What are your thoughts on this? What would be your answers?
Here's my list of questions:
- What best practices do you recommend, when starting and working on enterprise analytics projects?
- How do you see data science and exploitation of big data evolve, over the next 5-10 years?
- What are the bottlenecks and other issues that prevent analytic projects from reaching their full potential?
- 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?
- What areas of data science, big data, and analytics are best handled by consulting firms, versus software vendors, in-house solutions and outsourcing?
- Is US still dominant in terms of creating new solutions and innovation in big data, are other countries catching up?
- 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?
- 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?
- 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?
- One percent of data users consume 99% of the data. Do you agree with this?
- What is the most efficient way to become a real data scientist, based on your career path and history?
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