Making Data Work for Women: Reflections on the Women in Data Conference and Visualizing Gender Data and Statistics Training

March 14, 2019

By Sajani Lama and Aarya Bhandari

Facing a large and new crowd of people can be overwhelming, but less so when you have something in common: you are a woman and you are passionate about data. As two women working in Bikas Udhyami’s Nepal in Data initiative, we had yet to meet other women who like our work with data. So, when we walked in to find a room filled with more than 300 young women attending the first Women in Data conference, we were quite surprised.

The conference was organized by the Data for Development in Nepal program implemented by The Asia Foundation in partnership with Development Initiatives together with various Partner Organizations including Bikas Udhyami /Nepal in Data and with the support of DFID. The theme of the conference was “Where Two Superpowers Meet” referring to the combined power of “women” as forces of change and “data”, which has been hailed as the new oil of the 21st century. According to various sources such as the World Economic Forum and Forbes, data analysts and scientists are among the top emerging jobs globally. While in our country the use of data still needs to grow, it can be expected that in the near future the effects of these global trends can also be observed in Nepal. Hence, it is very important that we as women are aware, empowered with the right skills and ready to work with data!

During the conference, an impressive range of panellists with different backgrounds from civil society, media and private sector shared their experiences working in data and the challenges they faced as working professionals in their respective fields. As part of the first panel discussion ‘Move Over: Women Leaders in Data’, Ms.Pranita Upadhyay, Programme Leader at the British College, highlighted how people used to think that "Data was a man’s cup of tea only"and how she has tried to prove people wrong. Yet, still, women continue to be treated differently and are not treated as experts in their field. Ms. Prativa Pandey, CEO of Catalyst Technology, stressed how women continue to face structural barriers to be taken seriously in their work. She encouraged us to confront such barriers when we see them and find creative ways to overcome them.

The second panel discussion ‘Why Sex Matters: The Importance of Gender Statistics’ focused on why gender statistics are important and the various gaps that exist in this area in Nepal when it comes to the availability, reliability and timeliness of data pertaining to gender. Especially, getting access to disaggregated data can be quite a challenge. According to the Study into Development Data in Nepal that Bikas Udhyami undertook, 77 of the national SDG indicators were disaggregated by sex. Under SDG 5 on gender equality, 13 of the indicators had identified data sources. As Bivek Joshi, Monitoring, Evaluation and Strategic Partnerships Officer at UNFPA emphasized, "There are a lot of gaps in the production, use and enabling an environment of gender statistics." He highlighted the need to institutionalize deeper interaction between the users and producers of gender data. Gender statistics are often associated with women, but they also imply having disaggregated data on men and third-gender.Ms.Bhumika Shrestha, Transgender Activist, emphasized that despite the LGBTI Community being quite large in Nepal, LGBTI persons are not represented in official statistics. Ms.Meena Acharya, a Gender Expert and Economist, stressed the need to disaggregate data not only by gender but also by other dimensions such as social class, caste and ethnicity.

After the panel discussion, Fernanda Drummond, Head of Operations at Gapminder, gave an interactive presentation on “Factfulness” focusing on how our perceptions of the world are inherently biased towards the negative when in reality things are going much better than we think. In case you never heard of ‘factfulness’- it is described as the relaxing habit of basing your world view on facts! There is even a book written about by the founders of Gapminder, which has been recommended by Bill Gates and Barack Obama as one of their favourite books to read. One of the most fun parts of the presentation, was when participants had to answer questions related to issues such as poverty, educational attainment and climate change using electronic voting devices. Subsequently, Fernanda would show the scores live on the main screen and as it turned out, there were quite some misconceptions in the room! We had heard from our Nepal in Data colleagues about Nepal in Data evening in November 2017 when Fernanda together with her colleague Olaf of Gapminder gave a Guest Lecture on Factfulness and Dollar street. So, we were really excited, when prior to the Women in Data Conference, we already got an opportunity to see Fernanda live in action at D4D Data Talk with Gapminder.

The last panel session of the data focused on ‘Bridging the Digital & Data Divide: Women in Technology.’ It was very much encouraging to see several women our age, like Binita Shrestha from Women in STEM and Sushma Giri from Women Leaders in Technology, sit on the stage and share their stories of how they started their organization and entered the field of technology. The various women at the panel stressed how technological advancements have made data part of so many different aspects of our lives and how technology provides a very interesting career path to work with data in new ways. At the end of the panel discussion, Open Knowledge Nepal announced their Open Data Fellowship Women Edition, which provides young women interested in data with an opportunity to get work experience in 10 different organizations working in the field of data including in Bikas Udhyami’s Nepal in Data Initiative.

In the afternoon, there were a series of data labs by individual experts and partners of the D4D Program including one by us on ‘Visualizing Gender Data and Statistics’. We started the session off with an ‘icebreaker’ in the form of data pictionary asking some of the participants to draw a word scribbled on a paper with the audience having to guess what it was. The exercise caused quite some giggles as the audience tried to guess the image that was drawn while some of the person drawing started to resort to signing language. After that, we taught the basic principles of data visualization. This was followed by an exercise in which we gave participants a dataset on provincial literacy rates and asked them to visualize these. Participants enthusiastically started to visualize the data using bar charts, graphs, maps and icons. At the end of the exercise, three participants presented their visualizations and explained what motivated them to choose their mode of visualization. At the end of the conference, there was an opportunity to network with the other participants.

Following the conference, the D4D Program organized a series of data skills training. On February 26, it was our turn to train young women on ‘Visualizing Gender Data and Statistics’. As this was the first time for us to conduct such intensive training, we had been carefully preparing ourselves for several weeks starting with the development of the session plan and presentations to rehearsing every day. Still, when the day arrived to conduct the training, we felt nervous. However, luckily, among the 15 participants, we spotted a few familiar faces from the conference on Saturday and we felt at ease. The first thing we did was to hand out a pre-training questionnaire. We wanted to know what the participants were expecting and what they already knew. Whilst skimming over the completed questionnaires, despite having learned and worked with data in their day to day life, many participants still had plenty of misconceptions about it.

Often when people think about data visualization, they think it is only about design. However, in order to be able to make good data visualizations, one must first be able to understand, process and analyze data. While many participants were most interested to learn directly about the visualization part, we made sure to start with the basics first. Hence, the first part of our training focused on different data typologies, available data sources, what data producers you should and shouldn’t trust, and where and how to find data. The next part focused on teaching participants on how to use excel and when to use a line chart, column chart and a pie chart. Every topic was followed by an exercise. For example, once participants were taught how to find data, we created an exercise where they actually had to find the data from authentic data sources and producers.



The second day started off with two main questions “What are infographics?” and “Why are infographics important, especially in today's world?” We began by explaining and practising aspects such as adjusting the slides according to the data, changing backgrounds, making basic shapes, using SmartArt, icons and logos, and explaining about different colour combinations. After the participants were comfortable with those aspects, they proceeded to make structures such as human shapes, circular infographics, custom shape infographics, etc. Finally, the participants made their own infographics on literacy rates, educational attainment and household composition and presented these to the group.  


From our experience, many people, in the beginning, are often a little afraid of data or have misconceptions that data and statistics are boring. Hence, through our training, we wanted to show that you can make data work for you and data and statistics are tremendously interesting and can even be fun. So what did the participants learn? Many participants felt more comfortable after the training with finding data and conducting data research and various participants were more comfortable using data. Many participants stressed how they learned about different data sources and now feel more comfortable about using excel and powerpoint to create graphs, charts and pie charts. As one participant highlighted "I am now comfortable working with data and data visualization and will use it more in my work." Participants indicated that after the training, they intend to use their new found skills in future for their thesis, research and in making presentations. 

And what did we learn? As it was the first time that we conducted this type of training, it was a very useful experience for us. We learned more about planning, communication, public speaking, presentation and how to train others. After completion of the training, we now have a better understanding of what works and does not work and how we can further improve ourselves for future training. We hope to get more opportunities for exposure, learning, networking and communicating through events like the Women in Data Conference and training so that we can grow as well as help other women to become women working in data!



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