Teaching data science to people you already have on staff can help alleviate the massive shortage of data scientists. Here’s where to find them.
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.
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Does this mean that individuals without formal data science backgrounds can take on data science work? It’s an open question for organizations that want to build their data science expertise in the face of glaring industry shortages of data scientists and the cost-prohibitive salaries that these highly sought-after professionals can command.
Where citizen analytics is working
To consider the citizen data scientist question, it’s helpful to first take a look at where citizen development in analytics is working in today’s organizations.
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The current sweet spot for citizen development and analytics is in the development of Microsoft Excel spreadsheet reports and the use of no-code or low-code report generators that do not require much IT involvement. With these tools, end-user citizen developers can develop their own analytics reports and what-if analyses. It’s also important to note that a majority of these analytics reports use structured data and not the unstructured big data that so much data science work depends upon.
Enabling citizen data scientists
To make the transition from straightforward analytics report production to more complex data science work that involves structured and unstructured data and the generation of data models, it’s necessary to upskill non-data scientists so they can acquire a working knowledge of data science skills.
Stephen Watts, writing for BMC, a software company, explains the set of skills needed: “The right person is someone who understands the vision, mission and needs of the company, and how data helps propel their needs … . [He or she] can think outside the box, coming up with data models and connections that go beyond what the average layperson would conceptualize [and he or she] must be analytical.”
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Watts goes on to say that “Being able to perform fairly complex data analysis is part of the job. It’s important for a citizen data scientist not only to assess the data in front of them logically but also to draw meaningful conclusions from it that the average person might not see.”
Because of the emphasis on data know-how, when companies look for citizen data science candidates, it makes sense to review the ranks of disciplines within the company (e.g., engineering, software development, data analysis) where some of these skills may already be resident. For example, a data analyst in IT or a software developer is likely to grasp the idea of modeling data for analysis easier than an individual in purchasing, marketing or accounting.
There is also an added bonus if companies can cross train employees who have business acumen and strong relationships with end users throughout the company. These people might have more ability to understand and dissect the information needs of the business than a data scientist who knows math, statistics and analytics—but who has no working knowledge of the business.
Can citizen data scientists supplant data scientists altogether? No. But they can ease the load on the data science staff.
Finally, there is a pitch for IT leaders, and it is this: If your company wants to develop citizen data scientists, the most logical place to look for them is in IT.
Data analysts and software developers already know the data and how to model it. They’ve worked with end users and can extract the salient questions from the business that data science needs to answer, and they also understand the need for quality data that has been properly cleaned and prepared before it is queried.