Empowering the citizen data scientist

Artificial intelligence, machine learning, and augmented analytics are terms we now see on an ongoing basis. Every second, companies around the world create and collect new data. They therefore cannot afford to sit idle as they seek to become more data-driven organizations to advance their Business Intelligence (BI) strategies. Being immersed in the field of BI and analytics as the CEO of a natural language generation provider, I have seen the demand from businesses large and small to become data driven and their need to more resources to achieve this. There is a growing demand for data scientists and analysts who play an invaluable role in managing, discovering, analyzing, structuring, cleaning, validating and communicating data for projects and business needs. business.

While programs designed to educate and train data scientists make their way into higher education, it will still take time for supply to catch up with demand. The number of job openings far exceeds the number of professionals available to fill these roles, leaving organizations to fill the void in other ways. People in various roles within an organization are now in positions where they routinely interact with an incredible amount of data. This shift and rise of business professionals using software tools and technology is helping bridge the gap between supply and demand for data scientist experts. These people are known as citizen data scientists. With the increase in data interaction, it is imperative to prepare citizen data scientists to succeed when it comes to understanding, communicating and acting on data.

Who are citizen data scientists?

According to Gartner, a The citizen data scientist is someone who builds models using advanced or predictive analytical tools, but whose primary role is not in statistics or analysis. These users have data science type skills but are not as advanced as data scientists. They are power users, such as business analysts, who can perform simple and moderately sophisticated tasks. Citizen data scientists have a unique perspective in their specific fields of practice to provide context on what the visualizations and patterns highlighted in a dashboard mean. Any data-driven business, regardless of size, requires resources to extract and act on meaningful insights to advance their business position. Here are some ways to help your organization’s citizen data scientists.

Democratize analytics with BI platforms

BI software tools and platforms, like Tableau (one of my company’s platform partners), Domo, and Sisense that take data and create visualizations to represent what the data means, democratize the enterprise-wide analytics and make it more accessible for users to gather insights, draw conclusions, and make data-driven decisions. The increased availability of data enables the distribution of information to people within an organization who previously did not have the access or tools necessary for the discovery process. Using the right BI software to help citizen data scientists perform the simplest tasks takes the pressure off expert-level data scientists, giving them the freedom to focus on tasks that make full use of their advanced skills.

Use natural language generation to increase data literacy

Natural Language Generation (NLG), a subset of artificial intelligence, transforms structured data into clear, natural language. Citizen data scientists are introduced to complex dashboards and asked to complete tasks that extend their knowledge and comfort with data. Visualization platforms equip them with the tools to paint a picture of the data they are analyzing, but they only make the best guess based on their level of expertise with the data. NLG meets them there and fills the gap to break the barrier, ensuring that each user has a full contextual understanding of the data and the confidence to drive action. NLG supports citizen data scientists by empowering any employee, regardless of role, position, or level of data expertise, to interpret the data presented, what it means, and what action to take. a way that translates visuals using the most natural form of communication – the written word.

Provide the right training and tools to succeed

Giving developing citizen data scientists the training to develop the skills necessary to thrive requires intentionality and prioritization as a supervisor. The transition from an analyst or “statistics enthusiast” to a citizen data scientist requires a new way of thinking and looking at data. Citizen data scientists need to know how to evaluate analysis opportunities, go beyond surface data insights, and ask the right questions to get the most effective results. Invest in technical training to build new skills and strengthen existing ones, as well as provide deeper practical knowledge of tools and programs applicable to their new role. Training is available in many ways: there are online courses from colleges and universities, vendor software training and guidance, webinars, conference sessions, and community meetups that all provide in-depth understanding to improve outcomes and reduce the time emerging citizen scientists spend integrating into their new role.

Harmonize the relationship between expert data scientists and citizen data scientists

The role of a citizen data scientist complements that of an expert data scientist whose expertise, experience and training cannot be replaced. Tools for processing, cleaning, and connecting data to get data numbers into a usable format is a time-consuming process that is in high demand by any organization wishing to leverage its massive, unstructured datasets. Citizen data scientists complement experts by easing their most basic tasks, such as generating regular reports or ad-hoc queries. The bottleneck of having to rely on expert analysts to prepare basic datasets and infrastructure is removed and enhanced through the commercial intimacy advantage of a citizen data scientist.

It is easier to train subject matter experts to use data science software and BI platforms than to train data scientists to understand the intricacies of many industries. Training and empowering citizen data scientists, who maintain their level of industry expertise, make significant contributions to an otherwise inaccessible field of data science.

Sean N. Ayres