Tackling the divide in the big data industry
It’s fairly clear to most now that the continued advancements in analytics will have a profound impact on the business world. The Big Data Analytics market is anticipated to exceed $225 billion in the next few years, and according to LinkedIn is a major driver of new job opportunities worldwide.
Advanced analytics, machine learning and AI will transform every part of our lives, from business innovation and government plans to our health, wellbeing and the environment. We often perceive big data and AI as technical fields but is heavily interconnected with our lives and nature. Big data and analytics is driving changes in multiple markets, enhancing R&D and improving healthcare systems.
Considerable investment and energy go towards developing AI and analytical technologies. Venture capitalist investment in AI-targeted startups has expanded by over 20 times to a value of around $75 billion, according to the Organization for Economic Co-Operation and Development (OCED). Investment is quickly expanding to new industries from transportation and construction to retail and financial services. Sooner or later, everyone will need data analytics within their business management plans.
The challenge is a lot of time and energy is being directed at the technology, there is less focus on investing in talent. To meet the rising demand, the world will require additional people with STEM skills, especially those with experience in data science and advanced analytics. Demand for data scientists has grown significantly in the last few years. Data Science and ML jobs represent five of the top 15 fastest-growing job areas in the USA, according to LinkedIn. There is, however, simply not enough young people moving into these industries, despite the lucrative salaries and career options. This is particularly true in the case of younger women.
According to Cornelia Levy-Bencheton, author of Women in Data, believes the industry is underutilising women in data science. Women make up 57% of undergraduate students and 60% of post-graduate students, but only 35% follow their studies in STEM. In the US, women represent 56% of the total workforce, but only 25% work in technology. The number is even lower within the data science area. One of the main issues is the lack of role models and the representation of women in senior-level positions.
Any plans or discussions concerning the future of business analytics and data science need to incorporate gender representation. It’s clear we need more data scientists, but more importantly, the industry requires a diversity of viewpoints and ways of creating new solutions with data. In a society where AI and advanced analytics will become vital in driving creativity, customer experience and innovation, the business equipped with the most data scientists is likely to have a competitive edge, but the one with the most diversity of skills and opinions will come out on top.
Having a mix of viewpoints, skills and opinions are important to the industry of data science. The data scientists are what matters the most and their ability to tackle problems and determine what questions need to be asked about data to deliver the most effective insights.
Gender diversity will impact the industry as the more women in the field, the greater the volume of perspectives and knowledge will be for generating new value and solutions. In an industry where 80% of big data professionals are men, more diversity can only improve processes and enhance the ability to utilise large data sets effectively.
Data skills need to be interconnected with other subjects, such as economics, engineering and robotics. The majority of future careers will require some STEM skills and knowledge of computer science. Despite recognising this importance in STEM, most students fail to take STEM classes or focus on computer science. There needs to be integration with education, the community and general awareness. These areas are creating economic and gender gaps within big data.
Aside from the obvious barriers, there are other personal factors like confidence and participation which influence the uptake of data science roles. Studies suggest that most young women are interested in STEM careers, but very few pursue this further into later stages of education.
Industry experts highlight that we require more women as role models to encourage young female professionals to feel more confident that they can pursue a career in the data and analytics market.