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Babson-MIT-Harvard Digital Enterprise 2013
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Babson-MIT-Harvard Digital Enterprise 2013

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Babson-MIT-Harvard Digital Enterprise 2013 Babson-MIT-Harvard Digital Enterprise 2013 Presentation Transcript

  • The Digital Enterprise Big Data and Analytics Lead the Way! Thomas H. Davenport Babson/MIT/Harvard December 5, 2013
  • • Efficient, fast transactions • Agile system development • IT-enabled processes • Knowledge management • The ability to make sense of exabytes of data: analytics! • Ranked the #1 priority at WSJ CIO Summit last week The Digital Enterprise Key Capabilities
  • Big data begins at online firms & startups No technical or organizational infrastructure to co-exist with Working wonders for Google, eBay, & LinkedIn …but what about everyone else? What happens in 20 big companies when analytics are well-entrenched? Findings show evolution of a new analytics paradigm
  • “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
  • Analytics 1.0 Traditional Analytics • Primarily descriptive analytics and reporting • Internally sourced, relatively small, structured data • “Back room” teams of analysts • Internal decision support focus • Slowly-developed models 1.0
  • Analytics 1.0 Data Environment ERP CRM Legacy 3rd Party Apps Reporting OLAP Ad Hoc Modeling
  • • Spreadsheets • BI and analytics “packages” • ETL tools • OLAP cubes • On-premise servers • Out-of-database/memory analytics Analytics 1.0 Other Technologies
  • Keep inside the sheltering confines of the IT organization Take your time— nobody’s that interested in your results anyway Focus on the past, where the real threats to your business are
  • Analytics 2.0 The Big Data era • Complex, large, unstructured data about customers • New analytical and computational capabilities • “Data Scientists” emerge • Online and startup firms create data and analytics- based products and services 2.0
  • 2.0 Data Products From Online Firms • 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 Data Environment Map/Reduce Web Logs Images & Videos Social Media Docs & PDFs HDFS Operational Systems Data Warehouse Data Marts & ODS
  • We need to be “on the bridge” Agile is too slow Consulting = dead zone We’re changing the world
  • Analytics 3.0 Fast, Pervasive Impact in the Age of Smart Machines • Analytics used for data products and Industrialized decision processes • A seamless blend of traditional analytics and big data • Analytics integral to all business functions • Rapid, agile insight and model delivery • Analytical tools available at point and time of decision • Analytics are everybody’s job 3.0 TODAY
  • 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 • Continuous, real-time analytics • 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 • Internally, analytics built at scale and embedded into decision processes
  • Analytics 3.0: Data Types • Customer profiles • Organization contacts • Billing • Marketing • Contracts/orders • Shipping • Claims • Call center • Customer service • Purchase history • Segmentation • Customer value • Purchasing behavior • Recommendations • Sentiment analysis • Target marketing • Satisfaction • Customer experience management • Service tiers Clickstream logs Images RSS Videos Hosted applications Spatial GPS LinkedIn Device sensors Email Articles Text messages Cloud Mobile devices XML Presentations Blogs Website activity Social Feeds Twitter Documents
  • Analytics 3.0 Data Management Choices
  • • Heavy reliance on machine learning • In-memory and in-database analytics • Integrated and embedded models • Analytical “apps” by industry and decision • Focus on data discovery • Blended data science/business/IT teams • Chief Analytics Officers in many firms Analytics 3.0 Technology & people 3.0
  • • • Primary focus on improving management decisions at scale • “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 • 35% of marketing budget on digital • Real-time social media sentiment analysis for “Consumer Pulse” Procter & Gamble 3.0 176 years old
  • • $2B initiative in software, analytics, and “Industrial Internet” • 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 • Offering new industrial data platforms and brands like “Predictivity” and “Predix” GE 3.0 120 years old
  • • Bill Ford: “The car is really becoming a rolling group of sensors.” • Ford’s Digital Analytics and Optimization team has full responsibility for all B2C channels and N. American business units • Dynamic multichannel testing and targeting with automation and integration of SEO/SEM, CRM, email, media, etc. • Hyper-local dealer support digital algorithm delivered 85% increase in action rate and 48% decrease in cost per action Ford 3.0 110 years old
  • Recipe for a 3.0 World 1. Start with an existing capability for data management and analytics 2. Add some unstructured, large-volume data 3. Throw some product/service innovation into the mix 4. Add a dash of Hadoop and a pinch of NoSQL 5. Cook up some data in a high-heat convection oven 6. Train your sous chefs in big data and analytics
  • • Need to embed analytics into other systems • May be role for ongoing monitoring of embedded analytics • Software firms hold up the “data mirror” • Dealing with the law of large numbers on analytical skills • Analysts often need to be embedded to have an impact Implications for Software/Services Providers
  • Thank you! tdavenport@babson.edu