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Citizen Data Scientists are not business analysts or data scientists. They are business users with domain, market, or professional skills who will benefit from using data analytics. The ideal Citizen Data Scientist is a person who is respected in the enterprise, one who shares data and information with others to collaborate and produce positive outcomes, and one who is curious and likes learning new things. Citizen Data Scientists can be champions within the organization to spread the word and become role models for other team members so that, over time, the business can cascade this approach to all business users and achieve successful user adoption, improve data literacy and engender digital transformation.
To empower citizen data science (CDS), D&A leaders need a comprehensive ecosystem that includes people, tools, data, and processes. A superficial assumption that citizen data scientists have the knowledge and skills needed to access, transform and investigate data for conducting robust, advanced analyses is futile. A data literacy program is a must for data scientists to access, use and make sense of the data provided. Complementary roles such as business translators, developers, data engineers, and machine learning architects together can support citizen data scientists to fill in the skill gaps that they lack.
Organizations should consider incrementally adding capabilities that extend the analytics tools already in use instead of a "big bang" approach. This means that D&A leaders need to provide extensions to the existing tools used by citizen data scientists and not overwhelm them with entirely new tools. The first step is to conduct a holistic analysis of the existing tools and capabilities that citizen data scientists use and identify gaps. The tools need to complement the capabilities of CDS, such as data storytelling, data preparation, direct querying using natural language queries, operationalization of analytics models, and so on.
Business extension projects are the go-to opportunities for citizen data scientists to exhibit immediate value-add to their organizations' D&A strategy. For example, D&A leaders can start by identifying existing processes in the organization that require repetitive decision-making. Data scientists can be leveraged to perform repetitive and redundant tasks in the analytics workflow, and therefore create value to the organization, while allowing expert data scientists to focus on more complex tasks.
Citizen data scientists cannot replace expert data scientists; they are a complement to existing analytics roles. Citizen data scientists should not leverage self-service data science platforms in a siloed manner. Instead, they should participate in the development process with the expert data scientist who will eventually be responsible for validating those models before moving them into production.
Data literacy is the ability to understand and communicate analytical techniques and their insights. In addition to interpreting data and drawing insights, a good data literate person asks the right questions to creatively solve the problem. The best way to improve data literacy for citizen data scientists is to collaborate with an expert data scientist or analyst. Platforms such as Kaggle make it possible.
Predictive analytics is the most important part of businesses. With the advent of advanced predictive tools and technologies, companies have expanded their capability to deal with diverse forms of data. There are various applications of predictive analytics in businesses such as customer segmentation, risk assessment, sales forecasting, and market analysis. With predictive analytics, businesses have an edge over others as they are able to foresee future events and take appropriate measures with respect to it.
In the past, many businesses would make poor decisions due to the lack of surveys or sole reliance on 'gut feelings'. It would result in some disastrous decisions leading to losses of millions. So this is where citizen data scientists play a vital role to leverage data for their operations.
After making decisions through the forecast of the future occurrences, it is a requirement for the companies to assess them. This is possible through several hypothesis testing tools. After implementing the decisions, citizen data scientists will help businesses to understand how these decisions affect their performance and growth. If the decision leads to any negative factor, then they should analyze it and eliminate the problem that is slowing down their performance.
Citizen data scientists can use technologies like image recognition and are able to convert the visual information from the resume into a digital format. It then processes the data using various analytical algorithms like clustering and classification to churn out the right candidate for the job.
To become a successful citizen data scientist, you needn't have advanced degrees in Math or Computer Science. Instead, pick up an analytical mind and learn to create compelling stories with data. Being a citizen data scientist requires more than technical skills; it means understanding the business problem and finding insights that others can use in the organization. The best way to do this is to speak to people and find out what issues they need solving and how they want these results presented.
