– Krithika N.S.
http://hiase.com/uber-no-boys-club-tech-companies/
Silicon Valley tech companies often occupy meaningful positions in annual “best companies to work for” lists. They’re known for their young workforce, inclusive and liberal work culture, and great pay. But, one aspect that even these tech companies struggle with is gender diversity. In recent years, a number of these tech companies have begun addressing this issue by quantifying their gender diversity data. Google released its data in 2014 shortly followed by other companies such as Facebook, Twitter, LinkedIn, Apple etc. The most noticeable aspect of this data across all of these companies is that women are significantly under-represented in engineering and leadership roles.
Uber went through a lot of turmoil several weeks ago when a female ex-employee wrote an explosive inside story about how women were treated in the company. This led to a series of efforts from Uber to correct their management of internal issues related to gender.This also led to Uber releasing a detailed report about the gender breakdown and the racial make-up of the company. The above article goes into Uber’s gender breakdown report and discusses one aspect of it. The chart in the article illustrates the percentage of female employees in major tech firms. It presents Uber as having a slightly greater percentage of women employees than other valley firms but, broadly matches their trends.
From analyzing the chart, the following come to one’s immediate attention.
- The chart is represented as a 3D bar chart. While a 3D illustration may give the chart more sparkle, it often gives a distorted view of the data. It wasn’t necessary to do it here as the dimensions of the data did not require an extra axis. A simple 2D chart would have done the job for the reader.
- Communicating what the chart represents is a key aspect of data visualization. The labels in the chart occupy a significant part of this. The chart under consideration has three labels, namely, “Series 1”, “Series 2” and “Series 3”. Very little effort is made to explain what those labels mean. While this may make sense to the more data-driven audience, often times, readers are outside of this demographic and this labeling appears confusing to an outsider.
- The chart in the article does not appear to take any chronology or timeline into consideration. Absolute time adds a lot of meaningful information to visualized data and the reader is deprived of this information.
Doing It Right
While researching data sets related to gender diversity, I stumbled upon another very elaborate, thorough, and functional graph that is presented in the link below. Doing the above analysis correctly would correlate with the below graph in a lot of ways.
http://www.informationisbeautiful.net/visualizations/diversity-in-tech/
Some of the ways in which the data has been interpreted and presented better are,
- The percentage of men and women in the workforce has been illustrated in a stacked bar which appears easier to understand for comparison.
- The graph appears easy on the eyes making the important data simple to decode.
- The demographic information has been clearly labeled by year which does not require additional effort from the reader to decipher.
References:
https://pxlnv.com/blog/diversity-of-tech-companies-by-the-numbers-2016/
https://www.gooddata.com/blog/5-data-visualization-best-practices