The data skills gap is a well-publicized issue, and true data scientists are a relatively rare species. One way that organizations are attempting to solve the issue is by empowering all employees with some data skills - whether this be a math or social science degree - to analyze the data themselves. These are known as citizen data scientists.
Gartner defines a citizen data scientist as ‘a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.’ They have predicted that by 2017, the number of citizen data scientists will have grown five times faster than their highly trained counterparts. According to Shawn Rogers, Chief Research Officer at Dell Statistica, ‘I think that 2016 could be the year of the citizen data scientist because users throughout the business want a more democratized approach to Big Data and analytics. Not every company can afford a data scientist, which is a big reason why citizen data scientists will become a big part of the data ecosystem as it evolves.’
Data is now at the heart of any operations, and its importance to decision making and innovation is only going to grow. By 2018, over half of large organizations worldwide will be using advanced analytics and proprietary algorithms to compete, while by 2020, companies will be spending 40% of their net new investment in business intelligence and analytics on ’predictive and prescriptive analytics.’ Fundamentally, this means that everyone in the organization needs to be able to leverage the data to some degree, and it cannot simply be left to one highly trained individual sitting at the top of the firm dishing out insights as they deem fit.
The rise of the data scientist is largely being enabled by improvements in self-service Big Data discovery platforms. Advanced analytics capabilities are now available to far more people. For example, Dell this month announced that they would be rolling out version 13.1 of its Statistica data analytics platform. There are a number of other firms, such as Platfora, who have similar software.
The cost benefits of the citizen data scientist are obvious. The average annual salary of a data scientist is, according to Glassdoor.com, $119,000. Intelligent staff could potentially leverage the data in the same way for a far lower salary. There is also a huge benefit to be realized by having someone with actual industry and business experience analyzing the data - a rare commodity in traditional data scientists. IBM, for example, teaches people with professional tennis experience how to analyze the data at Wimbledon rather than teach data scientists about tennis, purely because it’s both easier and cheaper. The same logic applies across all industries in which the data capabilities do not necessarily need to be that high.
One example of a company who has invested heavily in a citizen data program is Sears. They recently empowered 400 staff from its business intelligence (BI) operations to carry out advanced, Big Data driven customer segmentation. This work would previously have been done by specialist Big Data analysts, and as a result, the retailer has reportedly been able to make hundreds of thousands of dollars’ worth of efficiencies in data preparation costs.
Providing so many employees with access to the data is not without risk, though. For one, it opens up far more potential for breaches in security, and companies must ensure they have the governance in place to meet privacy requirements, and a system in place to ensure it is adequately enforced.
What does this mean for data scientists? At first glance, it may look like the trend is a bad thing for them, however, there are also advantages to be had from giving so many people a greater appreciation of data. Big Data has suffered somewhat in the past from difficulties in getting leadership and staff to buy-in to data projects, and improved awareness of how powerful it can be should help overcome this and bring greater investment.