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Big data & data science challenges and opportunities

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Big data & data science challenges and opportunities

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Even when most companies see the advantages of using more data in their decisions, few actually do. Why is that? A few ideas on challenges and opportunities for (middle-size) companies. Talk audience was an engineering association, where most people represented engineering-centric companies in Germany (often in manufacturing).

Even when most companies see the advantages of using more data in their decisions, few actually do. Why is that? A few ideas on challenges and opportunities for (middle-size) companies. Talk audience was an engineering association, where most people represented engineering-centric companies in Germany (often in manufacturing).

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Big data & data science challenges and opportunities

  1. 1. Big Data & Data Science - Challenges and Opportunities Jose Quesada, Phd Director @quesada, @dataScienceRetreat
  2. 2. Personal Background • PhD in Machine learning, researcher at top labs • Solving data problems for the last 15 years • Consultant on ‘customer lifetime value’ • Data scientist at GetYourGuide • Today, Director at Data Science Retreat
  3. 3. Who is in a data-driven organization?
  4. 4. Who wants to be in a data-driven organization?
  5. 5. “Companies that have embraced a data-driven culture—rating themselves substantially ahead of their peers in their use of data—are three times more likely to rate themselves as substantially ahead of their peers in financial performance” --The Economist Intelligence Unit x3
  6. 6. http://www.tableau.com/learn/whitepapers/economist-fostering-data-driven-culture
  7. 7. "Many of my clients are clearly aware of the importance of data, But they don't know where to start in terms of where they should focus to get the most value, as well as how to translate the data into actionable insight." Jerry O'Dwyer, a principal at Deloitte Consulting http://www.cio.com/article/2387460/business- intelligence/data-driven-companies-outperform-competitors- financially.html
  8. 8. Data Science Retreat mission “Making sure we (EU) don’t fall hopelessly behind the US when it comes to technology”
  9. 9. What challenges are companies facing (B2B, B2C)?
  10. 10. Challenge 1: obtaining data from the end user
  11. 11. Manufacturer Distributor Retailer End user
  12. 12. Manufacturer Distributor Retailer End user
  13. 13. Bad Example: Window maker • Real company in DE (name omitted) • No information about what their customers care about • No brand recognition by customers • Exposed to cheaper competitor entering the market any time
  14. 14. Good Example:
  15. 15. Bad Example: textbook publisher • Real companies (everywhere) • No idea how long it takes for their customer to consume each page of the textbook • No information about what their customers care about • No brand recognition by customers • Exposed to cheaper competitor entering the market any time
  16. 16. Good Example:
  17. 17. Challenge 2: Creating a data culture, where data _is_ the core, not a side product
  18. 18. Peter Drucker: ...culture eats strategy for breakfast
  19. 19. Challenge 3: Finding talent
  20. 20. Each job ad for data scientist on linkedin gets an average of 150 applicants!
  21. 21. Challenge 4: Open data silos, democratize access to data in the company
  22. 22. Set programs or partnerships in place to make employees more data- literate.
  23. 23. Challenge 5: Big Data hype
  24. 24. You don’t need to have big data to extract value from it. You can make better decisions with your data today. Certainly, you don’t need a Hadoop cluster to start!
  25. 25. Opportunities and actionable advice
  26. 26. 1: Measure your company’s data maturity "When was the last time you had to defend forecasts against actuals?“ Identify where you are on the Drake scale for data maturity. Aim to move your company one level up
  27. 27. The Drake scale for data maturity http://aadrake.com/the-kardashev-scale-of-data-maturity.html Type 1 Type 2 Type 3
  28. 28. The Drake scale for data maturity http://aadrake.com/the-kardashev-scale-of-data-maturity.html Type 1 Type 2 Type 3 Staying out of jail. No data roles
  29. 29. The Drake scale for data maturity http://aadrake.com/the-kardashev-scale-of-data-maturity.html Type 1 Type 2 Type 3 Business Intelligence, reporting, or similar team that may use spreadsheets
  30. 30. The Drake scale for data maturity http://aadrake.com/the-kardashev-scale-of-data-maturity.html Type 1 Type 2 Type 3 Chief Data Officer or similar role. Reporting and ad hoc requests previously handled by the BI team are now part of a self-service platform so any employee can analyze the data
  31. 31. 2: Identify what value you would like to get out of your data Types of value: •Decrease risk •Higher precision •Foster innovation
  32. 32. 3: Identify who in the company has the most to gain, form a coalition Since you need to change the culture of your company (not easy!), every stakeholder you can recruit helps Recruit people from outside the company if needed
  33. 33. Call to arms!
  34. 34. Data Science is a chaotic field and people don’t really know what they want (much less what they need)
  35. 35. Thank You! Check out our short courses: Deep Learning Scalable machine learning Big Data Business value --- Jose Quesada, PhD Director, Data Science Retreat @datascienceret me@josequesada.com

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