Interview: Beena Ammanath, GE on Data Science – It’s Not Just Science!
We discuss benefits and challenges of Data Lake, trends, life lessons, motivation, desired skills, and more.

Beena has over 23 years’ experience in the data arena with a number of international organizations including British Telecom, E*trade, Thomson Reuters and Silicon Valley startups in engineering and management positions. She holds a Masters in Computer Science and MBA in Finance.
First part of interview
Here is second and last part of my interview with her:
Anmol Rajpurohit: Q4. What are the major challenges in building and maintaining an industry-grade DataLake?
Beena Ammanath Historically, you think about solving things through traditional data warehousing concepts and business intelligence, but with this push of IoT, or the industrial internet, there’s this whole thought process around the information coming off of machines. Being able to scale to support the amount of information that we were seeing coming from the OT space, or operational technology, the technology that relates to power plant, or a rail provider. That sort of backdrop has driven us to think about it in a way we haven’t had to before.

AR: Q5. Out of the current Big Data trends, which ones do you find particularly interesting and promising (with respect to future potential)?

AR: Q6. What are the top lessons that you have learned from your Data Science experience so far?
BA: One of the biggest lessons I have learnt is that data scientists cannot operate in silos. Deep domain expertise is key in generation of robust algorithms. “Know Thy Data”. Data Science is not just Science – it is a combination of creativity, curiosity, story-telling and science – being able to grasp the wider context to solve real business problems - the ability to understand the meaning of the data sets when applied to a machine operating in the real world and make the machine smarter.
AR: Q7. What motivated you to work in Data Analytics?

Also, this is a constantly evolving area and there is so much more to learn. I don’t think there’s anything more satisfying than identifying hidden patterns in data, solving the puzzle and then using the solution to drive impactful outcomes.
AR: Q8. What resources do you rely on to keep yourself updated with the advancements in Data Science?

AR: Q9. What do you consider as the most desired qualities in practitioners in the field of Data Science?

AR: Q10. What was the last book that you read and liked? What do you like to do when you are not working?

When I am not working, you will most likely find me at the baseball field with my two boys. I also like to read, fly planes and I am a big foodie.
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