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What's the profile of a data scientist?

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Slide deck as presented by Dries Van Nieuwenhuyse during the ba4all event of May 19th 2015.

Published in: Data & Analytics
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What's the profile of a data scientist?

  1. 1. Data  Scientist  Profiles Tuesday  May  19th  2015 By  Dries  Van  Nieuwenhuyse Prof.  EHSAL  Management  School Researcher  at  BICC  Thomas  More Lecturer  Strategic  Management  HoGent
  2. 2. Agenda • Do  analytics  matter?  Of  course! • What’s  in  a  name:  do  we  really  need  a  new  term  ”data  scientist”  for  this? • Characteristics  of  data  scientists • How  do  we  recognize  them? • How  do  we  build  up  data  scientist  teams? • Feasible?
  3. 3. Do analytics matters? Of course! Who’s on Facebook? Who’s on LinkedIn? How much overlap is there in the PYMK suggestions? (20 – 40 – 60 – 80 – 100)
  4. 4. Do analytics matters? Of course! « Some companies have built their very businesses on their ability to collect, analyze, and act on data. Every company can learn from what these firms do. » Thomas H. Davenport, 2006. Competing on analytics. Harvard Business Review, January 2006 p99-107.
  5. 5. What’s in a name? Whether  employers  know  or  don’t  know  what  data   scientists  do,  they  have  been  using  -­‐ in  rapidly   growing  numbers  -­‐ the  term  “data  scientist”  in  job   descriptions  in  recent  years...
  6. 6. What’s in a name? What then are the data scientists, these new men and women of industry? o Are  they  scientists?   o Engineers?   o Programmers?   o Business  Controllers?   o Financial  Controllers?   o A  new  breed  of  business  decision-­‐makers  and  innovators?
  7. 7. What’s in a name? • Google‘s  chief  economist  Hal  Varian  commented  in  January  [2009]  that  the  next  sexy  job  in  the   next  10  years  would  be  statisticians.   By  statisticians,  he  actually  meant  it  as  a  general  title  for   someone  who  is  able  to  extract  information  from  large  datasets  and  then  present  something  of   use  to  non-­‐data  experts… • In  June  2009  in  a  blog  post  titled  “Rise  of  the  Data  Scientist”  by  Natahn  Yau,  a  PhD  candidate  in   statistics,  the  term  was  first  really  used.
  8. 8. What’s in a name? “What data scientists do is make discoveries while swimming in data… [their] dominant trait is intense curiosity - a desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested. This often entails the associative thinking that characterizes the most creative scientists in any field….” Thomas H. Davenport & D.J. Patil (October 2012). Data Scientist: The Sexiest Job of the 21st Century.
  9. 9. What’s in a name? A  data  scientist  is  an  engineer  who  employs  the  scientific  method  and  applies  data-­‐discovery   tools  to  find  new  insights  in  data.  The  scientific  method—the  formulation  of  a  hypothesis,  the   testing,  the  careful  design  of  experiments,  the  verification  by  others—is  something  they  take   from  their  knowledge  of  statistics  and  their  training  in  scientific  disciplines.  The  application  (and   tweaking)  of  tools  comes  from  their  engineering,  or  more  specifically,  computer  science  and   programming  background.  The  best  data  scientists  are  product  and  process  innovators  and   sometimes,  developers  of  new  data-­‐discovery  tools. Data Scientists: The Definition of Sexy (Gil Press) http://www.forbes.com/sites/gilpress/2012/09/27/data-scientists-the-definition-of-sexy/
  10. 10. What’s in a name? A  data  scientist  is  a  job  title  for  an  employee  or  business  intelligence  (BI)  consultant  who  excels  at   analyzing  data,  particularly  large  amounts  of  data,  to  help  a  business  gain  a  competitive  edge.   Margaret Rouse, WhatIs.com
  11. 11. How can we recognize a data scientist? • Very  different  backgrounds • Curiosity    beyond  day-­‐to-­‐day  activities • Bricolage  versus  engineering… • Questions  are  more  important  than  answers D.J. Patil (2011) Building Data Science Teams. O’Reilly Media.
  12. 12. Skills of a data scientist The significant problems we face cannot be solvedby the same level of thinking that created them. If  I  had  an  hour  to  solve  a  problem  and  my  life  depended   on  the  solution,  I  would  spend  the  first  55  minutes   determining  the  proper  question  to  ask,  for  once  I  know   the  proper  question,  I  could  solve  the  problem  in  less   than  five  minutes. A. Einstein
  13. 13. Skills of a data scientist • Finding  rich  data  sources • Working  with  large  volumes  of  data  despite  hardware,  software,  and  bandwidth  constraint • Cleaning  the  data  and  making  sure  that  data  is  consistent • Merging  multiple  datasets  together • Visualizing  that  data • Building  rich  tooling  that  enables  others  to  work  with  data  effectively D.J. Patil (2011) Building Data Science Teams. O’Reilly Media
  14. 14. Data scientist = BI professional? EMC (2011). Data Science Revealed: A Data-Driven Glimpse into the Burgeoning New Field.
  15. 15. Data scientist = BI professional? EMC (2011). Data Science Revealed: A Data-Driven Glimpse into the Burgeoning New Field. • BI  professionals  focus  on  qualitative  visualization  of  existing  business  data • Data  scientists  apply  advanced  analytical  tools  to  generate  predictive  insights • More  communication • More  scientifically  trained   • Introvert  – Extravert • Bricolage  -­‐ Engineering
  16. 16. Do data scientists need a PHD?
  17. 17. Changing role of controllers • Gradient  between  Flexibility  and   Control • Gradient  between  Internal  and   External  focus • Finance  is  moving  in Cornel, Renes & Vervuurt (2013). Controllers - Fit for the future. MCA
  18. 18. Can BI professionals become data scientists? • Nowadays  everyone  wants  to  become  proactive,  analytical   and  strategically  aligned • Will  they  all  succeed  in  this  mental  shift? • Of  course  they  won’t  all  succeed • So  there  is  plenty  of  room  for  talent • Obviously  a  good  understanding  of  the  domain  of   Performance  MANAGEMENT  in  compleness  will  play  a   pivotal  role  in  this  change
  19. 19. Can BI professionals become data scientists? • Sure  they  can!!! • Own  research  shows  that  personal  traits  are  in  the  end  prevailing • Not  everyone  needs  to  kick  off  the  party • Soft  skills  are  more  important  than  hard  skills • Hard  skills  are  more  obvious  to  learn • Diversity  of  multi-­‐disciplinary  teams  is  more  important  than  individual  skills • Check  for  proactive  and  creative  thinking
  20. 20. How to recruit? • Focus  recruiting  the  ‘usual  suspects’  – (commercial)  engineers,  hard  scientists,  big  data  guru’s,   bricoleurs • Scan  memberships  and  active  people  in  analytical  and  decision-­‐making  communities • Steal  talent  from  Finance,  IT,  Marketing • Actively  scan  LinkedIn • Organize  a  contest  for  data  science  open  to  all  profiles • Did  the  candidate  publish  in  magazines,  books?
  21. 21. How to recruit? • Test  for  creativity  – ask  for  a  possible  research  agenda  for  your  company  testing  whether  they  are   actually  prepared • Organize  for  continuous  job  satisfaction  and  spontaneous  evolution  through  the  organization • Let  candidate  work  on  a  data  set  for  a  day,  come  up  with  proper  questions  and  answers  and  let  the   candidate  present  and  convince  an  audience  of  decision-­‐makers • Avoid  overskilled  and  overtechnical  PhD’s  that  can’t  communicate
  22. 22. Doesn’t matter who takes the lead... just get started “Don't wait until everything is just right. It will never be perfect. There will always be challenges, obstacles and less than perfect conditions. So what. Get started now. With each step you take, you will grow stronger and stronger, more and more skilled, more and more self-confident and more and more successful.” Mark Victor Hansen

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