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Eleven interesting questions about data science / big data

What are your thoughts on this? What would be your answers?  

Here's my list of questions:

  1. What best practices do you recommend, when starting and working on enterprise analytics projects?
  2. How do you see data science and exploitation of big data evolve, over the next 5-10 years?
  3. What are the bottlenecks and other issues that prevent analytic projects from reaching their full potential?
  4. Data science is performed by consultants (big and small firms, independent consultants), data scientists employees on payroll, software / vendors such as SAS, and outsourcing to vendors offering automated, generic or semi-customized solutions (Google Analytics or Accenture for instance). How do you see this ecosystem evolves over time?
  5. What areas of data science, big data, and analytics are best handled by consulting firms, versus software vendors, in-house solutions and outsourcing?
  6. Is US still dominant in terms of creating new solutions and innovation in big data, are other countries catching up?
  7. Do you think that one of the next big innovations will be the creation of automated, robust, black-box data science that non-experts can use?
  8. What are the biggest challenges of big data? Creating / collecting the right data? Identifying / blending multiple external data sources? Using specialized statistical big data tools to avoid predictive mistakes caused by spurious correlations? Working with the right team or vendor or recruiting / training talent? Communication between tech people and executives? Understanding what the problems are? Adapting fast enough to evolving targets and competitive landscape?
  9. Is it true that it is better / easier to train an engineer / software engineer / data plumber (with domain expertize) to learn the statistical science needed for your project, rather than train a statistician (generalist) to understand your business and write production code to process big data?
  10. One percent of data users consume 99% of the data. Do you agree with this?
  11. What is the most efficient way to become a real data scientist, based on your career path and history? 

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