Information technology is one of the world’s fastest growing sectors. Data, analytics and technology have permeated through all aspects of our lives, and it is safe to say that in all sectors and industries, there are data problems to be solved. Just in Australia, the IT sector is forecast to grow by 5.4% on average per year, for a total of 1.1 million workers by 2026. This is more than four times the expected growth rate of the work force at large.
DataWomen is all about supporting and empowering women in data and our data friends. This aim of fostering a great community is what led us to put together this session. We started to think about topics like:
- What in the world of data is bringing competitive advantages to our companies?
- Why is it important that more females participate in this?
- How does someone start progressing their career and benefiting from the untapped potential of exciting data roles out there?
Below, we revisit some of the highlights from of the recent event. You can catch the full recording at the bottom of the post!
What is Advanced Analytics?
We chatted about advanced analytics (AA) being the automatic or semi-automatic examination of data using specialised tools and techniques, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. Using advanced analytics can make the difference for the companies stuck between keeping up with the competition or falling further behind.
We also talked about machine learning being the science of getting computers to learn from experience and perform tasks automatically. Basically, it works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. With the current high performing cloud computing technologies, and the vast amount of data that is been collected; there has never been a greater time to experiment with machine learning.
There are so many great examples of how currently advanced analytics are being used. Nike, for example, uses predictive analysis to forecast consumer demand on a hyper-local level, which helps them to optimise their inventory and develop more targeted campaigns. Retail uses clustering techniques to create upselling and cross-channel marketing opportunities. Even in our everyday life, we make use of advanced analytics like, for example, how Google Maps uses predictive analytics to suggest to us the best route to our destination, or how our email providers use text mining and pattern recognition to be able to provide spam filtering options.
Gender Gap and the Motivation to Close it
“Today, women and girls are 25 per cent less likely than men to know how to leverage digital technology for basic purposes, 4 times less likely to know how to programme computers and 13 times less likely to file for technology patent. At a moment when every sector is becoming a technology sector, these gaps should make policy-makers, educators and everyday citizens ‘blush’ in alarm”
DataWomen has always advocated for diversity. Different studies have shown that diversity increases the performance of a team by having different points of view to learn from. During our webinar, we talked about two examples that showed that lack of diversity in the technology groups developing two different products.
The first one was Apple releasing its new iOS 8 in 2014 which included a HealthKit app that promised to “let you see your whole health picture.” This app would be able to track everything from sleep, BMI and weight weight, to some obscure data such as selenium and copper intake. It was, however, missing the one thing that most of us females would be interested to track: our periods.
Our second example came from the Amazon ML specialists group that was working on developing a recruiting engine that would score the resumes from 1 to 5. In 2018, they discovered a big problem with said engine: it did not like women. The issue was that it was trained to vet applicants by observing patterns in resumes submitted to the company over a 10-year period. Most came from men, a reflection of male dominance across the tech industry. The engine was penalising resumes that included the word “women,” like in “women’s chess club” or “Women’s College.”
This demonstrated twofold the importance of closing the gender gap in AI/ML:
- Awareness, education and skills. We need people to be informed of what AI is and how it works so that they can critically assess personal and societal risks and benefits from the impact of AI.
- Increase awareness within the industry and address the issues of a lack of representation and bias within used training data.
What are We Going to do About it?
We can start by understanding that there are so many roles to participate in the information technology world! The possibilities are endless. If something interests you, explore it and see where it can take you. There are so many roles, like data engineers, data scientists or data analysts.
Some of the benefits of getting into the data spectrum are:
- Almost every industry is moving towards AA and ML, which means plenty of opportunities!
- Become eligible for a wide range of roles like data analyst, data scientist, data engineer, etc.
- Career growth and strong pay scaling!
- Flexibility to work remotely from anywhere around the world.
- Plenty of amazing data science tools available in the market for upskilling.
How to get started, you ask?
Well, there are so many ways. There are plenty of free, or almost-free courses, to get started, plus meetups, volunteering opportunities, mentorship programs or data hackathons! Check out the recording of our session with Dataiku, Everyday AI, to get started.
Be part of the DataWomen Community!
DataWomen is intentional about relationship-building and empowerment. We cover meaningful topics in our events, like salary negotiations and productivity at home, host small group chats with amazing women in our community and support a mentoring program. You can learn more about our programming at our landing page, and check out our LinkedIn group to be the first to know what’s next!
We’re always on the lookout for stellar individuals to contribute to DataWomen and help it thrive. If that sounds like you or someone you know, reach out to Azucena Coronel, Beth Kairys, Carol Prins or Debbie Yu. See you next time!