"Disruption 101" Keynote Philly Phorum 2013


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Peter Coffee (VP Platform Research at salesforce.com) keynote on harnessing disruption in Mobile, Social, and Big Data technologies using cloud services and predictive tools

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"Disruption 101" Keynote Philly Phorum 2013

  1. 1. Exploiting Disruptive Technologies 101Peter CoffeeVP and Head of Platform Researchsalesforce.com inc.
  2. 2. Safe HarborSafe harbor statement under the Private Securities Litigation Reform Act of 1995: This presentation may contain forward-lookingstatements that involve risks, uncertainties, and assumptions. If any such uncertainties materialize or if any of the assumptions In Other Words:proves incorrect, the results of salesforce.com, inc. could differ materially from the results expressed or implied by the forward-looking statements we make. All statements other than statements of historical fact could be deemed forward-looking, includingany projections of subscriber growth, earnings, revenues, or other financial items and any statements regarding strategies or plansof management for future operations, statements of belief, any statements concerning new, planned, or upgraded services ortechnology developments and customer contracts or use of our services. Everything ThatThe risks and uncertainties referred to above include – but are not limited to – risks associated with developing and delivering newfunctionality for our service, our new business model, our past operating losses, possible fluctuations in our operating results andrate of growth, interruptions or delays in our Web hosting, breach of our security measures, risks associated with possible mergers You See Hereand acquisitions, the immature market in which we operate, our relatively limited operating history, our ability to expand, retain,and motivate our employees and manage our growth, new releases of our service and successful customer deployment, ourlimited history reselling non-salesforce.com products, and utilization and selling to larger enterprise customers. Further informationon potential factors that could affect the financial results of salesforce.com, inc. is included in our annual report and on our Form10-Q for the most recent fiscal quarter: these documents and others are available on the SEC Filings section of the Investor is RealInformation section of our Web site.Any unreleased services or features referenced in this or other press releases or public statements are not currently available andmay not be delivered on time or at all. Customers who purchase our services should make the purchase decisions based uponfeatures that are currently available. Salesforce.com, inc. assumes no obligation and does not intend to update these forward-looking statements.
  3. 3. Specifically, “Disruptive” Is* • Technologically straightforward • Simpler than prior approaches • Less than expected by established market • Attractive in new ways to emerging market • Initially focused on low-profit customers • Innovative at faster pace than incumbents*Bower, Joseph L. & Christensen, Clayton M."Disruptive Technologies: Catching the Wave" Harvard Business Review, Jan–Feb 1995
  4. 4. In Memory of Disruptees Past• Imaging: Film  CCD  CMOS• Printing: Offset  Laser  Inkjet• Storage: 8”5¼”3½”USB keyCloud• Connection: Circuits  Packets• Knowledge: Britannica  Wikipedia
  5. 5. Will Today’s Disruptors Sign In, Please?• Mobility• Social Computing• Big Data
  6. 6. Mobility: More Than a HandleThere are three parts of userexperience to increase convenience:immediacy, simplicity and context.The three parts make up a customer’smobile context, or the overall feedbackof what a customer has told you and isexperiencing during engagement.The Future Of Mobile Is User ContextContext Transforms Product Opportunities ForConsumer Product Strategists
  7. 7. The Opposite is “Antisocial”• ‘Social’ is not non-business• ‘Social’ is not non-serious• ‘Social’ is a set of behaviors – Sensitive to the user’s context – Adaptive to interests – Driven by events to offer proactive aid
  8. 8. Big Data Is Patterns, Not Records“By combing through 7.2million of our electronicmedical records, we havecreated a disease network tohelp illustrate relationshipsbetween various conditionsand how common thoseconnections are. Take a lookby condition or conditioncategory and gender touncover interestingassociations.” visualization.geblogs.com/visualization/network/
  9. 9. What If We Put Them Together?What do you get from• Patterns in big data derived from• Social networks (people/devices) via• Ubiquitous mobile devices/connections?
  10. 10. “Inform” is a Verb• Wiener: information = bits Shannon: information = entropy Information  behavior change• “Even the word ‘library’ is getting hazy…as the number of media grew, and the methods for searching became more sophisticated, there was no substantive difference between the Library of Congress and the Central Intelligence Agency. So they merged.” – Neal Stephenson, Snow Crash (1992)
  11. 11. Social Systems ‘Close the Loop’• Customers: records  communities• Employees: appraisals  collaborations• Partners: supply chain  value network• Financials: transactions  scenarios• USAF OODA:“Observe, Orient, Decide, Act”
  12. 12. Social Systems Demand ‘Big Data’ Power• Islands of data are cheap, but low-value• Integrate data with apps: cloud-platform (PaaS) strength – Data.com: 2 million participants, 1 million updates/month – Radian6: massive data flows distilled into understanding – Heroku + Treasure Data Hadoop: 0 to Warehouse in 3 minutes• Data-driven expertise for developers and managers
  13. 13. Social Systems Demand ‘Big Data’ Power
  14. 14. What’s Taking So Long?• “The typical large organization, twenty years hence, will be composed largely of specialists who direct and discipline their own performance through organized feedback from colleagues and customers.”• “It will be a knowledge-based organization.” Peter F. Drucker, in The New Realities …in 1989
  15. 15. Barriers to Becoming Knowledge-Based• Complex legacy IT portfolios have made mere integration of data an overwhelming task• Cumbersome and brittle integrations have relegated end users to roles as mere consumers• Path of least resistance has led to over-emphasis on complex measures… …based on historical data• Mere thin-client redesign attacked the form, not the substance, of these problems
  16. 16. Let’s Disrupt Our Notion of Normal• On spec, on time, on budget deployment of a fully tested, proven cloud capability: trusted security and global availability• Modern applications, driven by user feedback for continuing improvement – with “clicks, not code” customization• “No Software”: what’s paid for is function, not code. Continuous scrutiny of operations, maintenance of facilities, and world-class security are literally “part of the service”• Multiple upgrades per year: no disruption, shrinking deployment times, backward compatibility to previous API releases• “The future is already here – just not evenly distributed” - William Gibson
  17. 17. Let’s Talk About ‘Why’ – not ‘How’Fast: no delays of capital budgeting; upgrades part of the serviceFocused: no ‘keep the lights on’ software maintenance tasksField-ready: deliver coherent data & logic in any user contextFederated: apps market with click-to-try, click-to-integrateFor any organization, anywhere, of any size
  18. 18. Can Disruption Be Forecast?
  19. 19. Can Disruption Be Forecast? According to the researchers, Moore’s Law and other models such as Kryder’s Law and Gompertz’ Law predict a smooth increasing exponential curve for the improvement in performance of various technologies. In contrast, the authors found that the performance of most technologies proceeds in steps (or jumps) of big improvements interspersed with waits (or periods of no growth in performance)… While no one law applies to every market, Tellis and his co- authors looked at 26 technologies in six markets from lighting to automobile batteries, and found that the SAW model worked in all six, in contrast to several other competing models.
  20. 20. Excuse me, sir, about that rathole…An example of how the SAW model couldhave saved a company from decline isSonys investment in TVs. Sony keptinvesting in cathode ray tube technology(CRT) even after liquid crystal displaytechnology (LCD) first crossed CRT inperformance in 1996...Sony introduced the FD Trinitron/ WEGAseries, a flat version of the CRT. CRT out-performed LCD for a few years, butultimately lost decisively to LCD in 2001. Incontrast, by backing LCD, Samsung grew tobe the worlds largest manufacturer of thebetter performing LCD…"Prediction of the next step size and waittime using SAW could have helped Sonysmanagers make a timely investment in LCDtechnology," according to the study.
  21. 21. Excuse me, sir, about that rathole…An example of how the SAW model could Abstract:have saved a company from decline is The estimates of the model provide four important results.Sonys investment in TVs. Sony keptinvesting in cathode ray tube technology First, Moores Law and Kryders law do not generalize across(CRT) even after liquid crystal display markets; none holds for all technologies even in a singletechnology (LCD) first crossed CRT in market. Second, SAW produces superior predictions overperformance in 1996... traditional methods, such as the Bass model or Gompertz law, and can form predictions for a completely newSony introduced the FD Trinitron/ WEGA technology, by incorporating information from otherseries, a flat version of the CRT. CRT out- categories on time varying covariates. Third, analysis of theperformed LCD for a few years, butultimately lost decisively to LCD in 2001. In model parameters suggests that: i) recent technologiescontrast, by backing LCD, Samsung grew to improve at a faster rate than old technologies; ii) as thebe the worlds largest manufacturer of the number of competitors increases, performance improves inbetter performing LCD… smaller steps and longer waits; iii) later entrants and technologies that have a number of prior steps tend to have"Prediction of the next step size and wait smaller steps and shorter waits; but iv) technologies withtime using SAW could have helped Sonys long average wait time continue to have large steps. Fourth,managers make a timely investment in LCDtechnology," according to the study. technologies cluster in their performance by market. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2115237
  22. 22. The World is How Flat? “It “It now possible for is is now possiblemore people than ever tomore people than evercollaborate and compete to collaborate and in real time... compete in real time... …with more other people… …on more different kinds of work… …from more different corners of the planet… …and on a more equal footing… …than at any previous time in the history of the world.”
  23. 23. Build on Best Practices
  24. 24. Call on Trusted Advisors
  25. 25. Do Not Think Incrementally• In 1908, 4,700 hours of factory labor would pay for a Model T Ford;• Today, the same amount of labor will earn the price of a Porsche Cayman. That doesn’t make the Porsche the commuter vehicle of choice… …and even a Prius is not tomorrow’s solution.
  26. 26. Do Not Think Incrementally• In 1908, 4,700 hours of factory labor would pay for a Model T Ford;• Today, the same amount of labor will earn the price of a Porsche Cayman. That doesn’t make the Porsche the commuter vehicle of choice… …and even a Prius is not tomorrow’s solution. But in 1908, New York City’s first subway was only four years old.• Are you thinking in terms of the network that will create value?
  27. 27. Connect With Customersin a Whole New WayConnected ConnectedPartners Products Connected Connected Customers Employees
  28. 28. petercoffeelinkedin.com/in/petercoffeefacebook.com/peter.coffee Thank Youpcoffee@salesforce.com
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