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Analyzing Big Data to Discover Honest Signals of Innovation


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2013 한국데이터사이언스 창립기념 심포지움 발표 - MIT, Peter Gloore

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Analyzing Big Data to Discover Honest Signals of Innovation

  1. 1. Peter A. Gloor MIT Center for Collective Intelligence Analyzing Big Data to Discover Honest Signals of Innovation
  2. 2. Communication for Creativity How the right communication style will make you more successful and happy
  3. 3. Collaborative Interest Network Collaborative Learning Network Collaborative Innovation Network Creator
  4. 4. Most effective communicators personal community Degree of Sharing high Degree of Interactivity low high (m-m) small (1-1) Degree of Connectivity (1-m)
  5. 5. Very happy customer (NPS 67%) High GBC Clear leaders Very unhappy customer (NPS -33%) Low GBC Leaders invisible
  6. 6. Blue – drummer Red – singer/leader Green - audience
  7. 7. high- performing low- performing
  8. 8. Centralized leaders Non-centralized leaders
  9. 9. -100 -50 0 50 100 150 Non-emotional 1000 -600 -400 -200 0 200 400 600 emotional
  10. 10. Thank YouTom Allen Adam Almozlino Robin Athey Linda Bäbler Matias Barahona Melina Becker Hans Brechbuhl Gloria Busche John D. Collins Scott Cooper Marius Cramer Patrick DeBoer Arash Delijani George Dellal Marco DeMaggio Pierre Dorsaz Lyric Doshi Scott Dynes Marc Egger Eric Esser Kai Fischbach Hauke Führes Cristobal Garcia Julia Gluesing Francesca Grippa Michael Henninger George Herman Takashi Iba Bill Ives Eric Johnson Adriaan Jooste Jermain Kaminsiki Min-Hyung Kang Yared Kidane Reto Kleeb Jonas Krauss Dustin Larimer Casper Lassenius Rob Laubacher Jonas Lauener Charles Leiserson Fillia Makedon Tom Malone Pascal Marmier Peter Margolis Chris Miller Stefan Nann Keiichi Nemoto Tuomas Niinimäki Daniel Olguin Olguin Daniel Oster Maria Paasivaara Molly Pace-Scrivener Sandy Pentland John Quimby Shannon Provost Johannes Putzke Ornit Raz Renaud Richardet Ken Riopelle Michael Schober Detlef Schoder Thomas Schmalberger Michael Seid Shosta Sulonen Masamichi Takahashi David Verrill Manfred Vogel Christoph Von Arb Ben Waber Andrew Westerdale Stephanie Woerner JoAnn Yates Wayne Yuhasz Qiaoyun Yun Xue Zhang Antonio Zilli Kang Zhang Yan Zhao Kevin Zogg
  11. 11. Dimensions High-performing Low performing Remedy Condor Metric Strong leaders Clear leaders are well connected to other actors Leaders are not recognizable from the outside Self-selected or designated leaders need to become more active Betweenness centrality Degree centrality Responsiveness Prompt and consistent response indicates high engagement; enables fast problem-solving Slow response time is a consistent predictor of dissatisfied and non- motivated team Team members need to be made aware of importance of speed Average Response Time (ART) Rotating leadership Leadership rotates between whoever is best qualified for a task No leaders recognizable, or one person usurps leadership Delegate responsibility, nurture new talent Oscillation in group/actor betweenness centrality Initiative Proactive communication between parties; initiative on different leaders’ side. Flood of e-mails from always the same sources swamps team with e-mails Non-active team members should be encouraged to contribute Average Weighted Variance in Contribution Index (AWCI) Sentiment Consistently well-balanced sentiment signals fair and fact-based discussion Uneven and overly positive (exaggerated) and overly negative sentiment Don’t use overly positive or overly negative language, stick to the facts. Sentiment score