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The Care and Feeding of the Citizen Data Scientist

How to find, develop, and nurture a new breed of analysts.

• By Brian J. Dooley
• November 13, 2018

With the growing importance of data science and the continuing skills shortage at the highest levels of analytics capability, the role of the citizen data scientist (CDS) is extremely significant today. For decades, enterprises have included some individuals with business expertise capable of bridging the gap between business and IT by finding data-driven solutions to business problems using accessible self-service analytics software. The CDS term was first defined by Gartner in 2016 as "a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics."

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This role has become increasingly important with the explosion of analytics, AI, machine learning, and big data throughout the workplace during the past several years. It also helps to mitigate the problem of business-IT alignment that has existed since computers were first used to solve business problems.

The CDS role will continue to rise in importance over the next several years, but it requires special nurturing. We described the structure needed to support this role within a broader context in, "Reimagining the Analytics CoE." The CDS role demands greater understanding and a critical range of support available to ensure that these individuals remain productive.

Nurturing the CDS

Citizen data scientists need:

• Access to technical training for building new skills, reinforcing existing skills, and understanding new analytics developments
• Training in relevant company policies such as those for data usage, data access, modeling, and software development
• Deeper understanding of data characteristics, access, security, and management
• Support for modeling and algorithm selection and development
• Incentives and recognition for analytics usage
• Career paths incorporating analytics subspecialties
• Access to the greater analytics community
• Established processes governing interactions between analytics and IT departments and the CDS
• Access to programs and data suitable to CDS analytics and business needs and understanding
• Appropriate infrastructure to ensure adequate data access, governance, security, and oversight

As the CDS role matures and becomes better defined, it will continue to expand across the enterprise. A basic understanding of data is already a requirement of modern management; as further automation enters the workplace through robotic process automation (RPA) and other automation initiatives, this requirement will only broaden. Familiarity with analytics and data will become part of the basic toolbox of knowledge workers throughout the enterprise as well as employees in an increasingly wide range of occupations.

Developing CDS Skills

Provision of adequate training is at the forefront of the move into CDS. Business analysts often understand the fundamentals of statistics, but new ways of thinking need to be encouraged to understand the data science point of view. Ad hoc, spreadsheet-based solutions will be increasingly out of phase with business needs for repeatability, accuracy, and transparency. The CDS must know how to evaluate analysis possibilities, ask the right questions, and evaluate the types of models and approaches likely to yield a repeatable, accurate result.

Self-service software designed to aid the CDS plays an important part in this by managing some of the less-familiar aspects of data preparation and model selection. Training, however, provides a broader understanding that will both enhance results and reduce the time that data scientists need to spend fixing models and verifying results.

Training is available in many forms, from software training and guidance from vendors and practitioner communities to online course providers and colleges and universities.

Support for a New Role

Above all, the CDS needs to be integrated into the operations of the enterprise. This requires defining a coherent career path, supplying appropriate incentives, and ensuring that CDSs understand their responsibilities and role within the organization. Because this role is likely to become a permanent fixture, it will also require adequate professional support. Hiring decisions need to be made, and CDS skills need to be considered when establishing work teams.

The emphasis for these individuals must be on business knowledge first, so the ideal candidates include current business analysts who have demonstrated business skills and an aptitude for data analytics work. As the concentration on data science continues to grow in both the workplace and in academia, finding these skills is likely to become easier.

A CDS also needs to be always learning. Analytics continue to develop at a breakneck pace, and yesterday's skills are no longer sufficient. Individuals in this role must accept that they must always focus on the future.