How the data talent gap risks our AI future

Data workers are at the center of our ambitious targets for universal artificial intelligence adoption as they are the ones responsible for building the technology on a strong and stable foundation. However, there is one significant hurdle that stands between data workers and designing the perfect AI solution; the talent gap. Recent research has revealed that 44 percent of data workers are wasting time every week because they are unsuccessful in their activities, as they face challenges such as lengthy data preparation processes and a lack of collaboration. If organizations can't attack the data and analytical talent gap head-on, they will increasingly be buried under rising data volumes, complexity and an incomplete understanding as to whether their workflows are doing the job they want.

Without a productive workforce, organizations simply cannot proceed to tackle the technical challenges existing in a data-driven industry, such as reversing the inconsistencies and set-backs with data-led AI projects. With the right analytics platform, data capabilities can be put in the hands of the business experts who not only have the context of the problems to solve but the data sources needed to deliver insights quickly and efficiently. By alleviating data workers of some of the mundane, day-to-day tasks that are consistently clogging up their to-do lists, more time can be allocated to pushing through large-scale AI projects.

Existing employees should still be able to perform some level of data-related tasks despite not being experts, as they are in the line-of-business, close to the questions and the leaders who need insight. Linking up data insight for people with the vital business knowledge is paramount to making the most of data analytics and fueling the development of AI. What’s more, getting data in the hands of the employees is crucial in order to democratize AI and make advanced analytics more accessible to everyone, rather than locked away by a 'priestly caste' of data scientists. Empowering citizen data scientists with the right support and self-service tools is of the utmost importance in helping to speed up adoption and share the benefits across the organization.

Historically, analytics has required skills like coding in R or other specialized languages, in order to build out predictive models, skills that the average individual doesn’t typically possess. Modern zero code (or 'code-free') platforms are helping to make the democratization of AI a reality. They act as a gateway into the analytical world whereby every employee can collaborate to transform data into actionable insights, which removes the need to rely on a small group of data experts which often form an insight silo.

Demand for data democratization has created a wealth of self-service solutions. Now individuals across every department can interact with both their own data and external data with the help of these technologies. In turn, businesses and recruiters can look at a broader range of applicants to fulfill data-driven roles, rather than looking just to certified statistical grads and expert data scientists who are in such short supply. whilst those experts are certainly needed for tasks such as building complex algorithms, they are not needed to do every data task, investigation and project to the exclusion of other skill sets and the functional knowledge that those within the line of business apply to problems using their own data.

The citizen data scientist is armed with processing and analytical capabilities which allows them to engage more and more in an AI and data analytics-driven world. With more of these active employees within an organization, an AI future isn’t a distant dream, as more of the organization can be focused on the goal of delivering on data.

Photo Credit: Pixelbliss/Shutterstock

Nick Jewell is Director of Product Strategy at Alteryx. Starting his career with a Ph.D. in Chemoinformatics helped develop a life-long passion for applied analytics. As a long-serving analyst, architect and innovation leader for Analytics & BI at a large international bank, Nick helped define and implement large scale big data, analytics and informatics solutions at the petabyte scale for global audiences.

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