The citizen data scientist is a role that’s received a lot of attention from media, bloggers, and analysts over the past year. There’s a lot of talk about what citizen data scientists do and how they can change business analysis. But what, exactly, is a citizen data scientist?
“Citizen scientist” is not a new concept, having been around for decades in the space-science and earth-science worlds. However, with both of those types of citizen scientists, material and information are made publicly available by a government or organization, and “regular citizens” are invited to use it to help solve a defined problem. The collective efforts of these citizen scientists can be used to develop solutions in a low cost and (hopefully) quicker timeframe than if the government or organization went at it alone.
The way the business world defines citizen data scientist is a bit different.
Gartner recently defined 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.”
A citizen data scientist could be an accountant, a marketer, a customer service rep, an IT manager, or even a CFO. They are not hired by an organization to be a data scientist. They already work inside the organization, filling a role outside of data science.
High-level data scientists are strong in programming and math, with a “big picture” understanding of the business. They aren’t likely to have the in-depth knowledge of the departmental and functional components of the business that someone who has been involved in a particular area for some time would. That’s where citizen data scientists fit in. Normally, they are intimately involved with functional business areas, like finance, sales, operations, and customer support, and have a greater understanding of the challenges being faced in those areas.
That’s why the most effective citizen data scientists are often found within functional areas of the business. Current team members are best suited to add valuable business context to analytics initiatives, as well as to prioritize these efforts based on potential business value.
How to Identify Citizen Data Scientists within your Organization
Because citizen data scientists can come from many different departments within your organization, you could have hundreds or even thousands of people who potentially could fill the role. Not all of them are necessarily a fit, however. While experience in computer science and statistics is not required for a citizen data scientist, certain people fit the role better than others.
Following are eight characteristics and traits that indicate a person may have the aptitude to successfully fill the role of citizen data scientist. To find the best people for the role, look for people who:
- Embrace a data-driven work style. They tend to make decisions based on irrefutable fact rather than gut feelings or experiential anecdotes.
- Are committed to getting actionable and timely results. They’re willing to work with imperfect results in urgent settings if the alternative is to wait for all data to be delivered through centrally managed reports.
- Have an in-depth understanding of some aspect of the business and its data. They’re close enough to the data to set objectives, form hypotheses and ask questions of it.
- Are inquisitive by nature. They challenge conventional wisdom and question everything.
- Are persistent. They are undaunted by the need to often try many variants of their analysis before getting it right.
- Build cross-departmental relationships. They’re able to partner with many types of people, accommodate various working styles and effectively communicate data results.
- Have struggled with data. They have experienced the challenges of accessing and preparing the data they need.
- Thrive on innovation. They are always looking for a better way to do something and experimenting with new tools.
Growth and Future of Citizen Data Scientists
Gartner predicts that through 2017, the number of citizen data scientists will grow five times faster than the number of traditional data scientists. Training these people effectively will become a key factor to determining the success of an organization’s deployment of citizen data scientists.
However, a major challenge for some organizations when attempting to develop a team of citizen data scientists is the lack of an appropriate training plan. Training the citizen data scientist in the process and educating them in the use of emerging tools is critical to the success of the model.
One of the major considerations when assembling a toolset to empower the citizen data scientist is ease of use. The traditional tools used by the data scientist require extensive training in various scripting languages to perform the necessary analytics and modeling. Most individuals within the organization do not have that depth of knowledge. They are, however, familiar and comfortable with using point and click interfaces with minimal configuration. Providing self-service data preparation and data visualization tools that can deliver interactions through this type of application allows the citizen data scientist to overcome the major hurdle of presumed technical knowledge. The most powerful of these technologies also greatly extend the reach of traditional data scientists by allowing them to package their specialized analytical knowledge in ways that make them much more accessible to citizen data scientists.
With the appropriate tools in hand, citizen data scientists can get to the level where they can extract meaning from data to solve business problems or take advantage of opportunities.
As the volume of data continues to grow, businesses must adapt to take full advantage of the insights that data offers. General awareness of data and analytics also will continue to become more pervasive throughout the business. Centering the analytics process on a small group of highly technical data scientists is no longer an effective way to accomplish the goal of data analysis. Getting more qualified people involved – especially those with intimate knowledge of the business and its customers – is becoming essential for mining the valuable nuggets of insight that lie within the data.
Dan Donovan is Vice President of Market Development and Strategy, Financial Services at Lavastorm. He also has held several other strategic roles at the company, including Lead Technology Evangelist and Director of Customer Solutions. Prior to joining Lavastorm, he served in technical roles at companies including Telution, Ikon, and FreeDrive. Mr. Donovan has over 15 years of software and financial services experience and has a deep understanding of the data analytics challenges faced by financial services firms.
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