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 …

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" 

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  • 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
  • 3. Presentation Download Link Click on the double  links icon here to  download the  presentation  materials. OCTOBER 17, 2012
  • 4. Follow the Conversation on Twitter Use #HBRwebinar @HBRExchange OCTOBER 15, 2013
  • 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