Transformation and Adaptation in Data Science: Emergence of the Citizen Data Scientist
Role Related Research
One trend that I had been cautiously curious about is the emerging Citizen Data Scientist role. Citizen Data Scientist refers to an advanced data analysis professional or data professional who wants to do more than basic data analysis, they want to create or generate models that leverage predictive or prescriptive analytics or machine learning. This individual typically has a primary job function that is outside of the field of statistics and analytics. I conducted a literature review about Citizen Data Scientists and learned that while Gartner created the term several years ago, there are conflicting perspectives about the definition, scope and truthfully about the value of this role (Wilson, 2020). After examining multiple definitions of what a Citizen Data Scientist is, I learned that this role is significant mostly for what it helped me to understand about organizational change and adaptation.
A Closer Look: What Is a Citizen Data Scientist?
One reference described Citizen Data Scientists as software power users who can do moderate data analysis tasks. They don’t replace expert data scientists. Instead, they use software features like drag-and-drop tools, prebuilt models, and data pipelines to create models without code (GetApp, 2019).
Several references describe Citizen Data Scientists as a new type of business analyst with diverse business responsibilities. They apply sophisticated analytical tools, and complex methods of analysis (mostly around big data) to improve business results — all without the training or assistance of Data Scientists or IT team members (Blais, 2020).
While there are differences in definitions of the role some commonalities include:
- Citizen Data Scientists come from Lines of Business
- Role resides somewhere between a Business Analyst and Data Scientist
- Performs more complex analysis than a Business Analyst
- Rely on specialized tools, software and building blocks from more advanced roles
Figure 1: Relationship of Citizen Data Scientists to Other Roles
What are the skills of a Citizen Data Scientist?
Citizen Data Scientist is not a role that organizations are looking to fill from external sources. Most of the references about the role touted that you can’t find job postings for this role. Spoiler Alert: I did find one job posting that accurately reflected some of the common definitions of Citizen Data Scientist. But that singular job posting aside, that statement is generally true. Very simply stated Citizen Data Scientists “do cool things with data” (Ghosh, 2021). I learned that there are basically 4 core skills of a Citizen Data Scientist, although various references are not aligned about how involved this role is in the area of model development and coding. Citizen Data Scientists primary and secondary skills include;
DATA PREPARATION & EXPLORATION
Work with large data sets (Big Data)
Data preparation, algorithms and queries
Document the data extraction process
Ability to visualize data
DEVELOP MODELS
Knowledge of data models and statistics
Coding / proficiency in at least one programming language
Modify, create and deploy predictive models
Create data science models using advanced and predictive analytics
ANALYSIS & INSIGHTS
Strong analytical skills
Ability to perform fairly complex data analysis
Power users of business applications such as (familiarity with spreadsheets)
COMMUNICATIONS
Document their findings and communicate that with business staff, business Analysts, Business Intelligence(BI) and IT leaders.
Why all the hype around Citizen Data Scientists?
Some people may be quick to dismiss this role and the hype around it mostly because they are a bit confused about the title. I initially affiliated Citizen Data Scientist with the term “Citizen Scientists” which meant more of a hobbyist. For these reasons, it did not seem relevant to the primarily large enterprise contexts that I historically work in. However, digging under the hood to learn what factors lead to its emergence, uncovered some interesting findings.
High Demand. The primary driver for the emergence of this role is the high demand for, and shortage of trained Data Scientists (Maffeo, 2019). Gartner predicted that, ‘the number of Citizen Data Scientists will grow five times faster than the number of highly skilled data scientists” (Patel, 2020). The growth projections and supply of people who may be called Citizen Data Scientists is larger than the pool of Data Scientists. Companies are exploring different adaptations in people, technology, process and other areas to meet those needs (Arora 2021,Tibco, VentureBeat)
Digital transformation initiatives have impacted every aspect of how organizations do business today. These data-driven changes have led to more and more business leaders turning to Citizen Data Scientists to fill the gap between the demand for data and analytics and the limited supply of skilled data scientists in the market today (Tibco).
Big Data. Behind most of these transformation initiatives is the need to generate insights from large quantities of data. There are cost and time impacts associated with working with bigger and more complicated datasets. A new skill set is needed to meet these challenges. As the data gets more complex and large, it increases the length of time it takes to get data for reports and analysis. In many industries, there may also be changes in reporting requirements that add a layer of complexity to the task of working with Big Data (Chmiel, 2021).
Business Context. Data Scientists don’t always have the business context for their work to have maximum impact. Knowledge of the business context where a Citizen Data Scientist adds the greatest value.
Experimentation. With all this data, enterprise business users will want to experiment and try different hypotheses.
Career Growth. For some people, the idea of progressing towards a Citizen Data Scientist role seems like a more attainable path to becoming a Data Scientist.
What does this role need to succeed in an organization?
In order to meet these complex needs, there are a number of conditions that need to be in place.
Accessible Tooling. Various sources describe Data Scientists as somewhere between a business user using self-service analytics and a data scientist who is well versed in advanced analytics. Given the skill limitations, Citizen Data Scientists need augmented or new self-service tools to do big data analytics or augmented analytics. Several resources refer to the need for self-service “point-and-click” or “drag-and-drop” tools. New tools need to be easy to use, taking into account the lack of skills such as coding, statistics and automation (pipelines). Additionally, these tools need to make accessing data easier. With these new tools, developers need to be more conscious of the human-computer interaction requirements.
Collaboration. Because Citizen Data Scientists are primarily in the lines-of-business, they can benefit by working closely with more formal Data Scientists in the organizations. Data Scientists will continue to work on advanced analysis and statistics. Additionally, they can create processes, tools and infrastructure to support Citizen Data Science (Sakpal 2021, Tibco).
Invest in Training. Organizations need to invest in reskilling or upskilling people who take on Citizen Data Scientist roles. Based on the lack of agreement on the primary and secondary skills, organizations will need to do a thorough skills assessment to understand the full scope of the skills. Additionally, the Data Scientist Role will need to include process development for Citizen Data Scientist and an approach to validate the quality (QA) of the Citizen Data Scientist’s outputs. Because of the organization-specific differences, Citizen Data Scientist training can not support a one-size fits all approach. In exploring several of the companies that surfaced during this literature review, I learned that a number were investing in training initiatives.
Organizational Policies. Because of the changes in people, technology, process — organizations need to be deliberate and establish policies to make this new model of data science work (Blais). For example, there needs to be appropriate levels of transparency and sharing as the numbers of people doing aspects of data science increase (Tibco, Chmiel 2021).
Business Leadership. For business leadership, it’s important for them to be aware of these changes and to deliberately create the conditions to help Citizen Data Scientist work (SAS).
What are the impacts and benefits of cultivating Citizen Data Scientist Roles?
Benefits to Individuals
At least one author believes that this is a viable career choice for someone that wants to get into Data Science without the “time and expense” of an advanced degree (Arora, 2021).
Benefits to Organizations
Gartner predicted that in 2019, this role would have a larger impact on businesses than traditional Data Scientists (Datarobot). Experienced or skilled data scientists will be able to focus on more complex problems. Smarten talks about data democratization initiatives which can “optimize the time and resources of Data Scientists and improve productivity, empowerment and accountability for business users” (Smarten Blog, 2021). The emergence of the CDS role enabled the functional areas to draw the most benefit from data given the limited DS resources and to do that in a way that enables IT to maintain the most appropriate level of process and security over the systems.
I mentioned earlier that company policies and relationships will need to change. This will also impact the IT organization who has to make the bulk of policies and procedures. Security and governance policies will be more critical.
Benefits to Industry Overall
As ML gets more simplified you will see more of the companies who have not started their AI/ML journey applying Data Science and machine learning.
What does this all mean?
I explored some of the implications for individuals, organizations and even industries but what does this mean in the context of UX? Taking the time out to understand a user role that is outside of my normal scope was a valuable exercise. It helped me to bust some biases I had that prevented me from seeing past the title. It also highlighted the critical role that HCI and UX can play in the enterprise context. In this exploration of the Citizen Data Scientist role, I learned that perhaps the confusion about the title / role was a signal of important transformations and corresponding adaptations that organizations need to make.
