Data Democratisation: Bridging the Data Science Talent Gap
Data science continues to be one of the cornerstones of innovation as organisations explore its potential to transform business processes, generate new models for success, and boost efficiency across the enterprise. The Harvard Business Review even called the data scientist role ‘the sexiest job of the 21st century,' as these highly-skilled professionals interrogate and identify key patterns and trends within the data available to them, making a significant contribution to a company's overall performance.
However, as alluded to above, data science – however ‘sexy' the job role is perceived to be – requires a broad array of complex and scarce skills including (but not limited to) quantitative disciplines such as statistics, machine learning, operations research and computational linguistics. And, unfortunately, as the market currently stands, there simply aren't enough skilled and qualified people to fulfil this demand. In fact, a recent report by Indeed identified a 344% increase in demand for data scientists since 2013.
While this is a huge opportunity for young talent to capitalise on, it poses a challenge for businesses who are being held back by the data science talent gap. Without these roles, organisations are struggling to truly be able to harness the power of data science and gain value, as they're either prevented or being stalled from driving deeper and more meaningful insights from their data as they look to become more competitive.
Naturally, though, there is a middle ground where the gap can be significantly reduced by training and upskilling existing employees in key areas of data science, particularly in lines of business where its reach and impact can be most felt, for instance in marketing, product development, and risk management.
Getting up to speed by leveraging citizen data scientists
Gartner defines 'citizen data scientists' as an emerging set of capabilities and practices that allow users to extract predictive and prescriptive insights from data while not requiring them to be as skilled and technically sophisticated as the expert data scientists.
Therefore, these citizen data scientists can help organisations incorporate data science more easily and more broadly by providing the business context and value of data science and integrating its output into new and existing business processes and environments.
It is a complementary role to the expert data scientist who is typically a coder with a deep involvement in the development, training, and use of algorithms and models. Citizen data scientists bring business and industry vertical domain expertise that many data science experts lack. Plus, they utilise a different toolkit; one that typically uses visual drag-and-drop tools with prebuilt models and data pipelines that often build on the work of data scientists.
This is an emerging role and title within organisations, but its presence is growing. Especially as more and more organisations recognise that leveraging citizen data scientists can be an effective way to start bridging the current skills gap around data science and machine learning while increasing its impact.
It's also important to stress that citizen data scientists may already exist in many organisations, but just not with this specific job title, so education continues to be a priority to ensure businesses understand the value of a citizen data science as part of their data-driven culture.
Looking ahead: a bright future for data science
One way organisations can enable citizen data scientists is by bringing data science and business intelligence practices together, providing them with timely access to insightful, trustworthy, and governed data. Thanks to significant enhancements in open data frameworks, senior managers can leverage insights directly within their familiar visual business intelligence tools, enabling them to exploit complex data science algorithms under the hood, so they can self-serve reports whenever they are needed.
With the rise of data analytics in organisations, we've also seen the democratisation of business intelligence dashboards for knowledge workers, analysts, and senior staff, giving them customised up-to-date reporting on key business metrics.
The next evolution will be for business intelligence reporting to become fully self-service, empowering all employees with the latest up-to-date metrics that are relevant to their job. This is thanks to ever-more powerful and intuitive business intelligence tools that are augmented with self-learning features and functions sitting upon high-performance analytic databases – databases that can even cope with the Monday morning workload without a hint of slowdown.
Given the increased demand for a “data-driven” approach to business, leading organisations are ramping up efforts to democratise data access while increasing their use of data science disciplines, such as predictive and prescriptive analytics, to enable them to be more forward-looking and competitive organisations.
This is backed up by Gartner's recent research. It found that data analytics remains the biggest area of IT and business investment. Further research also shows that 85 per cent of companies know about the benefits of data and are actively working towards using it better, which is promising when it comes to organisational transformation.