Se ha denunciado esta presentación.
Se está descargando tu SlideShare. ×

data scientist the sexiest job of the 21st century

Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio

Eche un vistazo a continuación

1 de 32 Anuncio

Más Contenido Relacionado

Presentaciones para usted (20)

A los espectadores también les gustó (20)

Anuncio

Similares a data scientist the sexiest job of the 21st century (20)

Anuncio

data scientist the sexiest job of the 21st century

  1. 1. Data Scientist – ‚The sexiest job of the 21 century‘ Frank Kienle | @byanalytics_en
  2. 2. 2008: Founded by CERN Data Scientists Since 2011: Award- winning retail solutions 2014: International expansion, predictive applications
  3. 3. Blue Yonder History 2008 2011 2012 2013 2014 Founded Karlsruhe & Hamburg with a team of 15 Re-branding to Blue Yonder Cyber One Award RetailTechnology Award Top Retail Product Award Data Mining Cup BlueYonder UK Forward Demand 1.0 Data Science Academy Finalist: Entrepreneur of theYear 2012/13 BlueYonder Platform Internet ofThings Award RetailWeek Supply Chain Award 150 employees
  4. 4. •Individual product predictions for more than 700 locations •35 million product- location combinations •30.000 decisions per second •300 million data sets evaluated per week •5 billion individual forecasts annually •20% reduction in surplus stock •2 million article returns avoided •14% reduction in write-off-rate •9.5% reduction in tied-up capital •1.3% increase in sales due to increased item availability •€40 million sales increase 1 Predictive Applications at Scale 2 3
  5. 5. Europe’s largest team of PhD- level data scientists among Blue Yonder’s 150 employees.
  6. 6. Data Scientist: finding the gold nuggets in big data - extracting value out of data
  7. 7. Data Scientist: extracting value out of data - its all about predictions
  8. 8. More than half of the apps on a typical iPhone home screen are predictive applications.
  9. 9. Predictive Applications Foresight Hindsight Strategy Execution Predictive Analytics Business Intelligence Dashboards & Visualization Predictive Applications
  10. 10. — Thomas J. Watson Sr* (CEO IBM 1943) “I think there is a world market for about five computers.”
  11. 11. — Niels Bohr “Prediction is very difficult, especially if it's about the future.”
  12. 12. Data Scientist: extracting value out of data - its all about predictions
  13. 13. Programming/ Technology Statistics/ Mathematics Business/ Processes p(w|D) ⇠ p(D|w)P(w)
  14. 14. Data Scientist Skill/Mindset Programming • Programming is the process that leads from an original formulation of rules to executable computer programs • Its all about automatization Statistics • Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data • Its all about data Business • A business is an organization involved in the trade of goods or services to consumers • Its all about decisions p(w|D) ⇠ p(D|w)P(w)
  15. 15. Automation of gut feeling 2.0 Coding • automatization Business • decisions Danger Zone • first step: 
 simple rule, if this than that • next step: 
 add a rule/process to adjust • last step: 
 blocked by contradicting rules + =
  16. 16. Automation of modern art Coding • automatization Statistics • data Art Zone • solving problems which never occurs • defining new problems • we might need this + =
  17. 17. Gut feeling 2.0 gets proven wrong Business • decisions Statistics • data Theory Zone • traditional business research • new ideas how business should work in theory • proof me wrong! 
 by the way, I already updated my theory + =
  18. 18. Programming/ Technology Statistics/ Mathematics Business/ Processes
  19. 19. Team building
  20. 20. The key to becoming a better company are better decisions. The key to better decisions is using your own data.
  21. 21. Sensor Data Scientist — www.blue-yonder.com Frank Kienle
  22. 22. 1 Categorizing Analytics Descriptive • Focused on gathering and collecting data • Key challenges: data volume and data variety • Key outcome: hindsight • Examples: reports, dashboards • Answers“What happened?” Predictive • Focused on understanding and explaining data • Key challenges: data velocity and complexity • Key outcome: insight • Examples: prediction models • Answers:“Why did it happen and what will happen next?” Prescriptive • Focused on anticipating and recommending action • Key challenges: execution • Key outcome: foresight • Examples: decision support, predictive apps • Answers:“What should we do?” 2 3
  23. 23. — Jeff Bezos “Your margin is my opportunity.”

×