Analytics 3.0 Measurable business impact from analytics & big data

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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OCTOBER 15, 2013

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

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

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?

“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

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


Analytics 1.0│Data Environment


Analytics 1.0│Other Technologies
Standalone spreadsheets
BI and analytics “packages”
ETL tools
OLAP cubes
On-premise servers


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


Analytics 2.0│The Big Data Era

Traditional

1.0 Analytics

analytics and
reporting
relatively small,
structured data
• “Back room” teams
of analysts
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


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

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!”


Analytics 2.0│Data Environment


Analytics 3.0│Fast Business Impact for the Data
Economy

Traditional

1.0 Analytics

analytics and
reporting

Fast Business
3.0 Impact for the
Data Economy

relatively small,
structured data
of analysts
support

2.0 Big Data
• Complex, large,
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


Analytics 3.0│Fast Business Impact for the Data
Economy
Today

Traditional

1.0 Analytics

analytics and
reporting

Fast Business
3.0 Impact for the
Data Economy

relatively small,
structured data
of analysts
support

2.0 Big Data
• Complex, large,
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


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


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


Analytics 3.0│Data Management
Environment


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


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

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

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”


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

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


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


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

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

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


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

Analytics 3.0 Measurable business impact from analytics & big data

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