Compassionate and Data-Driven: Reflections with Ashish Agarwal
MPOWER boasts being “compassionate and data-driven” as one of our core values, but what does that really mean? We sat down and explored the topic with MPOWER Operations Analyst Ashish Agarwal.
Ashish began working at MPOWER in the summer of 2016 through the Hisaoka Fellowship offered by the Dingman Center at the University of Maryland. He is currently pursuing two graduate degrees there, an M.B.A. and an M.S. in Information Systems. Ashish has 7 years’ experience working in business analytics and data-driven strategy in India. He is also an international student with first-hand experience of the challenges non-domestic students face when seeking financial support to study in the United States. After graduation, Ashish will be working full-time as MPOWER Financing’s first Manager of Analytics.
You have a lot work experience in a variety of settings. What in particular drew you to MPOWER Financing?
I was drawn to MPOWER for many reasons.
First, I sincerely believe in MPOWER’s mission. Barriers should not exist between good students and quality education. Period.
Second, our team is passionate about making lasting, positive impact. And yes, the work is absolutely rewarding, but it’s even better when collaborating with talented, like-minded people.
Finally, I love the dynamic work environment. Start-ups are unique unto themselves and optimal for those who get bored easily. You work with everyone and do a bit of everything, and every project is vital to the overall success of the business. Fighting bureaucracy, monotony, or a rigid job description are non-issues here.
We’re global nerds who care, work hard, and have fun, and I’m happy to be a part of the team.
What is the best part about working at MPOWER?
How we value empathy. Unlike the standard financial institution model, compassion is an important factor in our decision-making process. And on top of that, we make sure it’s always balanced with a data-driven approach.
I admire our mission and authentic belief in providing financial aid and opportunities for students around the world. Not only is our team highly qualified, but every person also has experience attending school in the United States as a foreign student or with assisting international students through financial inclusion programs.
“Big Data” has become a huge buzzword. Why is data analytics important?
Data is the future of the business world. The availability of data, increase in processing power, and advent of cloud computing have changed the business landscape. Anyone can now obtain data — it’s everywhere and it’s fascinating.
Ten, 20 years ago, solid work experience was the holy grail of the workplace. Companies that made data-driven business decisions were at a distinct competitive advantage.
Now, companies not using data are systematically left behind. Even typical qualitative jobs, such as human resources, require basic data manipulation. Experience is still important, but it alone is no longer enough to be competitive. Possessing both work experience and data manipulation skills has become a key differentiator for both job-seekers and companies.
What advice do you have for students or professionals seeking to pursue or dabble in data analytics?
Data analytics is a huge field — it’s very easy to get lost without an end goal. No one has the time to master everything. So first, I recommend figuring out what you want and what your career goals are. Are you interested in solving real-world business problems, or working more on the technical side? What’s your preferred job function?
If your strength is with technology and programming, you could learn a programming language to record your work and share it with others. One great option is R — it’s suitable for both data analysis and statistics. Additionally, there’s RStudio, which is a visual interface for writing code to crunch numbers and draw graphs with the R programming language. Above all else, R and RStudio can make the process of learning data analysis easier. Tableau and Hadoop are also great programs to play around with, and there are hundreds of tutorials online to get you started.
If programming isn’t your best skill, try to build functional competencies in areas such as finance or marketing. Find out how analytics is commonly used in your preferred job function. If you’re in marketing, you can learn how Google Analytics works without learning any programming.
It’s important to remember that learning how to present your data is just as important as analyzing it correctly. If you can’t explain it to others — coworkers, your boss, the C-suite — your ideas will never be implemented. Visualization tools such as Tableau have become absolutely necessary for data visualization and for making engaging presentations.
How do you do data analytics the right way?
Data analytics is so much more than theoretical calculations and complicated programming. By themselves, data analytics and math are useless. Great data analysis involves critical thinking, problem solving, and a careful thought process.
Before whipping out your calculator or favorite program, you need to understand all the angles of the problem you are trying to solve. Next, you need to decide what data would answer your question. Once these steps are complete, you can perform the analysis.
Finally, FINALLY, you can draw conclusions from your work. This step must come last. All humans have inherent biases. If you aren’t careful, it’s easy to obtain the wrong data, solve the wrong problem, and/or manipulate the data to show your unconscious desired conclusion. Best-case scenario, your data is useless. Worst-case scenario, your results push your company in the wrong direction.
Our compassion drives us to be objective about our data. We know that biases exist, and we want to be 100% sure that our product is right for our customers.
In both data and in life, save your conclusions for last.
Are you compassionate and data driven? MPOWER is now hiring for full-time jobs and internships.
An insightful write-up for those wanting to take data analytics as career. I particularly liked the part about how Ashish has connected a rather philosophical part of life ‘that of being careful with your biases before choosing to solve any problem’ and rightly said if there is a bias the diagnosis of the problem itself would be wrong and could be most dangerous for any organisation. This is a must read for someone or rather anyone wanting to begin their tryst with data analytics.