Empowering Women in Tech
Insights from Google’s International Women’s Day Panel
Yesterday, I had the opportunity to participate in a panel discussion as part of the Google IWD event. It was an insightful discussion with my highly experienced co-panelists Shweta Oak, Manik Kashikar, and Pranoti Nandurkar, who moderated the discussion.
Here is the gist of the discussion:
In your experience, how has the landscape of women in tech changed over the past few years?
I can see a significant change in this regard.
In terms of numbers, there are definitely more women in the workforce now. Not only at junior levels, but there is also more female representation in leadership roles. We see more women VPs, CTOs, and entrepreneurs.
Regarding skills, there has been a rise in the number of girls pursuing STEM courses, particularly professional courses like engineering. This has helped bring more women with relevant skills into the workforce.
Women are now more confident and focused on their careers. It is quite an encouraging picture, I would say.
What are some of the biggest challenges women in tech face today?
Women have made a lot of progress with support from society, management, and most importantly, their own efforts to make themselves capable of the opportunities provided to them. However, there are still some challenges that women face, and I personally feel that two of them are the most important ones:
- Work-life balance: This is the most talked-about topic in the industry, but statistics show that a very large percentage of women drop out of the workforce at various milestones in their lives compared to men. Some drop out after getting married, some after having kids, and some take breaks to care for ailing parents and many other reasons. This challenge has a two-pronged solution, creating a strong support system at home and an understanding environment at work.
- Lack of confidence: I think women subconsciously tend to doubt their abilities. In reality, they are capable of many more things than they think, but they hold themselves back. If they can identify this and at the same time, realize their hidden potential and the resilience they inherently possess, then there is no stopping them. So the solution lies in the hands of the women themselves. Becoming better at the job at hand, upskilling, and learning new technologies all the while, makes one more confident.
How can women leverage their unique skill sets in quality assurance to excel and lead in the tech industry?
This is an important topic to discuss. Women possess some great inherent skills, which are at times overlooked. If women can identify these qualities and turn them to their advantage, it will make their path towards success much easier. I will talk about a couple of traits women possess which they can use in QA particularly.
- Attention to detail: This quality can ensure bringing out a high-quality product. Let me give you an example. So many times we see new apps or websites getting launched, which fail miserably. In frustration, we conclude that no one used the app before making it live… in other words, no QA was done on it. I’m sure all of you must have faced this at some point.
Women, with their quality of unwavering attention to detail, can identify the smallest of flaws, rectify them in time, and thereby contribute to creating reliable and high-quality products.
- Empathy: Empathy means trying to understand another person’s perspective. Women can actually step into the shoes of the end-user and understand their needs, challenges, and preferences. This perspective can help bring out user-friendly and intuitive products.
I would encourage women to identify such hidden qualities and make them their strengths.
With the rise of AI-powered testing tools, what new opportunities are emerging for people in QA, and how can they prepare for them?
AI has found its applications in all areas of work, including QA. AI can be leveraged in every phase of the Software Development Life Cycle (SDLC), starting from requirements analysis to analyzing defects.
In the requirements analysis phase, AI can extract key points from requirements documents and summarize them for us, making it easier to understand. Going a step further, AI can also figure out ambiguities/inconsistencies in these documents. It is very much possible that everybody — the product owners, BAs, QAs, Devs — everybody can miss them, only to be caught in User Acceptance Testing (UAT). Finding defects at such a late stage comes at a cost. But AI can catch such lacunae at the initial stage in the software development lifecycle.
- Impact analysis and regressions: Changes to individual components in large systems can result in an effect on some other component. It is hard to figure out what can get impacted, and it is even harder to identify the areas that we should be testing after such a change happens. AI solutions can help with identifying all these impacted areas and can go a step further and can also identify test cases to test these changes.
- Test data: Before we can begin our testing, we would need a lot of diverse test data to verify every scenario. Generating such diverse or large volumes of test data can be challenging and more than that, time-consuming. In this era of reducing Time to Market (TTM), we can’t afford to spend that much time. AI can help auto-generate test data and make life easy to some extent.
- And what about automation itself? We have seen Devin and Devika generate code. It can be used to generate automated test scripts as well. Then with small tweaks, we can just plug in those test cases into our frameworks.
Now, coming to the second part of the question, how can we prepare for it?
So when we say AI can be leveraged, we mean that we need to build systems to do specific tasks. If it’s impact analysis or test data generation, then it is one approach that you follow, but it might not work for the requirements gathering phase. There you might need a Natural Language Processing (NLP) solution to extract data. Or if it’s code generation, it would help if you use say Vertex AI foundation models along with Palm APIs. So it’s important to know what all options are available and which one would suit where. Preparing for this means investing in upskilling. Enroll in courses which will teach you the fundamentals of AI, ML, and data science.
What advice would you give young women who are interested in pursuing a career in technology?
Never stop learning.
Even if you choose a managerial path, even if you do not have to do anything hands-on, don’t lose touch with technology. That is because it helps in making better and informed decisions. A manager who is so aware of new technological advances commands a lot of respect.
Looking ahead to the future, what are you most optimistic about regarding the opportunities for women in tech?
The number of women in tech is slowly increasing, which means more role models to look up to. More junior women are getting inspired by the rising participation of women in key roles.
At the same time, I see many women proactively upskilling themselves, doing certifications, enrolling for courses. This will open more doors for women in technical and leadership roles.
It was indeed a pleasure to share the stage with experienced and like-minded panelists and listen to their viewpoints on the challenges faced by women and the solutions they found for themselves.
An afternoon well spent!
Here is the link to the video of the snippets of the discussion
