Finding a decent data scientist for less than the price of a deposit on a Sydney harbour side home is almost impossible these days. But that could change quickly and dramatically due to automation, according to research analysts at Gartner. More than 40 per cent of data science tasks will be automated by 2020, resulting in increased productivity and broader usage of data and analytics by citizen data scientists, say the researchers. Gartner defines a citizen data scientist as a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics. According to researchers, citizen data scientists can bridge the gap between mainstream self-service analytics by business users and the advanced analytics techniques of data scientists.
Gartner predicts that over 40% of data science tasks will be automated by the year 2020. This will result in greater productivity and usage of data and analytics by citizen data scientists. With data science continuing to emerge as a powerful differentiator across industries, almost every data and analytics software platform vendor is now focused on making simplification a top goal through the automation of various tasks, such as data integration and model building. "Making data science products easier for citizen data scientists to use will increase vendors' reach across the enterprise as well as help overcome the skills gap," said Alexander Linden, research vice president at Gartner. "The key to simplicity is the automation of tasks that are repetitive, manual intensive and don't require deep data science expertise."
The government is actively pursuing a strategy which places the UK at the'forefront of the artificial intelligence and data revolution', however, the £45 million to be allocated for 200 extra PhDs in AI and related disciplines will barely make a dent in this omnipresent issue. In order to progress towards an AI future, we need to value the human role in this process and ensure the right skills are in place to develop the best tech. By better preparing a shift towards data skills, companies can build an army of citizen data scientists' capable of better reacting and leading in a data-led world. Through empowering every member of the business to use and leverage analytics to amplify their abilities companies can develop existing talent to understand the data in a business context without investing in consultants to do relatively easy data tasks. Without the right workforce, organisations simply cannot proceed to tackle the technical challenges existing in a data-driven industry.
Data science has been a buzzword in the market for quite some time, it has even been labelled the "Sexiest Career of the 21st Century". That quote alone has cropped up many times in presentations, articles and undoubtedly many internet searches. So, where did this "new" career suddenly come from? The term data science is not actually as new as we think it is, the discipline of data science has already been around for over 30 years. In 1997, C.F. Jeff Wu gave the inaugural lecture entitled "Statistics Data Science?" for his appointment to the H. C. Carver Professorship at the University of Michigan.