A citizen data scientist is defined as a professional who utilizes predictive analytics to address specific business challenges, bridging the gap between business users and expert data scientists. They possess high analytical abilities and a strong understanding of their industry but do not require formal training in advanced data science. Their integration into organizations can enhance analytics maturity and operational efficiency by focusing on collaboration with expert data scientists and utilizing appropriate tools and strategies.

1. What Is a Citizen Data Scientist?
Gartner defines the term "citizen data scientist" as " A person who designs or
develops models that use the power of predictive or prescriptive analytics,
having an in-depth understanding of business problems and domain
knowledge." These data scientists bridge the gap between people who do
self-service analytics(Business users) and those performing complex analytics
as data scientists.
Citizen data scientists usually work in a specific business area like sales,
marketing finance, finance, or human resources. They have an in-depth
understanding of the challenges that their respective departments are faced
with. Armed with powerful software, they conduct a thorough diagnostic
analysis and build machine learning models to enhance the tasks that used to
require an expert in data science, statistics or mathematics.
The Role of a Citizen Data Scientist
The primary duties of the citizen data scientist typically don't involve handling
data. They are professionals in the mainstream who use predictive analysis to
make better decisions that affect their performance at work, for instance,
someone working in IT at an investment bank with a knack for analytics can
be a good pick for the role of a citizen data scientist. Professionals in data
analytics typically concentrate on identifying trends in the data of past events
(descriptive-analytics) and present their findings in static reports, dashboards,
and graphs. Nowadays, however, citizen data scientists, computer engineers
and data scientists, and AI developers are equipped with powerful tools to
tackle business issues by using data that is greater in range than their data
analytics professional peers.
They can build models that recommend the next best option and identify
potential customers who are most likely to purchase an item, identify the loans
most likely to be repaid or be defaulted, and many more.
Citizen data scientists can be described as "power users" who can carry out
easy and moderately sophisticated analytical tasks that once required more
skill & expertise.
Nowadays, citizen data scientists can perform the same function as expert
data scientists. They cannot replace experts since they don't possess
advanced, specific data science expertise, BUT they certainly bring their
expertise and special skills to the table.

2. The skills of a citizen data scientist
To be a citizen data scientist, you must possess the following skills:
 Organizational Context: The right person knows the business's
mission, vision, and needs and how data can achieve its goals.
 Strategic thinking: The Ideal citizen data scientist thinks outside
the box and comes out with connections and data models beyond
what an average employee from the same team would imagine.
 High analytical abilities: A citizen data scientist is expected to
be analytical as the main requirement of the job. Being able to
perform complicated data analysis is an essential part of what
they do.
 Ability to assess the significance of information: The citizen
data scientist needs to analyze the data before them rationally
and draw conclusions that an average may not recognize.
 Emphasis on the business value Data analysis is a key part of
the job. The Citizen Data Scientist should understand their work to
advance efficiently so that they are able to step up from their
current tasks with ease to more valued tasks.
 Adjacency to industry: The most suitable candidates for a
citizen data scientist are in a field closely related to data science,
which involves a lot of math and analytical processes. Software
engineers and developers might be suitable candidates for this
job.
Why are Citizen Data Scientists Important?
Companies are increasingly utilizing analytics to enhance all aspects of their
business, including pricing decisions and hiring methods.
But, many companies find that their IT infrastructure isn't up to speed with the
demands to integrate analytics into their day-to-day activities. Citizen data
scientists fill the space.
Citizen data scientists do not require formal training in mathematics or
analytics, nor do they need to possess an in-depth IT understanding of the
systems. They need to be aware of whether the results are acceptable and
seek help when they need it.

3. How to Use Citizen Data Scientists to Maximize Your
D&A Strategy
Data and analytics (D&A) leaders must encourage and support the function as
a legitimate strategy to produce analytics to allow citizens to succeed. To do
this, D&A leaders need to follow these four steps.
1.) Create an ecosystem of CDS
To enable Citizen Data Science (CDS), D&A leaders require a robust
ecosystem that comprises people, tools, data, and procedures. The
assumption that citizen data scientists possess the expertise and capabilities
to access, transform, and analyze data to conduct robust, advanced analyses
is false. A data literacy program is essential for all citizen data scientists to
gain access to and make sense of the data available.
"Citizen data scientists should not be working in isolation."
The roles that complement each other, such as developers, business
translators, data engineers, and machine learning architects, can help citizen
data scientists to fill in the gaps they do not have.
2.) Add capabilities that allow an enhanced augmented
analytics
Companies should add incremental capabilities that expand the current
analytics tools instead of taking the "big bang" approach. It means D&A
executives must offer enhancements to existing tools utilized by citizen data
scientists and not overburden them with completely new tools.
First, conduct a comprehensive study of the instruments and tools that citizen
data scientists employ and find the gaps. These tools should be able to
complement CDS' capabilities. The role of a CDS includes data storytelling,
direct querying, data preparation by using natural language queries, the
operations of analytics models, and more.
Augmented analytics is a method for conducting various steps, including
augmenting data preparation, the discovery of data, and enhanced data
science. D&A managers can include these features in the toolkit already in
place for CDS.
3.) Begin business extension projects which involve CDS
Business extension projects are the most popular chances for citizen data
scientists to show immediate value to their companies' D&A strategy.

4. For instance, D&A leaders can start by identifying processes within the
company that requires repetitive decision-making. Citizen data scientists can
be utilized to complete routine and redundant tasks within the workflow of
analytics. Consequently, adding value to the business allows expert data
scientists to concentrate on more challenging tasks.
When expanding the workflow for citizen scientists, D&A leaders need to keep
four things in mind:
 The priority of projects is to address, identify and map
opportunities related to current business processes and products.
 Let people communicate and share the models that you've
developed along with the results of analytics.
 Do not create shelf work -make use of what you have made.
 Incorporate transformative initiatives into the equation, working in
close cooperation with experienced Data Scientists.
4.) Create collaboration channels between citizen data
scientists and experts data scientists
Citizen data scientists cannot replace expert data scientists and are only an
addition to the existing analytics positions. "Citizen data scientists shouldn't
utilize their self-service platform for data science in a siloed way. They should
take part in the development process and the expert data scientists who are
ultimately accountable for validating the models before putting them into the
production phase.
D&A leaders must be focused on creating and enabling collaboration and
communication throughout the analytics process and include both experts and
citizens in roles in defining the collaboration and how to approach it.
CONCLUSION
Citizen Data Scientists can enhance the value of an organization's business
and analytics maturity. They provide industry and business expertise that a lot
of experts in data science are lacking. Their experience in business and
understanding the needs of companies allows them to efficiently integrate
machine learning and data science output into the business process. Make
sure to not miss out on the leverage provided by a Citizen Data Scientist
because of a lack of strategy.