EDF2012 Andreas Both - From data-driven startup to large company in a decade

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EDF2012 Andreas Both - From data-driven startup to large company in a decade

  1. 1. From data-driven startup to large company in a decade Dr. Andreas Both Head of Research and Development Unister GmbH Germany European Data Forum 2012 Copenhagen, June 6-7, 2012
  2. 2. Claim . . . challenges of Big Data and the emerging Data Economy and to develop suitable action plans for addressing these challenges . . .Slide 2 Dr. Andreas Both, Head of R&D, Unister
  3. 3. Claim . . . challenges of Big Data and the emerging Data Economy and to develop suitable action plans for addressing these challenges . . .Slide 2 Dr. Andreas Both, Head of R&D, Unister
  4. 4. Claim . . . challenges of Big Data and the emerging Data Economy and to develop suitable action plans for addressing these challenges . . .Slide 2 Dr. Andreas Both, Head of R&D, Unister
  5. 5. Claim . . . challenges of Big Data and the emerging Data Economy and to develop suitable action plans for addressing these challenges . . .Slide 2 Dr. Andreas Both, Head of R&D, Unister
  6. 6. Personal Retrospective . . .Slide 3 Dr. Andreas Both, Head of R&D, Unister
  7. 7. Personal Retrospective . . . . . . joining Unister, April 2010 eCommerce is challenging complexity of business everything has to work smoothly punishment comes quicklySlide 3 Dr. Andreas Both, Head of R&D, Unister
  8. 8. Personal Retrospective . . . . . . joining Unister, April 2010 some lessons eCommerce is challenging improve your business model complexity of business care about our customers everything has to work smoothly punishment comes quicklySlide 3 Dr. Andreas Both, Head of R&D, Unister
  9. 9. Personal Retrospective . . . . . . joining Unister, April 2010 some lessons eCommerce is challenging improve your business model complexity of business care about our customers everything has to work smoothly rapide impact of decisions punishment comes quickly increasing data complexitySlide 3 Dr. Andreas Both, Head of R&D, Unister
  10. 10. It’s about the business activities, stupid. Should we really talk about data? Why should we talk about data processes? Why should we talk about the data analytics?Slide 4 Dr. Andreas Both, Head of R&D, Unister
  11. 11. It’s about the business activities, stupid. Should we really talk about data? Why should we talk about data processes? Why should we talk about the data analytics? Is it just about the business? Is data a core value?Slide 4 Dr. Andreas Both, Head of R&D, Unister
  12. 12. IT companies vs. traditional business classic business e-business need natural ressource need data to establish processes to establish processes depending on location independent from locationSlide 5 Dr. Andreas Both, Head of R&D, Unister
  13. 13. IT companies vs. traditional business classic business e-business need natural ressource need data to establish processes to establish processes depending on location independent from location SME will find many niches within the market.Slide 5 Dr. Andreas Both, Head of R&D, Unister
  14. 14. IT companies vs. traditional business classic business e-business need natural ressource need data to establish processes to establish processes depending on location independent from location SME will find many niches within the market. . . . establish large companies too!Slide 5 Dr. Andreas Both, Head of R&D, Unister
  15. 15. Unister’s Success Story Internet startup mainly located in Leipzig, Germany private limited company (German GmbH) founded in 2002, 5 founders managing director: Thomas Wagner aSlide 6 Dr. Andreas Both, Head of R&D, Unister
  16. 16. Unister’s Success Story Internet startup mainly located in Leipzig, Germany private limited company (German GmbH) founded in 2002, 5 founders managing director: Thomas Wagner eCommerce company, B2C national and international activities > 40 web-portals and services > 13.22 mio. unique user / month in Germanya a AGOF e.V. / internet facts 2012-01Slide 6 Dr. Andreas Both, Head of R&D, Unister
  17. 17. Unister’s Success Story Internet startup mainly located in Leipzig, Germany private limited company (German GmbH) founded in 2002, 5 founders managing director: Thomas Wagner eCommerce company, B2C national and international activities > 40 web-portals and services > 13.22 mio. unique user / month in Germanya IT-driven approach a AGOF e.V. / internet facts 2012-01Slide 6 Dr. Andreas Both, Head of R&D, Unister
  18. 18. Unister’s Success StorySlide 7 Dr. Andreas Both, Head of R&D, Unister
  19. 19. Unister’s Success Story Number of Employees 1530 1157 Employees 701 372 185 106 7 38 1 2003 2004 2005 2006 2007 2008 2009 2010 2011Slide 7 Dr. Andreas Both, Head of R&D, Unister
  20. 20. IT companies Data is the foundation getting data is tough having data is not enough managing data is challengingSlide 8 Dr. Andreas Both, Head of R&D, Unister
  21. 21. IT companies Data is the foundation getting data is tough having data is not enough managing data is challenging Next parts of the talk Data Access Data Integration (Big) Data AnalysesSlide 8 Dr. Andreas Both, Head of R&D, Unister
  22. 22. IT companies Data is the foundation getting data is tough having data is not enough managing data is challenging Next parts of the talk Data Access ← Steps a start-up has to tackle! Data Integration (Big) Data AnalysesSlide 8 Dr. Andreas Both, Head of R&D, Unister
  23. 23. IT companies Data is the foundation getting data is tough having data is not enough managing data is challenging Next parts of the talk Data Access ← Steps a start-up has to tackle! Data Integration Steps Unister had tackled. (Big) Data AnalysesSlide 8 Dr. Andreas Both, Head of R&D, Unister
  24. 24. IT companies Data is the foundation getting data is tough having data is not enough managing data is challenging Next parts of the talk Data Access ← Steps a start-up has to tackle! Data Integration Steps Unister had tackled. (Big) Data Analyses Steps Unister has to tackle.Slide 8 Dr. Andreas Both, Head of R&D, Unister
  25. 25. Data AccessSlide 9 Dr. Andreas Both, Head of R&D, Unister
  26. 26. Data Access Observations business models need access of data support, description, enrichment, . . .Slide 10 Dr. Andreas Both, Head of R&D, Unister
  27. 27. Data Access Observations Unister’s Success (step 1) business models need access was capable of integrating of data many data sets support, description, user-focussed data enrichment, . . .Slide 10 Dr. Andreas Both, Head of R&D, Unister
  28. 28. Data Access Observations Unister’s Success (step 1) business models need access was capable of integrating of data many data sets support, description, user-focussed data enrichment, . . . eCommerce demand local information link to local events (legal) contraints → such data not availableSlide 10 Dr. Andreas Both, Head of R&D, Unister
  29. 29. Data Access Observations Unister’s Success (step 1) business models need access was capable of integrating of data many data sets support, description, user-focussed data enrichment, . . . Challenges eCommerce demand Open Data should be local information established link to local events Standards have to be defined (legal) contraints and followed → such data not availableSlide 10 Dr. Andreas Both, Head of R&D, Unister
  30. 30. Data Access: Needed Actions Open Data initiatives need support enhanced tool support political commitmentSlide 11 Dr. Andreas Both, Head of R&D, Unister
  31. 31. Data Access: Needed Actions Open Data initiatives need support enhanced tool support political commitment will establish (local) data economics locally connected companies could growSlide 11 Dr. Andreas Both, Head of R&D, Unister
  32. 32. Data IntegrationSlide 12 Dr. Andreas Both, Head of R&D, Unister
  33. 33. Data Integration Observations bread and butter of data-driven companies needs much effortSlide 13 Dr. Andreas Both, Head of R&D, Unister
  34. 34. Data Integration Observations Unister’s Success (step 2) bread and butter of fusion of different data sets data-driven companies leads to good user experience needs much effortSlide 13 Dr. Andreas Both, Head of R&D, Unister
  35. 35. Data Integration Observations Unister’s Success (step 2) bread and butter of fusion of different data sets data-driven companies leads to good user experience needs much effort eCommerce demand matching processes integration toolsSlide 13 Dr. Andreas Both, Head of R&D, Unister
  36. 36. Data Integration Observations Unister’s Success (step 2) bread and butter of fusion of different data sets data-driven companies leads to good user experience needs much effort Challenges eCommerce demand establish distributed matching processes knowledge base integration tools Linked Data paradigm NOSQL + SQLSlide 13 Dr. Andreas Both, Head of R&D, Unister
  37. 37. Data Integration: Needed Actions support Linked Data Cloud companies need sound and solid data setsSlide 14 Dr. Andreas Both, Head of R&D, Unister
  38. 38. Data Integration: Needed Actions support Linked Data Cloud companies need sound and solid data sets research on scalable data integration processes Cloud Computing → Big Data challengeSlide 14 Dr. Andreas Both, Head of R&D, Unister
  39. 39. Data Analyses and Big DataSlide 15 Dr. Andreas Both, Head of R&D, Unister
  40. 40. Data Analyses and Big Data Observations disseminate, understand and ultimately benefit from increasing volumes of data example: social networksSlide 16 Dr. Andreas Both, Head of R&D, Unister
  41. 41. Data Analyses and Big Data Observations Unister’s Success (step 3) disseminate, understand and defining data analysis ultimately benefit from processes with impact increasing volumes of data pareto-optimal processes lead example: social networks to good coverage analysis came to a limit because of many segmentsSlide 16 Dr. Andreas Both, Head of R&D, Unister
  42. 42. Data Analyses and Big Data Observations Unister’s Success (step 3) disseminate, understand and defining data analysis ultimately benefit from processes with impact increasing volumes of data pareto-optimal processes lead example: social networks to good coverage analysis came to a limit because of many segments eCommerce demand descriptive analyses processes higher-level process interfaces good developersSlide 16 Dr. Andreas Both, Head of R&D, Unister
  43. 43. Data Analyses and Big Data Observations Unister’s Success (step 3) disseminate, understand and defining data analysis ultimately benefit from processes with impact increasing volumes of data pareto-optimal processes lead example: social networks to good coverage analysis came to a limit because of many segments Challenges eCommerce demand ... descriptive analyses processes higher-level process interfaces good developersSlide 16 Dr. Andreas Both, Head of R&D, Unister
  44. 44. Data Analyses and Big Data: Global Movement source: ucsd.eduSlide 17 Dr. Andreas Both, Head of R&D, Unister
  45. 45. Data Analyses and Big Data: Challenges source: hadapt.comSlide 18 Dr. Andreas Both, Head of R&D, Unister
  46. 46. Data Analyses and Big Data: Challenges The 3 V Volume Varity VelocitySlide 19 Dr. Andreas Both, Head of R&D, Unister
  47. 47. Data Analyses and Big Data: Challenges The 3 V Volume Varity Velocity The +2 V Virality ViscositySlide 19 Dr. Andreas Both, Head of R&D, Unister
  48. 48. Data Analyses and Big Data: Needed Actions handle this is all about knowledge . . .Slide 20 Dr. Andreas Both, Head of R&D, Unister
  49. 49. Data Analyses and Big Data: Needed Actions handle this is all about knowledge . . . levels of challengeSlide 20 Dr. Andreas Both, Head of R&D, Unister
  50. 50. Data Analyses and Big Data: Needed Actions handle this is all about knowledge . . . levels of challenge need systems for big data analysesSlide 20 Dr. Andreas Both, Head of R&D, Unister
  51. 51. Data Analyses and Big Data: Needed Actions handle this is all about knowledge . . . levels of challenge need systems for big data analyses good support: Cloud Computing, . . .Slide 20 Dr. Andreas Both, Head of R&D, Unister
  52. 52. Data Analyses and Big Data: Needed Actions handle this is all about knowledge . . . levels of challenge need systems for big data analyses good support: Cloud Computing, . . . need people to operate on the systemsSlide 20 Dr. Andreas Both, Head of R&D, Unister
  53. 53. Data Analyses and Big Data: Needed Actions handle this is all about knowledge . . . levels of challenge need systems for big data analyses good support: Cloud Computing, . . . need people to operate on the systems → ok: some experience availableSlide 20 Dr. Andreas Both, Head of R&D, Unister
  54. 54. Data Analyses and Big Data: Needed Actions handle this is all about knowledge . . . levels of challenge need systems for big data analyses good support: Cloud Computing, . . . need people to operate on the systems → ok: some experience available need people to develop applicationsSlide 20 Dr. Andreas Both, Head of R&D, Unister
  55. 55. Data Analyses and Big Data: Needed Actions handle this is all about knowledge . . . levels of challenge need systems for big data analyses good support: Cloud Computing, . . . need people to operate on the systems → ok: some experience available need people to develop applications → bad: rarely teachedSlide 20 Dr. Andreas Both, Head of R&D, Unister
  56. 56. Data Analyses and Big Data: Needed Actions handle this is all about knowledge . . . levels of challenge need systems for big data analyses good support: Cloud Computing, . . . need people to operate on the systems → ok: some experience available need people to develop applications → bad: rarely teached need people to think about using the network effectSlide 20 Dr. Andreas Both, Head of R&D, Unister
  57. 57. Data Analyses and Big Data: Needed Actions handle this is all about knowledge . . . levels of challenge need systems for big data analyses good support: Cloud Computing, . . . need people to operate on the systems → ok: some experience available need people to develop applications → bad: rarely teached need people to think about using the network effect → very bad: Talent GapSlide 20 Dr. Andreas Both, Head of R&D, Unister
  58. 58. Data Analyses and Big Data: Potential Big data – The next frontier for innovation, competition, and productivity (McKinsey May 2011)Slide 21 Dr. Andreas Both, Head of R&D, Unister
  59. 59. Data Analyses and Big Data: Potential Big data – The next frontier for innovation, competition, and productivity (McKinsey May 2011) Plenty of possibilities!Slide 21 Dr. Andreas Both, Head of R&D, Unister
  60. 60. Summary: Most important activities Open Data will give a push well-developed tools are crucial for SME talent gap has to be tackledSlide 22 Dr. Andreas Both, Head of R&D, Unister
  61. 61. Conclusion Data Access, Data Integration, Data AnalysesSlide 23 Dr. Andreas Both, Head of R&D, Unister
  62. 62. Conclusion Data Access, Data Integration, Data Analyses Big DataSlide 23 Dr. Andreas Both, Head of R&D, Unister
  63. 63. Conclusion Data Access, Data Integration, Data Analyses Big Data successful companiesSlide 23 Dr. Andreas Both, Head of R&D, Unister
  64. 64. From data-driven startup to large company in a decade Dr. Andreas Both andreas.both@unister.de Head of R&D +49 341 65050 24496 Unister GmbH http://www.unister.deSlide 24 Dr. Andreas Both, Head of R&D, Unister

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