4 min read
Back in early 2012, the UK-based bank I was working for decided to begin outsourcing a significant portion of it's technology and analytics capability to India. I was contracting with them as a senior data analyst under the umbrella of a BI and marketing strategy company I'd founded the year before with two other colleagues.
Seeing as the economy in the UK and Ireland was still feeling major repercussions from the Global Financial Crash in 2008, we were somewhat concerned about what that would mean for our own immediate futures.
We needn't have worried. What we got instead over the years was an introduction to a long list of excellent analysts who helped us all grow as data professionals.
This week's guest was one of the first of these new India-based analysts to join our team. And it's been great for me to catch up with him and see what he's been doing since we last worked together around six years ago.
Welcome to Analysts Assemble, Manpreet Singh.
Tell us a bit about yourself, how did you get into the data space and what does your data journey look like so far?
Always being curious about mathematical tools and the way they work during school days and later on wanting to dig deeper into its real life applications going forward, I got drawn towards studying Economics in my graduate and post-graduate level education. There I enjoyed econometrics, mathematical economics, and statistical analysis.
Although I graduated from one of the best places for economics in India (First Economic Research of Country), there were no immediate placements allowed (as the Institute wanted everyone to go for a PhD). I still got lucky to start as a research assistant in another great research institute that had state of the art facilities and computing labs to get me started.
Back in those days basic data management in SAS and knowing proc logistic could land you with a nice office job in MNCs ,so I focused on learning those few then niche skills and before I was an expert I already had a job with GENPACT working for premier GE clients.
I think a good data scientist will need to have 3 very basic skills:
These three basics of analytics have been constant throughout, its just that the tools are always evolving and changing with time.
Back in early 2000's it was SAS, VBA and SQL. Today there are open source tools like PYTHON, R (Opensource), Cloud, Tableau, Qlikview etc. for visualization.
At Google we have all sorts of inhouse tools like Google Trix and Google Docs. Google has it's own version of every tool you can think of.
Having traveled this long journey of data analytics, I have worked across domains and for some of the best of businesses. Companies ranging from GE, RBS, Whole Foods to Google, and across domains including Industrial Manufacturing Analytics, Retail, Digital Marketing and BFSI.
This has helped me to envision how data flows from a ground level sensor to a SCADA system in manufacturing. Or from a POS transaction to weekly targeted marketing campaign. It shows how these databases can be married to create a single source of truth to answer all customer questions in every business.
What’s a typical day look like for you in your current data role? Which tools and languages do you use? Big team/small team/lone wolf? Remote/office based/co-working space?
I am heading India Analytics for a global major in Renewables Energy headquartered in Singapore. My role is a rich mix of Vendor Management, Strategic Decision making and delivering on top leadership expectations (Global CTO & India Head).
My work week starts with taking a stock of things on timelines and then working with the vendor partners throughout the week to deliver on committed timelines. That means over other routine things keeping a watch on sudden surprises coming our way that may impact the team works and keeping them in check.
I'm mostly interacting with my team at remote site locations: data scientists in Singapore and some remote vendor teams in India working on various new, newer and upcoming tools and techniques.
These include Python, to cloud to IOT data handling from remote wind turbines, solar panals and generators in thermal plants.
You've been active sharing your progress on LinkedIn. How important do you think it is for data professionals, at all stages of their career, to share publicly what they are doing and learning?
LinkedIn can be leveraged to share high level details ensuring confidential client and business information is not compromised.
The right people can always track you and understand that you are the right fit for their requirements if you share information smartly.
Where do you see your own data career going next? Building on your technical skills or moving into a more management-based role?
After a level (10-15 yrs) you are bound to get into senior/key roles as the organizations can always find resources to do your role at 1/10th the cost. Also the experience you have gathered over years helps you to do justice to the strategic roles you get into.
However, you need to keep pace with the changing technology in industry and always keep yourself abreast with the latest.
If you had a list of “best-kept-secrets” (websites, books, coaches) that have helped you, which would you recommend? What is the number one piece of advice you give to aspiring data analysts?
Everything is open source so there is no secret.
The key is to stick with something over time and trying to do it again and again till you master it. It takes about 10,000 hours to master a skill and takes about 10 years of time to put that kind of effort.
There is nothing like right or wrong idea in technology and business, just focus on the present and do the things right and optimally. There are times when things go south when you need to do even better, that is how you get there eventually.
Where can readers find you online?
Yes, I have my web footprint. I run my page called Hadooping on Facebook with around 1000+ followers and I also keep posting on LinkedIn at times.