Arun: My Data Analyst Career Journey

Hi Arun, thank you so much for taking the time. Please introduce yourself to our site’s readers.

I am Arun Chandramouli. I am a Data analytics professional and I have about 9.5 years of experience advising Fortune 500 companies across retail, pharma, financial services, telecom, e-commerce industries to take actionable insights using their data.

I utilize tools like SQL, Tableau, DOMO, SPSS, Excel, Python, ML/AI techniques and problem-solving skills to achieve the same.

Currently, I work for Fulcrum Digital Inc as a Senior Data Scientist providing data science and analytics advisory support for a large fortune 100 payment services company creating predictive analytics solutions, python scripting, building dashboards for the business operations organization in DOMO and recently started doing data management for the PROD edge node servers in LINUX, providing optimization techniques in python, help manage process killing, assignment of permissions.

Can you share with us how you entered the data analytics industry, and tell us about your first data analyst role?

I started researching data analytics way back in 2011 during my first job as a research analyst at a procurement research consulting services company. This resulted in me pursuing a Master’s in Industrial Engineering and Operations Research from Suny Buffalo, NY where I pursued courses in Linear Programming, Discrete Optimization, Decision analysis, Design of experiments.

I actively networked on LinkedIn on a daily basis since the 3rd month of my Master’s program whenever I had some free time. That resulted in me securing a data analytics intern at eBay in San Jose.

My role during the internship involved identifying a potential list of sellers on eBay who would respond to a rebates campaign positively which would require them to increase their sales through eBay by 10%-20% for that particular quarter. In return they would need to pay less commission to eBay for every transaction resulting in a win-win situation for both the seller and eBay. This involved accessing the data in their databases using SQL, Excel to gather the data and generate a report and SPSS to load the data and use logistic regression to calculate the probability of a seller hitting a particular sales target based on historical data and other key variables. This project involved interacting with the Merchant development team, Account managers for the sellers and the other buyer and seller teams.

This project gave me a great experience as a kick start in the data analytics industry.

What is it that you've personally found most exciting about being a data analyst?

Personally, for me the most exciting part of being a data analyst is the ability to make an immediate impact on the business using my valuable analysis or at least alerting the business that something is going wrong.

I feel a strong sense of fulfillment when I am able to find patterns or generate valuable insights from the data. It gives a great platform to get an opportunity to interact with senior stakeholders in the company and develop close relationships with them thereby paving the way to promotions.

Growth in the data field is also very fast if you are able to learn the data well and utilize the tools to generate timely insights along with working with senior stakeholders and other team members collaboratively.

You seem to have had a very diverse experience, with roles across different industries (from retail, financial, consulting). Could you tell us a bit more about what your day to day looks like, and share how what you do drives insights and business decisions? How are you using data in you role, and what is the impact you’re able to achieve?

Most of the data roles involve interacting with the offshore teams and onshore teams together. So, finding suitable times either early in the morning or late in the evening is one of the key things as the work is equally split between the onshore and offshore teams.

Project roadmaps are created for every quarter with the level of importance of each project and priority of each project. Apart from this there are ad hoc requests from the business, marketing, sales, product teams. The work also involves working with engineering teams to make sure data is available in the backend.

Day-to-day work involves extracting the data from databases using SQL, creating and running stored procedures, creating dashboards in tableau, exploratory analysis and visualization in python, creating data pipelines /workflows using tools like Alteryx, making progress on planned projects and responding to ad-hoc requests from other teams on priority.

Final insights are usually provided as a dashboard in a visualization tool like Tableau and also PowerPoint decks to present key insights/takeaways along with relevant data evidence and visualizations.

An example of an insight generation and subsequent action taken by the business would be: Say there is a lot of returns for the products sold by a particular vendor on the company’s e-commerce platform with the reason being products being damaged on delivery the business works with the specific vendor to provide a more robust delivery solution and resolve them.

Throughout your career, what did you learn about using data insights to make an impact in these various organisations and industries? What would you say all your roles had in-common - i.e core, and on the other hand, did you have an experience where you really had to get outside the box and the usual path?

There are two things that I have learned so far in my data analytics career so far:

1) Make sure to have clean data - sometimes there is garbage data, missing data, outliers, wrong data. It is important to identify them at the beginning stages of the project. Otherwise, it would lead to very bad results/analysis in the end requiring you to repeat the analysis and also leads to you losing the trust from your manager and senior stakeholders.

2) One of the most important things is to make sure to define the problem /understand the problem from the stakeholder very well because if you don’t do that you tend to go in a different direction from what the stakeholder is seeking and it’s very difficult to get hold of senior stakeholders as their calendars are rough almost all the time and it would be very difficult to get back to the required analysis. So, making sure to communicate well, ask questions, and clearly update the senior stakeholders regularly is very essential to succeed in this field.

All my roles involved understanding the business, the business use case and then understanding the data tying it to the business, learning to identify bad and good data and then generating and testing hypotheses to solve business problems. All the roles required creating dashboards using visualization tools like Tableau to distribute it enterprise wide and are viewed on a regular basis by the senior stakeholders.

I remember a time when I had to push back on a meeting with a senior stakeholder as I was waiting on answers from other teams on the data and requested to push back the meeting with the support from my manager as I don't want to deliver a wrong analysis.

What are you currently doing to keep up with the ever-changing data analytics industry?

I am active on platforms like LinkedIn where I read the posts related to data and keep myself informed and also respond to the posts to add more value. I share content too on LinkedIn with regard to my expertise in Data analysis, Data science, data engineering, Machine learning and AI.

I also publish papers with regard to the latest trends and the projects that I have worked on in data in journals such as European Journal of Advances in Engineering and Technology, International Journal of Science and Research, Journal of Economics & Management Research, Journal of Artificial Intelligence and Cloud Computing. I will share the links to my papers at the end of this interview.

I have done a Postgraduate course in Machine learning and artificial intelligence from Great Learning in partnership with UT Austin. I am also planning to do a course on Gen AI and LLM with Analytics Vidhya.

You recently moved from a data analyst role, into a data scientist position - tell us more about the move (for example: what would you say the biggest difference on day to day responsibilities is, and how does the previous DA experience help you in your current role?)

All data roles in general are partners of the business. There is a lot of emphasis on being aligned with the business teams and strongly supporting them.
As a data scientist there is a lot of emphasis on building predictive models which involves doing Exploratory data analysis, feature engineering, building machine learning/AI models, model evaluation, deployment and maintenance.

But the key to all of these things is making sure the problem statement and the goal is understood along with ensuring the data cleaning and preparation are done in the best possible manner.

So being an experienced data analyst helped me in the areas of SQL, building visualizations using tools like Tableau, DOMO and also having strong connections with the business stakeholders and to deliver valuable timely insights which helped me be a well-rounded data scientist.

What recommendations would you give to someone who is looking to join the data industry and get their first full-time data analyst position?

For someone looking to join the data industry one should be strong at SQL by doing regular practice along with the ability to implement statistical tests and exploratory analysis in python. Even now Tableau is the most widely used visualization so it doesn’t hurt to practice building dashboards using open data sets in Tableau.

Read up on existing blogs/news articles on the fundamentals of retail analytics, pharma analytics, e-commerce analytics would be helpful to showcase their knowledge during the interviews.

A soft skill aspect is to have a keen sense of listening and then proposing ideas, the ability to ask valid/thoughtful questions and to be able to work in coordination with teams.

Anything you'd like to highlight, or add? Something we haven't covered, but you hear a lot?

In this field there is a need for continuous learning and being enthusiastic about generating valuable insights - a need to learn about the business as much as possible and have close partnership with the business teams.

There are two types of career paths in the field of data:

1) Working for consulting companies like Mu sigma, Fractal, EXL, McKinsey etc.
2) Working directly for product companies such as TESCO, Meta, Unilever, Pepsico, Google etc.

Choosing either of the two depends on what kind of career paths that you want to pursue as both provide different kinds of career paths to pursue. Consulting provides exposure to a variety of analytics projects across domains and industries while working with Product companies helps you gain a lot of knowledge about the product and grow well too. I feel that consulting is a bit stressful but might provide huge dividends in the future if you manage to stick through it.

An extra one: How do you see the increased availability of AI tools such as ChatGPT, Bard etc., impacting the typical data analyst role? Are you using AI tools to augment your thinking, analysis and overall work?

Yes, the increased availability of AI tools such as ChatGPT and Bard has made it possible for easier access to research and help with coding readily available which was previously done through hours of Google search and browsing through Stackoverflow questions and answers. But this does not mean we can forgo learning the concepts, trying to come up with pseudocode/concepts on our own because everyone is unique and has immense levels of creativity which would remain dormant if it is not used. Also to ensure the maximum usage of AI tools we need to be very strong in the subject and improve our critical thinking skills as they work only through our prompts.

This has been great, thank you again, Arun. Where could people see more of your work, and connect with you?

Linkedin: https://www.linkedin.com/in/arunchandramouli/

Some of my published work:

European Journal of advances in Engineering and Technology - Optimization of Flight Time for Innovative Rotorcraft Designs through Advanced Experimental Techniques

European Journal of advances in Engineering and Technology - Enhancing Analytical Maturity in IT Functions: A Case Study on Framework Development and Implementation

International Journal of Science and Research - Improving Customer Service with data driven models : A Telecommunications case study