Women in Wind Q&A 2020: Mahalakashmi Gunasekaran

Women in Wind 2020 Q&A: Mahalakshmi Gunasekaran

The Women in Wind Global Leadership Program sat down with Mahalakshmi Gunasekaran, one of this year’s Participants, to chat about her pathway to renewable energy and issues facing women in the wind sector. 

Maha is a Data warehousing specialist with the product lifecycle management (PLM) department, Vestas R&D and is based in Chennai, India.  She works on integrating data from multiple systems to derive data insights used for analysis, decision-making and performance monitoring in change management and material master data management.

She specializes in SQL server programming and development of tabular cubes to build BI reports, collaborating with stakeholders across the value chain. She also supports the business process improvement and operational execution with these insights and automation. 

How did you first become interested in renewable energy and joining the clean energy transition?

I was looking for a job where my ideas and contribution would be valued, but I was not aware that original equipment manufacturer (OEM) companies provide opportunities for women in the data science stream. I obtained two opportunities: one  with Vestas and the other with a service provider. My preference was to be part of the renewable energy industry, where my professional growth would go hand-in-hand with contributing to building a greener future.

Tell us about your expertise and passion in the sector. For you, what is the next “space to watch” in renewable energy?

At Vestas, part of my role is to develop the framework and foundation for data analysis along with business intelligence reports. These play a key role in improving our process performance in PLM, and have been immensely helpful in closing the knowledge gaps. 

The field of artificial intelligence in our sector is growing enormously and has started to pay off with great results. I also see wind and solar hybrid technology paired with electrical energy storage systems as a very promising field where we can use  real-time data for our analysis.