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As the power of data science provides organizations with differentiating competitive advantages, the demand for talent is rising. Supply, meanwhile, remains too scarce to meet that demand. This has led to data science and machine learning (ML) being opened up to nontraditional roles, such as the citizen data scientist. According to Gartner, a citizen data scientist is 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. Download E-Book: The Future of Decisions These roles are often promoted as a silver bullet that can accelerate organizations into artificial intelligence (AI) and ML easily and cost-effectively. However, very few organizations have managed to harness the capabilities of citizen data scientists. “The biggest struggle organizations face is the lack of clarity of responsibilities of a citizen data scientist,” says Anirudh Ganeshan, Associate Principal Analyst, Gartner. “This vagueness creates hostilities among expert and citizen roles and impedes healthy collaboration and communication.” For citizen data scientists to be successful, data and analytics (D&A) leaders must enable, encourage and promote the role as a legitimate approach for producing analytics. To do so, D&A leaders need to perform these
