”There aren’t enough expert data scientists to meet data science and machine learning demands, hence the emergence of citizen data scientists. Data and analytics leaders must empower “citizens” to scale efforts, or risk failure to secure data science as a core competency”. – Gartner 2019
As data science provides competitive advantages for organizations, the demand for expert data scientists is at an all-time high. However, supply remains pretty scarce for that demand! This limitation is a threat for enterprises’ competitiveness, and in some cases, their survival in the market.
In response to this challenge, an important analytical role providing a bridge between data scientists and business functions was born: the citizen data scientist.
What is a citizen data scientist?
Gartner defines the citizen data scientist as “an emerging set of capabilities and practices that allows users to extract predictive and prescriptive insights from data while not requiring them to be as skilled and technically sophisticated as expert data scientists”. A “Citizen Data Scientist” is not a job title. They are “power users” who can perform both simple and sophisticated analytical tasks.
Typically, citizen data scientists don’t have coding expertise but can nevertheless build models using drag-and-drop tools and run prebuilt data pipelines and models using tools such as Dataiku. Be aware: citizen data scientists do NOT replace expert data scientists! They bring their own expertise but do not have the specialized expertise for advanced data science.
The citizen data scientist is a role that has evolved as an “extension” from other roles within the organization! This means that organizations must develop a citizen data scientist persona. Potential citizen data scientists will vary based on their skills and interest in data science and machine learning. Roles that filter into the citizen data scientist category include:
- Business analysts
- BI Analysts / Developers
- Data Analysts
- Data Engineers
- Application Developers
- Business line manager
How to empower citizen data scientists?
As expert skills for data science initiatives tend to be quite expensive and difficult to come by, utilizing a citizen data scientist can be an effective way to close the current gap.
Here are ways you can empower your data science teams:
Break enterprise silos
As I’m sure you’ve heard this many times before, many organizations tend to operate independently in silos. Mentioned above, all of roles are important in an organization’s data management strategy, and they all have expressed interest in learning about data science and machine learning skills. However, most data science and machine learning knowledge is siloed in the data science department or specific roles. As a result, data science efforts are often invalidated and unleveraged. Lack of collaboration between data roles makes it difficult for citizens data scientists to access and understand enterprise data!
By establishing a community of both business and IT roles that provides detailed guidelines and/or resources allows enterprises to empower citizens data scientists. It is important for organizations to encourage the sharing of data science efforts throughout the organization and thus, break silos!
Provide augmented data analytics technology
Technology is fueling the rise of the citizen data scientist. Traditional BI vendors such as such as SAP, Microsoft and Tableau Software, provide advanced statistical and predictive analytics as part of their offerings. Meanwhile, data science and machine learning platforms such as SAS, H2O.ai and TIBCO Software, provide users that lack advanced analytics capabilities with “augmented analytics”. Augmented analytics leverages automated machine learning to transform how analytics content is developed, consumed and shared. It includes:
Augmented data preparation: machine learning automation to augment data profiling and quality, modeling, enrichment and data cataloguing.
Augmented data discovery: enables business and IT users to automatically find, visualize and analyse relevant information, such as correlations, clusters, segments, and predictions, without having to build models or write algorithms
Augmented data science and machine learning: automates key aspects of advanced analytics modeling such as feature selection, algorithm selection and time-consuming step processes.
By incorporating the necessary tools and solutions and extending resources and efforts, enterprises can empower citizen data scientists!
Empower citizen data scientists with a metadata management platform
Metadata management is an essential discipline for enterprises wishing to bolster innovation or regulatory compliance initiatives on their data assets. By implementing a metadata management strategy, where metadata is well-managed and correctly documented, citizen data scientists are able to easily find and retrieve relevant information from an intuitive platform.
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