In a previous blog, I discussed the growing importance of citizen data science due to the scarcity of data scientists, the emergence of data science and machine learning platforms (DSML), and the scarcity of business translators. To effectively integrate the role of citizen data scientists (CDSs) within an organization, it’s important to define the required expertise, skills, tasks, and leadership decisions necessary for their success. So, what expertise is needed? What kind of involvement is required from data and analytical leadership to maximize the benefits derived from CDSs?

Kartik Patel from Gartner highlighted that although CDSs are tasked with creating models for predictive and prescriptive analytics, they often lack formal training in computer science or data analysis. Predominantly, their main job functions lie outside the realms of analytics or statistics. However, with the support of advanced data technologies and tools, CDSs are able to analyze data and make informed business decisions. The skills of a CDS blend basic business analyst capabilities with some data science knowledge, enabling them to conduct data mining, exploratory analysis, data visualization, and possess some programming, quantitative, and data interpretation skills. Alongside these technical capabilities, problem-solving skills are crucial, enabling CDSs to identify business problems, specify models to address these problems, and use augmented analytics platforms to implement these models effectively.

How can data and analytical leaders maximize the benefits?

Data and analytical leaders can elevate the impact of CDSs in domains like AI and machine learning, but success largely depends on leadership support. Promoting the role as a viable solution to bridge the data science gap can help organizations develop better analytics products.

Gartner recommends four key actions for leaders:

1. Build a CDS-compatible ecosystem: Simply possessing the right knowledge and skills does not make someone a citizen data scientist, especially if they work in isolation. Access to ETL processes and robust analytics is crucial. An ideal ecosystem includes supportive roles such as business translators, developers, data engineers, and machine learning architects, who collaborate and contribute to effective data analysis and problem-solving. Strong data governance and an understanding of the organization’s data needs are foundational to this ecosystem.
2. Embed augmented analytics with incremental capabilities: Instead of making abrupt, major upgrades to analytical tools, gradually enhancing their capabilities can prevent CDSs from becoming overwhelmed. These progressive enhancements should help bridge the skill gap between CDSs and other technical roles, supporting advanced functions like data storytelling, feature engineering, and natural language queries.
3. Involve CDSs in the business: For CDSs to truly add value, they must be actively involved in projects that align with existing business processes and address well-known opportunities. Communication of models and analytical results is crucial, as is utilizing the insights generated to avoid wasteful efforts. Moreover, introducing transformational projects that require closer collaboration with expert data scientists can foster innovation.
4. Facilitate collaboration between citizen and expert data scientists: While CDSs are not replacements for expert data scientists, their collaboration can enhance efficiencies. CDSs can handle simpler analytical tasks, allowing expert data scientists to focus on more complex issues and validate models before deployment. Building effective collaboration channels between these roles is essential for leveraging the full range of data science capabilities within an organization.

In summary, embracing citizen data scientists within an organization involves recognizing their potential, providing a supportive ecosystem, and aligning their roles with strategic business goals. This approach not only helps in filling the gaps in data science capabilities but also enhances the organization’s overall data-driven decision-making processes