Interview: Satyam Priyadarshy, Halliburton on Unlocking Success for Big Data Projects
We discuss Predictive Analytics in Oil & Gas industry, Big Data analytics, key drivers of success,common reasons of failure, trends, advice, and more.
Dr. Priyadarshy has appeared as speaker at several international conferences, and has written, co-authored, presented and published numerous research papers in peer-reviewed journals and magazines. He has held various leadership positions in AOL, Network Solutions, Acxiom Corporation prior to joining Halliburton. He was recently named to The Financial Times’ list of potential board candidates with emerging technology and analytics expertise. Dr. Priyadarshy is an adjunct faculty at Georgetown University. He is currently senior fellow at the International Cyber Security Center at George Mason University. He is advisory board member at multiple organizations including Big Data Summit, Virginia Tech’s MBA Board, etc.
Dr. Priyadarshy holds a Ph.D. from Indian Institute of Technology in Bombay, and an MBA from Virginia Tech.
First part of interview
Here is second and last part of my interview with him:
Anmol Rajpurohit: Q5. How do you assess the current maturity level of Predictive Analytics in Oil and Gas in-dustry? What major developments do you expect in the next 2-3 years?
AR: Q6. How do you differentiate between traditional analytics (KPI focused) and Big Data analytics?
AR: Q7. What are the key drivers of success in today's Big Data projects? What are the most common reasons behind failure?
SP: The explosion of TAPS (Technologies, Applications, Products and Solutions) is one of the key driver for success in Big Data projects. For the data that one’s enterprise has, it is possible that various combinations of these agile TAPS will work to find new patterns and discoveries. One does not have to restrict to a fixed number of these TAPS to gain value.
The reasons for failure are few:
- The cultural and data silos within the organization
- The lack of knowledge and patience at the leadership level - Big Data analytics is about finding answers and new patterns and then asking the question why it is there. A large number of leaders are trained in traditional way of business intelligence, asking for answers for a question they have
- Agility in adapting emerging technologies
AR: Q8. Which of the current trends in Big Data arena are of great interest to you?
SP: Some of areas of interest from value creation point of view are:
- Real-time predictive models using streaming big data technologies
- The in-browser highly interactive information and scientific visualizations using Javascript and newer technologies on large volumes of data.
From a total cost of ownership point of view, a much cheaper storage that supports data-model free stor-age of massive amounts of historical raw data for easy access would be welcome technology. As the raw data size grows, the cost of traditional Hadoop and other platforms will become significant and not feasible for many businesses.
AR: Q9. What is the best advice you have got in your career?
AR: Q10. What key qualities do you look for when interviewing for Data Science related positions on your team?
AR: Q11. On a personal note, are there any good books that you have been reading lately, and would like to recommend?
SP: The books that inspire me originate from the land of Bharat, like the Hanuman Chalisa because it tells that a single entity can be successful in many areas, if they work sincerely for it.
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