPS—Started building telematics
capabilities in 1986
Excited about new sources of
data, new processing capabilities
Familiar rationales for big data:
 Same decisions faster—Macy’s, Caesars
 Same decisions cheaper—Citi
 Better decisions with more data—United
Healthcare
 Product/service innovation—GE, Novartis
Need new management
paradigm
8 | 2013 © Thomas H. Davenport All Rights Reserved

9. Analytics 1.0│Traditional Analytics
Traditional
1.0 Analytics
• Primarily descriptive
analytics and
reporting
• Internally sourced,
relatively small,
structured data
• “Back office” teams
of analysts
• Internal decision
support
9 | 2013 © Thomas H. Davenport All Rights Reserved

10. Analytics 1.0│Data Environment
10 | 2013 © Thomas H. Davenport All Rights Reserved

11. Analytics 1.0│Other Technologies
Standalone spreadsheets
BI and analytics “packages”
ETL tools
OLAP cubes
On-premise servers
11 | 2013 © Thomas H. Davenport All Rights Reserved

12. Analytics 1.0│Ethos
► Stay in the back room—as far away from decision-makers as
possible—and don’t cause trouble
► Take your time—nobody’s that interested in your results anyway
► Talk about “BI for the masses,” but make it all too difficult for anyone
but experts to use
► Look backwards—that’s where the threats to your business are
► If possible, spend much more time getting data ready for analysis
than actually analyzing it
► Stay inside the sheltering confines of the IT organization
12 | 2013 © Thomas H. Davenport All Rights Reserved

13. Analytics 2.0│The Big Data Era
Traditional
1.0 Analytics
• Primarily descriptive
analytics and
reporting
• Internally sourced,
relatively small,
structured data
• “Back room” teams
of analysts
• Internal decision
support
2.0 Big Data
• Complex, large,
unstructured data sources
• New analytical and
computational capabilities
• “Data Scientists” emerge
• Online firms create databased products and
services
13 | 2013 © Thomas H. Davenport All Rights Reserved

14. Analytics 2.0│Data Products
► Google—Search, AdSense,
Books, Maps, Scholar, etc.,
etc.
► LinkedIn—People You May
Know, Jobs You May Like,
Groups You May Be Interested
In, etc.
► Netflix Cinematch, Max, etc.
► Zillow Zestimates, rent
Zestimates, Home Value
Index, Underwater Index, etc.
► Facebook People You May
Know, Custom Audiences,
Exchange
14 | 2013 © Thomas H. Davenport All Rights Reserved

15. Analytics 2.0│Ethos
► Be “on the bridge” if not in charge of it
► “Agile is too slow”
► “Being a consultant is the dead zone”
► Develop products, not presentations
or reports
► Information (and hardware and
software) wants to be free and shared
► All problems can be solved in a
hackathon
► “Nobody’s ever done this before!”
15 | 2013 © Thomas H. Davenport All Rights Reserved

16. Analytics 2.0│Data Environment
16 | 2013 © Thomas H. Davenport All Rights Reserved

17. Analytics 3.0│Fast Business Impact for the Data
Economy
Traditional
1.0 Analytics
• Primarily descriptive
analytics and
reporting
Fast Business
3.0 Impact for the
Data Economy
• Internally sourced,
relatively small,
structured data
• “Back room” teams
of analysts
• Internal decision
support
2.0 Big Data
• Complex, large,
unstructured data sources
• New analytical and
computational capabilities
• “Data Scientists” emerge
• Online firms create databased products and
services
• A seamless blend of
traditional analytics and big
data
• Analytics integral to running
the business; strategic asset
• Rapid, agile insight delivery
• Analytical tools at point of
decision
• Industrialized decisionmaking at scale
17 | 2013 © Thomas H. Davenport All Rights Reserved

18. Analytics 3.0│Fast Business Impact for the Data
Economy
Today
Traditional
1.0 Analytics
• Primarily descriptive
analytics and
reporting
Fast Business
3.0 Impact for the
Data Economy
• Internally sourced,
relatively small,
structured data
• “Back room” teams
of analysts
• Internal decision
support
2.0 Big Data
• Complex, large,
unstructured data sources
• New analytical and
computational capabilities
• “Data Scientists” emerge
• Online firms create databased products and
services
• A seamless blend of
traditional analytics and big
data
• Analytics integral to running
the business; strategic asset
• Rapid, agile insight delivery
• Analytical tools at point of
decision
• Industrialized decisionmaking at scale
18 | 2013 © Thomas H. Davenport All Rights Reserved

19. Analytics 3.0│Competing in the Data
Economy
► Every company—not just online firms—can
create data and analytics-based products and
services that change the game
► Use “data exhaust” to help customers use your
products and services more effectively
► Start with data opportunities or start with
business problems? Answer is yes!
► Need “data products” team good at data science,
customer knowledge, new product/service
development
► Opportunities and data come at high speed, so
quants must respond quickly
19 | 2013 © Thomas H. Davenport All Rights Reserved

20. Analytics 3.0│Data Types
Social Feeds
Hosted applications
Blogs
Twitter
Website activity
Cloud
Email
Presentations
Images
Articles
Device sensors
Clickstream logs
Documents
Mobile devices
LinkedIn
Spatial GPS
Text messages
RSS
Videos
XML
20 | 2013 © Thomas H. Davenport All Rights Reserved

21. Analytics 3.0│Data Management
Environment
21 | 2013 © Thomas H. Davenport All Rights Reserved

22. Analytics 3.0│Technologies and People
► Analytical “apps”
► Integrated and embedded models
► Focus on data discovery
► Heavy use of visual analytics
► Faster technology and analytical methods
► Blended data science/analytics/IT teams
► Chief Analytics Officers and their ilk
► Use of prescriptive analytics
15 | 2013 © Thomas H. Davenport All Rights Reserved

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
23

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
24

25. GE 3.0
► $2B initiative in software and analytics
► Primary focus on data-based products and
services from “things that spin”
► Will reshape service agreements for locomotives,
jet engines, turbines
► Gas blade monitoring in turbines produces 588
gigabytes/day—7 times Twitter daily volume
► Marketing new industrial data platforms and
brands like “Predicity” and “Datalandia”
25 | 2013 © Thomas H. Davenport All Rights Reserved

26. Procter & Gamble 3.0
► Primary focus on improving management
decisions
► “Information and Decision Solutions” (IT) embeds
over 300 analysts in leadership teams
► Over 50 “Business Suites” for executive
information viewing and decision-making
► “Decision cockpits” on 50K desktops
► Real-time social media sentiment analysis for
“Consumer Pulse”
► Financial restatements in seconds versus
several days in the past
► P&L’s by brand and retailer on the fly
26 | 2013 © Thomas H. Davenport All Rights Reserved

27. Novartis 3.0
► CEO Joe Jimenez: “If you think about the amounts
of data that are now available, bioinformatics
capability is becoming very important, as is the
ability to mine that data and really understand, for
example, the specific mutations that are leading to
certain types of cancers.”
► “IT has become a very important part of drug
discovery”
► Programs at Novartis Institutes for Biomedical
Research in bioinformatics, quantitative biology,
computational biology
► Big user of big data tools
27 | 2013 © Thomas H. Davenport All Rights Reserved

28. Schneider National 3.0
 Has invested heavily in sensors to automate
data collection on trucks, trailers and intermodal
containers
 Quality of decisions has improved as a result of
sensor data
 Prescriptive analytics are changing job roles
and relationships
 Sensor data related to safety predicts drivers at
risk of safety accident for preventative
conversations
28 | 2013 © Thomas H. Davenport All Rights Reserved

29. Monsanto 3.0
 FieldScripts program uses data from field
testing and Monsanto research to
recommend what corn hybrids to plant
where
 Genotypes and phenotypes of plants add
up to tens of petabytes of data for analysis
 Field photographs analyzed to determine
correct watering, fertilizer
 Paid almost $1B for The Climate
Company, which gathers and analyzes
weather data for agriculture
 Embarking on data and analytics
education programs for farmer customers
29 | 2013 © Thomas H. Davenport All Rights Reserved

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
3
0

31. Recipe for a 3.0 World
 Start with an existing capability for data
management and analytics
 Add some unstructured, large-volume data
 Throw product/service innovation into the mix
 Add a dash of Hadoop and a pinch of NoSQL
 Cook up data in a high-heat convection oven
 Embed this dish into a well-balanced meal of
processes and systems
 Promote the chef to Chief Analytics Officer
31 | 2013 © Thomas H. Davenport All Rights Reserved

32. 32 | 2013 © Thomas H. Davenport All Rights Reserved

33. Questions?
To ask a question
… click on the
“question icon” in
the lower-right
corner of your
screen.
OCTOBER 17, 2012

34. Thank you for joining us!
This webinar was made possible
by the generous support of SAP.
Learn more at www.SAP.com
OCTOBER 15, 2013