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The Inspiring Story Of Hareesh G Who Transitioned From A Shop Floor Planner To Data Science Advisor At Dell EMC

The Inspiring Story Of Hareesh G Who Transitioned From A Shop Floor Planner To Data Science Advisor At Dell EMC

Prajakta Hebbar
Hareesh dell emc

From aerospace and marketing to analytics and data science, Hareesh G, Senior Advisor, Data Science, Performance Analytics Group at Dell EMC, has over a decade of experience in working with various facets in business. Now, he is a part of the team which has been working on high-impact projects around various aspects of business analysis for Dell EMC. Analytics India Magazine caught up with Hareesh and we discussed a wide variety of topics — from the greatest challenges in data science to its immense potential for India.

Analytics India Magazine: How did you start your career in data science? How has the journey been so far?

Hareesh G: The start of my career looked very different versus how it is today. About a decade ago, I started as a shop floor planner and manager in an aerospace manufacturing firm. Approximately four years into this job, I joined an MBA course in marketing and soon realised that it was not my cup of tea. At that time, the university I was studying in, formed an analytics stream in collaboration with an analytics firm. Although I had never imagined being in an analytics or a data science career, I took it up.

After my MBA, I started doing short-term projects apart from a regular internship in analytics. I also participated in some national analytics competitions that were running at that time. I liked this space and remained consistent. Later I joined Cognizant as an analytics lead. My skills in management and knowledge in analytics came handy to deliver multiple projects while I was there.



Today, I work as a data scientist at Dell EMC and focus on financial service transformation at the company. The potential of data continues to motivate me to remain in this field and always stay challenged.

AIM: Can you please share some of the data science-based solutions/projects that you have worked on and you found inspiring?

HG: I work for Performance Analytics Group (PAG) at Dell EMC. PAG is a global, central organization within the company that helps various business functions for their data and analytics needs. Being in a central organization, I get several opportunities to work with various business functions and hence on many exciting projects.


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One of the recent projects I would like to call out at Dell EMC is a high-impact project around ‘delayed payments’. Delayed payments cause a significant impact on the working capital of the company. To address this issue, we created a cross-functional program to minimize friction in ‘order to cash’ process. First, we identified breaks in purchase order patterns in various aspects of the order process like PO numbers, taxing discrepancies, reinvoicing etc. Next, we estimated the impact of each of these processes and discussed their corrective actions. The entire program resulted in significant dollar savings for Dell EMC in a short time. This solution is a rare combination of data science, business analysis, business process transformation and cross-functional collaboration that resulted in a huge business impact.

Another project that I worked on in my previous company was a recommendation engine that helped sales offer personalized solutions to the customers. We studied product purchasing behaviour of the customers to recommend solutions and also tell the reasons behind the suggested recommendations. The ’why’ of the recommendation engine was impactful since the call centre agents were more aware of the context while making recommendations to their customers.

AIM: Can you please share with us some specific use cases in data science that brought significant value to Dell?

HG: Apart from the delayed payments project I described above, I would like to talk about another ongoing project we have done for the financial service organization at Dell EMC.

Detection of fraud transaction is another area that brought significant value to the company.  My team and I proactively generated transaction profiles that have a high probability of committing fraud. We further reviewed these profiles and developed a tool that has the capability to recognize fraud transactions in real-time. This project is an ongoing initiative to minimize losses and create a significant business impact  

AIM: Do you think India has embraced Data Science to its full potential?

HG: Data Science is labelled as the sexiest job of the 21st century and is growing fast in all parts of the globe. While there is a lot that can be done, I think India has taken big leaps in this area.

Central and various state governments have given adequate recognition to data science and are working on framing an Artificial Intelligence (AI) policy. There is significant work happening in this space:

  1. Encouraging citizen data scientists: Through National Data Sharing and Accessibility Policy, non-sensitive  data from different government institutions is made accessible to users, to enable them to tap into community wisdom
  2. Corruption free system: Identifying fraud financial transactions through AI has enabled crackdown on dubious companies. Tata Power has launched an AI solution to detect power theft. Power theft is a rampant problem in India.  We have the technology and data; the next step is to scale it at the national level
  3. Effective governance: NITI Aayog’s website has a tableau dashboard that gives the real-time performance of 115 aspirational districts. Real-time tracking is also done for the electrification program. These are significant milestones in implementing government schemes
  4. Agriculture: Pilot program on precision agriculture is being conceived in around 10 districts. Once we start getting the data from fields, data science can help answer critical questions we face today

AIM: How do you think India can adopt data science in governance and policies?

HG: Data Science can completely change the way we approach our problems and opportunities. A few simple ideas on how we can implement data science in our governance and policies are as follows:

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  1. Monitor performance of all government functions to take corrective actions
  2. Address the gap between citizens’ expectations and governments delivery with help from data collection and analysis
  3. Gather insights from millions of responses received by citizens during the formulation of critical policies
  4. Deployment of AI-powered bots across government services for efficient and automated delivery

The above suggestions are just a start. Data Science can do wonders for how we operate as a nation.

AIM: What are the most significant challenges that you face, being at the forefront of data science space?

HG: Every profession has its own challenges and ours is no different. Availability of ‘right data’ is the most common problem. A data scientist cannot build a self-learning model without correct information.

Another challenge we come across is the ability to explain complex models. It takes a combination of great storytelling skills for data scientists and the team members’ ability to understand data; to be able to conclude how they can together make the best use of the machine learning models at hand.  

From a talent perspective, getting data scientists with the right skills as team members is not easy.  There is a scarcity of talent in the market which has the right mix of business, statistical and programming knowledge.

AIM: How do you think data science and analytics as an industry is evolving? Could you tell us the most important contemporary trends that you see emerging in the across the globe?

HG: As I already mentioned, data science has taken big leaps and is here to grow. Some of the key trends I see in this space:

  1. The democratization of data: Modern technology offerings give us the flexibility to execute machine learning tasks with little coding knowledge. When we empower a business person with this capability, we see more meaningful outcomes
  2. XAI or explainable AI: Many good AI models face hurdles in implementation because they are too complex to understand. XAI is an area of research, which is focused on finding reasons to complex ML models outcomes. When ‘explainable AI’ is fully developed, it will help us gain the ‘needed trust’ from the business users
  3. Data literacy: More and more companies are working towards making their workforce data literate. It is the need of the hour. Data literacy means that EVERY employee in an organization must have the knowledge to read data visualizations and have the ability to question it

Data Science is fast emerging, evolving and is making an enormous difference in how the world operates. I am excited to be at the forefront of this space!

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