Getting women in the driver’s seat of the data-driven world: Your success starts with you!
By Rasyida Paddy, with inputs from Shrishti Vaish and Jyoti Kumar Bansal
Rasyida Paddy
Rasyida is a mom, millennial and marketer. She is ever-curious and thrives on connecting concepts and ideas to solve problems.
With economies across Asia opening up and governments in the region recalibrating their strategies to return to normalcy in a post-COVID world, there is so much for us to look forward to. One of which is economic recovery — a key item on the forward-looking agenda for both the public and private sectors across the world.
Some interesting data points: The International Monetary Fund (IMF) projected a 4.4 percent economic growth in 2022. Specific to India, the United Nations World Economic Situation and Prospects (WESP) 2022 report forecasted a GDP growth of 6.5 percent this year.
This promise of economic rebound brings with it optimism about the job market, which was significantly impacted during the pandemic. Data from Statista shows that as of December 2021, the unemployment rate in India was recorded at nearly 8 percent.
On the flipside, the percentage of employable workers in India in 2022 saw an increase from the previous year. Women accounted for slightly more than 51 percent of employability this year. Particularly noteworthy is the fact that women have constituted a larger share of India’s employable talent than men since 2016.
Here’s another statistics worth calling out: Women make up nearly 43 percent of the total graduates in Science, Technology, Engineering and Mathematics (STEM) in India — one of the highest in the world — according to data from World Bank. The growing popularity of online learning platforms like Coursera and upGrad, especially during the pandemic, have also contributed to more female students enrolled in STEM programs. For Coursera, the share of STEM course enrolments by women learners in India increased to 33 percent in 2020 from 22 percent pre-2020, while upGrad saw a 27 percent increase.
Surely, these should be indicative of the progress that we are making towards greater gender equality in today’s economy, isn’t it?
Higher employability, however, does not necessarily translate to actual employment amongst women. Further breakdown of the data from Statista shows that the participation of women in the workforce was negligible in comparison to their male counterparts.
The Female LaborForce Participation Rate (FLFPR) has continued to fall over the last three decades — currently, the women’s workforce participation rate across India stands at 20.3 percent. Within STEM, the sector is experiencing what is called a “leaky pipeline” of women talent, with a survey by Niti Aayog revealing that 47 percent of women in the industry cited family care as a reason for refusing a challenging opportunity in their careers. As we do our groundwork and speak to women in the country, many also cited the lack of support and a strong network as reasons impeding their motivation to grow in the field.
The discrepancy between employability and actual employment, amongst women in the country has piqued the curiosity of our team here at She Loves Data, especially as we are seeing many companies investing into programs to hire more technical women this year. How can we help bridge the gap between talent availability and employment opportunities for women in thiscountry?
The answer is upskilling and learning platforms – learning pathways that will arm and equip them for a data-driven world.
Earlier in February, the Indian government announced that as part of its Union Budget 2022, it will be boosting investments and capacity for skilling initiatives, which will set the tone for a massive push to create more jobs, benefitting women predominantly. We applaud this announcement, and look forward to supporting this agenda through our platform and community.
In addition to skilling programs, organizations like She Loves Data provide women with a network of support and opportunities to connect with like-minded individuals, which we hope will give a boost in the motivation of women pursuing careers in STEM.
As the road is being paved to boost the participation of women in the country’s digital economy, it is still important to note that true impact can only be seen when women take charge and be in the driver’s seat of their career. The foundation has been set for you, and the rest of the journey is yours! The keys to the ignition are in your hands. So what are you waiting for?
Featured Volunteer: Lindsay Sunhendra Jap
Aug 18, 2021 | Nikita Phavade
1. Hi Lindsay! Tell us more about your domain, Data Analytics. What does it mean to you?
Data analytics is about discovering insights from data that could bring values to the organization in a form of optimization. For example, improving effectiveness in customer acquisition efforts or reduction in operational costs. So, there are two parts of it; insights generation and value generation by taking action on the insights.
The first part includes digging deep into the data by asking micro questions that relate to the bigger objective. This requires technical and statistical skill. Technical skill is to plow through big datasets via SQL Query and Python. Statistical skill is to understand the behaviour behind numbers.
The second part of value generation involves different ways to communicate these findings to stakeholders. It could be in the form of self service analytics via dashboard or ad hoc analytics via presentations. This point requires soft skills from the analyst, specifically data storytelling. For dashboards, creativity is empirical to know how to present the data in a way that will be easy to understand. For presentations and bigger projects, influence and storytelling skill are more prominent to be able to convince stakeholders on testing out the recommendations.
2. What does a day look like for a Data Analyst ?
Well, it started with a cup of coffee, hoping that nothing breaks in the dataflows. LOL. There are no two similar days as a data analyst. Some days, I am challenged with new technical issues in dataflow. Some days, there will be meetings with business stakeholders trying to understand their needs for insights and analytics work that will enable them to make better decisions. Some days are spent in front of a dark screen coding away in Python to complete a Machine Learning project.
3. What are the best parts, and maybe some challenges involved in your work?
The best thing I enjoyed most about my work is the variety. You get to be technical one day and present your insights and recommendations the next day. I also enjoy the creativity of designing dashboards to showcase data. I am constantly challenged and stimulated intellectually with new problems, which makes it interesting.
The difficult part is influencing the stakeholders to take actions on the recommendations. All hard work on discovering data patterns is all gone to waste if it does not bring value to the organisation. Therefore, understanding how to work with different types of stakeholders is also key to bringing impact through data.
4. Data Mining is a buzzword today, but a lot of us are still trying to catch up. With your expertise, what are the upcoming trends you foresee in your domain?
Analytics on demand might be in the near future. We have seen increasingly that companies are automating Machine Learning processes. Similarly with analytics in general, with the tremendous advancement of NLP (Natural Language Processing) and GPT-3, we can enquire any questions about the data by letting the computer learn about all the tables in our database.
5. What message would you like to give someone trying to carve a path in the Data analytics industry?
I believe curiosity and inquisitiveness are the key qualities you should embrace in this industry. Sometimes the problem is so vague or novel that you might know the answer yet. Curiosity will enable you to probe further to understand the questions better. Willingness to learn will empower you with knowledge in new areas that could potentially solve the issue. In terms of technical skills, SQL and Python are two key skills that all aspiring analysts should be comfortable with. These are the enablers for dealing with big data which usually is not as straightforward to mold according to our needs. Being familiar with data structures and schemas will be important too.
6. Lastly, I want to thank you for all the amazing work you do at She Loves Data Community, how has your experience been?
I am currently part of the Data Integration team contributing as a data engineer. I am in charge of bringing in, cleaning, and transforming data from multiple sources so that it is ready for data visualisation. The team aims to understand how much lives we impact and to learn from the past events on what worked and did not work.
It has been a great experience so far. I am surrounded with smart and passionate people with the same goal in mind whom I always learn from. I am always upgrading and being challenged to go beyond the familiar. It has been fulfilling to be part of the journey of women empowerment in the data and analytics industry.
