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
- 1.Exploiting Disruptive Technologies 101Peter CoffeeVP and Head of Platform Researchsalesforce.com inc.
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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, JanFeb 1995 4. In Memory of Disruptees Past Imaging: Film CCD CMOS Printing: Offset Laser Inkjet Storage: 853USB keyCloud Connection: Circuits Packets Knowledge: Britannica Wikipedia 5. Will Todays Disruptors Sign In, Please? Mobility Social Computing Big Data 6. Mobility: More Than a HandleThere are three parts of userexperience to increase convenience:immediacy, simplicity and context.The three parts make up a customersmobile 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. The Opposite is Antisocial Social is not non-business Social is not non-serious Social is a set of behaviors Sensitive to theusers context Adaptive to interests Driven by events tooffer proactive aid 8. Big Data Is Patterns, Not RecordsBy 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. What If We Put Them Together?What do you get from Patterns in big data derived from Social networks(people/devices) via Ubiquitous mobiledevices/connections? 10. Inform is a Verb Wiener: information = bitsShannon: information = entropyInformation behavior change Even the word library is gettinghazyas the number of media grew,and the methods for searching becamemore sophisticated, there was nosubstantive difference between theLibrary of Congress and the CentralIntelligence Agency. So they merged. Neal Stephenson, Snow Crash (1992) 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. 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. Social Systems Demand Big Data Power 14. Whats Taking So Long? The typical large organization, twenty years hence, will becomposed largely of specialists who direct and disciplinetheir own performance through organized feedback fromcolleagues and customers. It will be a knowledge-based organization. Peter F. Drucker, in The New Realities in 1989 15. Barriers to Becoming Knowledge-Based Complex legacy IT portfolios have made mere integration of data anoverwhelming task Cumbersome and brittle integrations have relegated end users toroles as mere consumers Path of least resistancehas led to over-emphasison complex measuresbased on historical data Mere thin-client redesignattacked the form, notthe substance, of these problems 16. Lets 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 continuingimprovement with clicks, not code customization No Software: whats paid for is function, not code. Continuousscrutiny of operations, maintenance of facilities, and world-classsecurity are literally part of the service Multiple upgrades per year: no disruption, shrinking deploymenttimes, backward compatibility to previous API releases The future is already here just not evenly distributed- William Gibson 17. Lets Talk About Why not HowFast: 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. Can Disruption Be Forecast? 19. Can Disruption Be Forecast?According to the researchers, Moores Lawand other models such as Kryders Law andGompertz Law predict a smooth increasingexponential curve for the improvement inperformance of various technologies. Incontrast, the authors found that theperformance of most technologies proceedsin steps (or jumps) of big improvementsinterspersed with waits (or periods of nogrowth in performance) While no one lawapplies to every market, Tellis and his co-authors looked at 26 technologies in sixmarkets from lighting to automobilebatteries, and found that the SAW modelworked in all six, in contrast to several othercompeting models. 20. Excuse me, sir, about that ratholeAn 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. Excuse me, sir, about that ratholeAn example of how the SAW model could Abstract:have saved a company from decline isThe estimates of the model provide four important results.Sonys investment in TVs. Sony keptinvesting in cathode ray tube technologyFirst, 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 Gompertzlaw, and can form predictions for a completely newSony introduced the FD Trinitron/ WEGAtechnology, 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. Inmodel 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 thenumber of competitors increases, performance improves inbetter performing LCDsmaller steps and longer waits; iii) later entrants andtechnologies that have a number of prior steps tend to have"Prediction of the next step size and waitsmaller 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. The World is How Flat? It It now po