Analytics 3.0: Measurable Business
Impact From Analytics & Big Data

Featuring analytics expert Tom Davenport, author of
C...
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… click on the
“question icon” in
the lower-right
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OCTOBER 17, 2012
Presentation Download Link
Click on the double 
links icon here to 
download the 
presentation 
materials.

OCTOBER 17, 20...
Follow the Conversation on Twitter
Use #HBRwebinar
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OCTOBER 15, 2013
Analytics 3.0: Measurable Business
Impact From Analytics & Big Data
Today’s Speaker
Tom Davenport
President’s Distinguishe...
Analytics 3.0
Measurable Business Impact From Analytics & Big Data
Tom Davenport
Babson/MIT/International Institute for An...
The Rise of Big Data
More Words on Big Data?

Working wonders for
Google, eBay, & LinkedIn
…but what about
everyone else?
...
“Big Data in Big Companies” Study
How new? “Not very” to many;
continually adding data over time
 UPS—Started building te...
Analytics 1.0│Traditional Analytics

Traditional

1.0 Analytics

• Primarily descriptive
analytics and
reporting
• Interna...
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 serve...
Analytics 1.0│Ethos
► Stay in the back room—as far away from decision-makers as
possible—and don’t cause trouble
► Take yo...
Analytics 2.0│The Big Data Era

Traditional

1.0 Analytics

• Primarily descriptive
analytics and
reporting
• Internally s...
Analytics 2.0│Data Products
► Google—Search, AdSense,
Books, Maps, Scholar, etc.,
etc.
► LinkedIn—People You May
Know, Job...
Analytics 2.0│Ethos
► Be “on the bridge” if not in charge of it
► “Agile is too slow”
► “Being a consultant is the dead zo...
Analytics 2.0│Data Environment

16 | 2013 © Thomas H. Davenport All Rights Reserved
Analytics 3.0│Fast Business Impact for the Data
Economy

Traditional

1.0 Analytics

• Primarily descriptive
analytics and...
Analytics 3.0│Fast Business Impact for the Data
Economy
Today

Traditional

1.0 Analytics

• Primarily descriptive
analyti...
Analytics 3.0│Competing in the Data
Economy
► Every company—not just online firms—can
create data and analytics-based prod...
Analytics 3.0│Data Types
Social Feeds

Hosted applications
Blogs

Twitter

Website activity
Cloud

Email

Presentations

I...
Analytics 3.0│Data Management
Environment

21 | 2013 © Thomas H. Davenport All Rights Reserved
Analytics 3.0│Technologies and People
► Analytical “apps”
► Integrated and embedded models
► Focus on data discovery
► Hea...
Analytics 3.0│Everything’s Much Faster!
► In-memory analytics
► From 2-3 hours to prioritize customers
at Hilti to 2-3 sec...
Analytics 3.0│Everything’s Much Cheaper!
► Some organizations using big
data technologies just to save
money

Cost/Perform...
GE 3.0
► $2B initiative in software and analytics
► Primary focus on data-based products and
services from “things that sp...
Procter & Gamble 3.0
► Primary focus on improving management
decisions
► “Information and Decision Solutions” (IT) embeds
...
Novartis 3.0
► CEO Joe Jimenez: “If you think about the amounts
of data that are now available, bioinformatics
capability ...
Schneider National 3.0
 Has invested heavily in sensors to automate
data collection on trucks, trailers and intermodal
co...
Monsanto 3.0
 FieldScripts program uses data from field
testing and Monsanto research to
recommend what corn hybrids to p...
Problematic Issues 3.0
• Labor intensiveness of data science work
• Privacy/security implications
• How to get to more sop...
Recipe for a 3.0 World
 Start with an existing capability for data
management and analytics
 Add some unstructured, larg...
32 | 2013 © Thomas H. Davenport All Rights Reserved
Questions?
To ask a question
… click on the
“question icon” in
the lower-right
corner of your
screen.

OCTOBER 17, 2012
Thank you for joining us!
This webinar was made possible
by the generous support of SAP.
Learn more at www.SAP.com

OCTOBE...
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Analytics 3.0 Measurable business impact from analytics & big data

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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" 

Published in: Business, Technology

Analytics 3.0 Measurable business impact from analytics & big data

  1. 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. 2. Questions? To ask a question … click on the “question icon” in the lower-right corner of your screen. OCTOBER 17, 2012
  3. 3. Presentation Download Link Click on the double  links icon here to  download the  presentation  materials. OCTOBER 17, 2012
  4. 4. Follow the Conversation on Twitter Use #HBRwebinar @HBRExchange OCTOBER 15, 2013
  5. 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. 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. 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. 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. 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. 10. Analytics 1.0│Data Environment 10 | 2013 © Thomas H. Davenport All Rights Reserved
  11. 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. 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. 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. 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. 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. 16. Analytics 2.0│Data Environment 16 | 2013 © Thomas H. Davenport All Rights Reserved
  17. 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. 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. 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. 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. 21. Analytics 3.0│Data Management Environment 21 | 2013 © Thomas H. Davenport All Rights Reserved
  22. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 32 | 2013 © Thomas H. Davenport All Rights Reserved
  33. 33. Questions? To ask a question … click on the “question icon” in the lower-right corner of your screen. OCTOBER 17, 2012
  34. 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
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