Women in Data Science
Featuring Medha Devare (Platform for Big Data In Agriculture)
Agronomist and Data Architect
I am a Module Lead for CGIAR’s Big Data Platform for Agriculture, which I helped write, and currently lead efforts to help the CGIAR System’s 15 Centers better manage and organize their agricultural knowledge resources to make them discoverable, accessible, interoperable, and reusable. By supporting the use of semantic approaches and Big Data capabilities to enable the easy aggregation of different datasets, and new analyses and interpretations, I assist CGIAR’s efforts to accelerate innovation to improve crop productivity sustainably, reduce poverty, and improve food security and nutrition.
How did you come to pursue a career in data science?
I loved biology from my early school years and ended up at Cornell University to study soil microbiology for my Master’s degree, and agronomy and international development for my Doctorate. As a post-doc at Cornell, I studied the effects of genetically modified Bt maize on soil microflora, and the molecular techniques I used introduced me to the world of data science and Big Data. My next position as a bioinformatics specialist at Cornell’s Mann Library for the agricultural and life sciences exposed me to information management standards and approaches, and leading a cutting-edge semantic web initiative named VIVO plunged me further into the world of data science – specifically, data interoperability, and how semantic standards could provide meaning to data and enable easy querying, organization, and integration of varied data types. From there it was just a couple of steps to full immersion in my current Big Data efforts at CGIAR.
What are the things you love about your role in data science?
I love science, and I love efficiency in what we do as scientists. So, I find it frustrating that so many researchers still believe that the data they collect is only of value to them, and only for the specific question they’re asking and, very often, only as long as they publish from it. Typically, they then move on to the next project and the data stays locked up in one way or another and may as well never have been collected in the first place.
Science is more than the serial collection of data; it is the generation of new meaning, new insight from data by making it open, sharing its value with others who have other types of data that can add value! I love that my work allows me to support the unlocking of data, to evangelize about annotating and sharing it effectively, and to leverage the power of new standards and tools to realize its value in combination with other data streams.
What has been the most exciting project you have worked on?
I am proud of the work I do now – it isn’t easy to try and forge a common and consistent path to FAIR (or Findable, Accessible, Interoperable, and Reusable) resources across geographically dispersed entities with different mandates and disciplinary foci – but the 15 CGIAR Centers are increasingly receptive, and actively trying to support their researchers. The changes I’ve seen just in the last couple of years are fulfilling and make me feel that what I do matters, and that it is helping to catalyze change and transform agricultural research and development.
Do you feel this is an exciting time to be a woman working in the data sector?
Absolutely. I think the number of women working in sectors where they have traditionally been a minority is increasing, and as the proportion of women working in the data sector goes up, it paves the way for more women by creating larger support and mentorship networks (with perhaps some of the best of the old boys networks) and an environment that affirms and values the strengths that women bring to these –often male-dominated– jobs.
Why do you think there is a lack of women in Tech/Data sectors?
There has been a lot of debate about this lately, and much written about inherent discrimination at a variety of levels. Whether or not due to overt biases, the tech sector has traditionally been a male bastion, and it has been difficult for women to break through, and for what they offer to be valued without reference to gender.
I hear a lot about efforts for “gender mainstreaming” across the tech and data sectors. However, for it to really work, we need secure and safe processes to report harassment, and there needs to be more effective training and accountability regarding inappropriate behavior and gender insensitivity in terms of gender, diversity etc. And of course, a higher proportion of women leaders and mentors would help.
Even when women make up a substantial proportion of the workforce in the Tech and Data – and many other – sectors, they are still typically represented in appallingly low numbers in leadership positions, and this tends to promote a male-centric perspective, values, and environment.
As a woman, did you face any challenges in your career pathway to work in data science? Any advantages because of your gender?
As an Indian woman — and as a mother and a daughter needing to take care of dependents, it has not always been easy to work in the demanding (if fulfilling) world of either field agronomy or data science. I have had to make peace with the competing pulls of professional responsibilities with personal/family obligations, and to feeling that I was not able to give enough time or quality attention to either. Along the way I’ve had to listen to “helpful” comments from people I like and respect, such as “you should really not be here [at a professional meeting], you should be thinking about taking care of your son and your husband” or “…women were made to support men”. These are not the sorts of comments male professionals would ever hear, and they can be demoralizing and diminishing — if one lets them.
Why should more women get into data science?
Women who are interested and passionate about any field of work should make a confident, passionate bid to get a job that interests them in that sector and to stick with it despite gender-related frustrations. More for their own fulfillment and sense of having made good on their abilities and desires, but also to be models and mentors for other women – and men.
How could more women working in data, specifically for the development/agriculture sector benefit the sector/?
Just under 50% of the global agricultural workforce is female, and over two-thirds of smallholder farmers are women, by some estimates. Why shouldn’t this female workforce have similar representation in those trying to work for and with them? Women’s experiences are often best understood and captured by other women, and women may respond most easily to other women rather than men — sometimes for as simple a reason that rural women in developing countries are more intimidated by men. That is not to say that a man cannot sensitively and accurately capture or positively influence the agricultural constraints and successes of women farmers, but trust is more likely easier to build between and among women.
What advice would you give to women wanting to get into data science?
Remain confident, believe in yourself, be passionate about what you enjoy about your work, and be strong in not letting thoughtlessness or a hostile work environment grind that away – as far as possible. Don’t be afraid to make your opinions and objections to bias be respectfully known, and to be persistent when needed. Find champions and mentors if you’re able, particularly people who will give you honest feedback but also be unstintingly positive and affirming when deserved. And, in turn, be that for others — both for women as well as for men.