Analytics 3.0 Measurable business impact from analytics & big data
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Transcript of 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 17, 2012
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OCTOBER 15, 2013
Today’s Speaker
Tom DavenportPresident’s Distinguished Professor, Management & IT, Babson College
Author, Keeping Up with the Quants
Analytics 3.0: Measurable Business Impact From Analytics & Big Data
OCTOBER 15, 2013
Analytics 3.0Measurable Business Impact From Analytics & Big Data
Tom Davenport
Babson/MIT/International Institute for Analytics
Harvard Business Review/SAP Webcast
15 October 2013
More Words on Big Data?
Big data beginsat online firms
& startups
No technical ororganizational
infrastructure to co-exist with
Working wonders forGoogle, eBay, & LinkedIn
…but what abouteveryone else?
What happens in big companies when
IT & analytics arewell-entrenched?
Findings show evolutionof a new analytics
paradigm
The Rise of Big Data
“Big Data in Big Companies” Study
8 | 2013 © Thomas H. Davenport All Rights Reserved
� 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
Analytics 1.0│Traditional Analytics
1.0 Traditional 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
Analytics 1.0│Data Environment
10 | 2013 © Thomas H. Davenport All Rights Reserved
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
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
Analytics 2.0│The Big Data Era
1.0 Traditional Analytics
Big Data2.0
• Primarily descriptive analytics and reporting
• Internally sourced, relatively small, structured data
• “Back room” teams of analysts
• Internal decision support
• Complex, large, unstructured data sources
• New analytical and computational capabilities
• “Data Scientists” emerge
• Online firms create data-based products and services
13 | 2013 © Thomas H. Davenport All Rights Reserved
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
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
Analytics 2.0│Data Environment
16 | 2013 © Thomas H. Davenport All Rights Reserved
Analytics 3.0│Fast Business Impact for the Data Economy
1.0 Traditional Analytics
Fast Business Impact for the Data Economy
Big Data2.0
3.0• Primarily descriptive
analytics and reporting
• Internally sourced, relatively small, structured data
• “Back room” teams of analysts
• Internal decision support
• Complex, large, unstructured data sources
• New analytical and computational capabilities
• “Data Scientists” emerge
• Online firms create data-based products and services
17 | 2013 © Thomas H. Davenport All Rights Reserved
• 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 decision-making at scale
Analytics 3.0│Fast Business Impact for the Data Economy
1.0 Traditional Analytics
Fast Business Impact for the Data Economy
Big Data2.0
3.0• Primarily descriptive
analytics and reporting
• Internally sourced, relatively small, structured data
• “Back room” teams of analysts
• Internal decision support
• 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 decision-making at scale
• Complex, large, unstructured data sources
• New analytical and computational capabilities
• “Data Scientists” emerge
• Online firms create data-based products and services
Today
18 | 2013 © Thomas H. Davenport All Rights Reserved
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
Analytics 3.0│Data Types
Website activity
Clickstream logs
Social Feeds
Spatial GPS
TwitterLinkedIn
Blogs
Videos
ImagesRSS
Text messages
Cloud
Hosted applicationsMobile devices
Device sensors
XML
Articles
Documents
Presentations
20 | 2013 © Thomas H. Davenport All Rights Reserved
Analytics 3.0│Data Management Environment
21 | 2013 © Thomas H. Davenport All Rights Reserved
Analytics 3.0│Technologies and People
15 | 2013 © Thomas H. Davenport All Rights Reserved
► 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
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
Analytics 3.0│Everything’s Much Cheaper!
24
Cost/PerformanceCost/Performance► Some organizations using big data technologies just to save money
► 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
25 | 2013 © Thomas H. Davenport All Rights Reserved
► $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”
GE 3.0
26 | 2013 © Thomas H. Davenport All Rights Reserved
► 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
Procter & Gamble 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
Novartis 3.0
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
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
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
30
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 | 2013 © Thomas H. Davenport All Rights Reserved
Questions?
OCTOBER 17, 2012
To ask a question … click on the “question icon” in the lower-right corner of your screen.
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