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Big data is a big deal. Every second, people around the world are creating new data and companies are collecting new data. In the race to be data-driven, organizations can’t afford to be idle and allow forward thinking competitors to move ahead. Technology advancements in artificial intelligence and machine learning for business intelligence and analytics are driving the need for highly-skilled data scientists to adopt, utilize, and deploy platforms that leverage this emerging technology.

There’s a rising demand for data scientists and analysts who play an invaluable role in data wrangling, discovery, analysis, structuring, cleaning, and communicating data to grow organizational health and furtherment. However, the number of job openings for data scientists far outweigh the professionals available to fill those positions, and while designated programs to educate and train data scientists are on the rise, it’ll still take time for supply to catch up with demand. The worldwide shortage likely won’t end anytime soon.

In an age where understanding and decision-making based on analytics are the foundation to business strategy, people who can bridge the gap are becoming increasingly valuable. This shortage is giving rise to a new role emerging from within organizations: the citizen data scientist. These individuals fill the need between simple self-service analytics and advanced analytics done by expert data scientists. The adoption and integration of business intelligence and analytics software tools and technology helps this shift of business professionals as incoming citizen data scientists.

According to Gartner’s Top 10 Strategic Technology Trends for 2019, “through 2020, the number of citizen data scientists will grow five times faster than the number of expert data scientists.” With this massive jump, it’s imperative to set citizen data scientists up for success when it comes to understanding, communicating, and acting on data.

Who are citizen data scientists?

Gartner defines a citizen data scientist as someone who creates models using advanced analytics or predictive tools, but whose main role is outside of statistics or analysis. Not everyone can, or will, become a citizen data scientist. Those who enter this position have data science-like skills and are power users, like business analysts, who can perform analysis tasks that are both simple and moderately sophisticated. The defining characteristic of a citizen data scientist is that analysis, data, and statistics are secondary to their role.

It’s not a position you’ll see advertised on a job board because it’s not a position an organization will directly hire for, but it’s a requirement they still need to fill. The key is looking internally at employees who have a passion for data discovery and analytics and want to evolve their skills and incorporate those aspects into their position. These could be business analysts adept with basic data science skills, but don’t be surprised if you find this person in your marketing, communications, engineering, or sales departments.

Since they come from positions within an organization, they hold a unique perspective in their specific business area that provides context on what the data visualizations such as charts, graphs, and models, in a business intelligence platform dashboard illustrating the data-driven story behind the scenes. Every business striving to become fully data-driven demands resources for extracting, communicating, and acting on meaningful insights, regardless of the size of the company. Data discovery isn’t just reserved for the big enterprises anymore, all organizations need to harness the power of data if they want to remain relevant among competitors and see exponential growth within their industry.

How do we empower citizen data scientists in their new role?

  1. Democratize analytics with business intelligence platforms

Like any other position, citizen data scientists must be armed with the right tools, technology, and development opportunities to ensure success. Software and BI platforms like Tableau, Power BI, MicroStrategy, Domo, and Sisense take data and create visualizations to portray the data story in an effort to democratize analytics throughout an enterprise and make them accessible for users to gather information, draw conclusions, and make data-driven decisions. Increasing availability of data allows insights to be distributed to the right people across an organization who previously didn’t have the access or tools needed for the discovery process. By skilling employees to take on these enhanced data roles, companies can make better use of data which leads to cost savings through more utilized resources (human and technical) and increased competitiveness in the market.

  1. Augment analysis with natural language generation to increase data literacy

The more tools available to assist citizen data scientists, the more effective they will be in their new role. The purpose of data discovery and analysis is to expand insights to the broadest audience within an organization in order to drive value, so it’s crucial to ensure that  audience can accurately interpret and understand the visualizations of a dashboard that are presented. Citizen data scientists are being introduced to complex dashboards and asked to perform tasks that stretch their knowledge and comfortability with data. Visualizations paint a picture of the data they’re analyzing; however, they’re still essentially making their best guess of what the data means based on their current level of data expertise. With increased complexity of visualizations comes an increased risk of misinterpretation.

Adding narratives through natural language generation (NLG), a subset of artificial intelligence, improves comprehension by transforming structured data into clear, natural language. There’s a broader story that needs to be told in addition to explaining the graphs, the overall story is told through explaining the relationships between visualizations. NLG augments analysis by meeting citizen data scientists in their place of expertise and fills the data literacy gap to break down the barrier to ensure each user has contextual understanding of what they’re looking at so they can best decide what to do about it. NLG supports citizen data scientists by allowing any person, regardless of their role, position, or level of data expertise, to understand the data presented, know what it means in its entirety, and act on it with confidence because of the mode in which insights are presented–the written word. It acts as an expert analyst guiding viewers through visualizations and talking them through insights using the most natural form of communication.

  1. Provide Adequate Training for Success

Training programs for developing citizen data scientists builds the skillset required to thrive in their new position, this requires intentionality and prioritization of training on their management’s behalf. The leap from “employee with a passion for statistics” to citizen data scientist takes a new way of thinking, processing, and looking at data. Instead of simply investing in a short training (while still beneficial), invest in continued technical training to develop new skills, strengthen existing ones, and provide a deep working knowledge of platform tools. Ongoing training empowers citizen data scientists to evaluate analytic possibilities, go beyond surface-level data insights, and ask the right questions that will yield the best results. There’s ample training available through online courses, executive education programs, vendor software training and tips, webinars, conference sessions, and community meetups aimed at people whose career focus isn’t primarily data science, analytics, and statistics with the goal of enhancing the outcome and reducing the time spent incorporating themselves into their new role.

  1. Harmonize the relationship between citizen and expert data scientists

Emerging citizen data scientists don’t threaten or replace expert data scientists, data analysts, or business analysts. Citizen data scientists compliment and supplement their expertise by alleviating simpler tasks, like generating regular reports or one-off requests. They remove the bottleneck of reliance on expert analysts to prepare core data sets and infrastructure and, at the same time, bolster findings with the advantage of their business intimacy. There will always be a need for the unique experience, education, and high-level skills of data scientists. As the citizen data scientist explores data, generates mildly sophisticated reports, and develops hypotheses, data scientists can reign in further exploration, developing advanced models, and doing the time-consuming work of data wrangling in all forms.

It’s easier to train subject matter experts to use data science software and BI platforms than it is to train data scientists to understand the intricacies of many business sectors. Raising up and empowering citizen data scientists, holding their expert level of industry knowledge, makes significant contributions to an otherwise inaccessible field of data science. Gartner states, “By 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader use by citizen data scientists.” This emerging wave of new positions isn’t just coming, it’s here and will only grow.

About the author

Marc Zionts (Chief Executive Officer, Automated Insights) is a technology executive and entrepreneur who’s been successfully leading companies since 1987. He currently serves as the CEO of Automated Insights, a Vista Equity Partners owned company. Zionts is also an Independent Board Director for Pivot 3, TEOCO, and Friends of the Earth, an environmental group based in Washington, D.C. Zionts earned a BS in industrial design and MS in management from the Georgia Institute of Technology.