The Emergence of Citizen Data Scientist: the democratization of analytics
By Othmane Lamrani · April 12, 2022 · 12 min read
Data has always been central to the growth of companies, but new technologies have made it easier than ever to collect and analyze data on a scale never before possible.
This has led to an increase in what has been termed citizen data or citizen data scientists – anyone who can use data to analyze and draw conclusions about the world around them, rather than just working with experts inside organizations.
This may sound like science fiction, but in reality, this trend is already here, creating an entirely new way of using data to enhance society that we’re only just beginning to understand.
However, there’s little clarity on what exactly it means to be called a citizen data scientist or how someone can become one, not to mention that there are many misconceptions about the term floating around out there that confuse people even more about it.
Here’s what you need to know about the emergence of citizen data scientists, and how you can be one too!
Summary
What is a Citizen Data Scientist?
Many companies have found it difficult to recruit, retain and develop the necessary skills in their data science teams.
Citizen Data Scientists can help fill this gap by using advanced analytics tools for self-service data preparation, predictive modeling, and machine learning.
The Harvard Business Review marked the data scientist boom in a 2012 article “Data Scientists: The Sexiest Jobs of the 21st Century” followed by unsustainable demand over the next decade.
Quanthub estimates that there is three times more job posting versus job search for Data Scientist.
Data Scientist shortage Graph from Quanthub
Citizen Data Scientists tend to work alongside data science teams but don’t necessarily have a background in computer science or mathematics or need to be able to write code.
They are typically business users who have an interest in analytics and a willingness to learn from other data scientists but don’t necessarily want to commit their careers to become an expert in data science.
The rise of citizen Data Scientists is being driven by several factors. The first is the increase in the amount of data being generated today which has led to an increased demand for analytical insight. Organizations want more insights into their business and this requires more people to consume them — not just Data Scientists and Analysts but also business decisions actors across the organization.
In addition, there is an increasing body of evidence that suggests that Citizen Data Scientists can complement existing data science teams by becoming more productive, efficient, and agile.
Last but not least, the emergence of citizen data scientists is enabled by more powerful software platforms that allow non-technical business users to access and use advanced business intelligence and data science tools.
Types of citizen data, from the UK Government Office for Science Future of Citizen Data Systems report (2020)
The No Code revolution
The emergence of citizen data scientists is a natural progression and part of the no-code or low-code revolution that we are witnessing.
Even more than that, it is an inevitability. In times past, many aspects of the business were considered too complex for ordinary people to understand. For example, in the 1940s and 1950s, computers were enormous machines that required teams of specialists to operate. No one outside the programming department ever saw them in action.
But by the 1980s, PCs had come into widespread use and most people knew how to operate them.
The same pattern plays out time and time again as technology becomes commoditized, simplified, and easier to use. As this happens, citizen data scientists will become as common as citizen programmers or citizen computer operators before them.
It’s not just about getting rid of code, it’s about making it possible for non-technical people to solve problems, and build applications and their own businesses.
I remember the first time I built a website. By the time I was done, I had taught myself HTML, CSS, and JavaScript. For me, it was a great experience because I did it in my free time for personal use with no time constraints. But most people don’t have that same experience.
Most people find it confusing and tedious and hard to understand why anyone would ever want to do this kind of work. Most people don’t have the patience or interest in learning how to build complex systems – they’d much rather be using them than building them!
Now anyone can create sophisticated websites (WordPress, Wix, etc) without having to write a single line of code.
This opens up new possibilities for non-technical people to create great content.
“Under very challenging and difficult circumstances, low-code was able to showcase its value, delivering new apps for organizations within days and sometimes overnight.”
The no-code revolution didn’t stop at the creation of websites.
A new wave of platforms and tools are helping anyone create software, web apps, and mobile apps without writing a single line of code. Online services such as Zapier and IFTTT have long offered ways of automating processes and integrating different technologies.
There are now also more traditional software companies getting involved, from Microsoft Azure to Salesforce App Cloud. Forrester predicts that 75% of all enterprise software will be built with low-code or no-code technology in 2022.
In the last few years, the no-code revolution reached a domain longly limited to data engineering: a modern data stacks platform.
If I take the example of RestApp. This all-in-one data platform connects, models, and syncs any data with business tools thanks to a Drag & Drop SQL editor (no-code SQL interface) that anyone can run.
The no-code revolution is still at its beginning. The worldwide low-code or no-code stage market income is estimated at right around $13bn in 2020 and is estimated to arrive at around $65bn in 2027.
Low-code development platform market revenue worldwide from 2018 to 2025 Graph from Statista
How Citizen Data Science can help your business?
Citizen data scientists can improve business outcomes because they understand how to apply analytics in their specific business areas.
They also know how to use advanced analytics more effectively than nontechnical users and are more capable of communicating with expert users.
Citizen data scientists often focus on smaller projects that can yield quick wins—a smart strategy when budgets are tight and all eyes are on them to produce results.
They also tend to work within the context of their own department rather than across the entire enterprise.
As a result, they can help companies rapidly get value out of their existing investments in big data platforms and other analytics technologies
As a practical matter, businesses today simply cannot afford to have powerful analytic capabilities sitting unused.
Most organizations have an enormous amount of untapped opportunity for applying advanced analytics to produce better outcomes across the organization. Citizen data science is one of the best ways for businesses to close this gap between what’s possible and what’s actually being achieved.
Citizen data scientists are challenging traditional ways of building analytical models.
Traditionally, business intelligence (BI) professionals would build models using statistical algorithms and then create reports for key decision-makers.
Now, citizen data scientists can directly access these reports and ask questions about the underlying data that would normally require advanced technical skills or a lengthy back-and-forth with BI professionals.
Moreover, data exploration and analysis can be tedious tasks for analysts. The use of tools like Hadoop and MapReduce by citizen data scientists can help them analyze large amounts of data and generate insights that positively impact the business.
Let’s take concrete examples.
With the help of advanced analytics tools, citizen data scientists can find answers to questions such as:
- What will happen if I drop my prices by 15%?
- Which marketing campaigns are most successful in acquiring new customers?
- What is the likelihood that our best customers will leave us in the next quarter?
Challenges To Citizen Data Science
The main challenge to citizen data science is organizational and cultural, not technical.
The cultural barrier includes the fear of failure and change that comes from opening up new ways of doing things.
It also includes the fear that people will be replaced by data science automation.
The organizational barrier includes lack of support for data literacy, lack of understanding of data science and analytics, and lack of time to learn new skills.
Some vendors have begun to market their products as being able to bring data science capabilities to the masses, unfortunately, this overzealous claim is not exactly true.
The reality is that even with the most advanced tools available, it will take time for people to acquire the level of expertise necessary to become citizen data scientists.
Most people who work in an organization don’t have the skills required to create a predictive model from scratch.
They need more than just tools — they also need guidance on how and when to use them.
Organizations need to realize that the future is a blend of different types of analytics practitioners with different skills.
For example, there will always be a need for professional data scientists who understand how to use advanced techniques, build complex models, and deal with big data.
However, there will also be an increasing need for citizen data scientists who can apply those techniques in their roles across all lines of business to deliver more value from their data while leveraging professional support when needed.
How to spot a Citizen Scientist?
If you’re a data scientist, data engineer, or data analyst at a company that’s serious about analytics and AI, you’ve probably seen a Citizen Data Scientist at work.
But if you’re not one of these professionals, here are the ways to spot someone with the heart of a Citizen Data Scientist.
They know more than they should. They’re often curious people who have made it their business to learn as much as they can about new tools and technology.
They know enough about analytics and data science that they can speak the language of actual data scientists.
But they don’t know everything, which is why they rely on self-service tools that guide them through the process of building models without delving too deeply into the underlying algorithms or assumptions.
They want to help everyone succeed. You’ll likely find Citizen Data Scientists in the marketing, sales, and finance departments.
That’s because many of them have a strong interest in how analytics can help make their departments run more smoothly, identify growth opportunities and improve the customer experience.
Almost all of them are motivated by improving their companies’ performance or want to help others do so.
To sum up, you should look for these traits to spot a citizen data scientist:
- A contextualized version of the organization
- An interest in using analytics to solve problems
- An understanding of basic programming concepts without knowing how to write code
- Advanced Excel skills
- Appetite for what matters relative to business priorities
Traits of a Citizen Data Scientist from Gartner
How to define the role of a Citizen Data Scientist?
These roles are often promoted as a silver bullet that can accelerate organizations into artificial intelligence (AI) and machine learning (ML) easily and cost-effectively.
However, very few organizations have managed to harness the capabilities of citizen data scientists.
“The biggest struggle organizations face is the lack of clarity of responsibilities of a citizen data scientist” claims Anirudh Ganeshan, Associate Principal Analyst at Gartner. “This vagueness creates hostilities among expert and citizen roles and impedes healthy collaboration and communication.”
For citizen data scientists to be successful, you need to build collaboration channels between citizen data scientists and expert data scientists.
Data and analytics (D&A) leaders must enable, encourage and promote the role as a legitimate approach for producing operational and activable analytics.
If you’re a Citizen Data Scientist who wants to build a project in No Code, check out the RestApp website or book a demo.
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Additional resources:
- What is Data Activation? The ultimate guide with 3 use cases per team
- What is Data Transformation? Definition, process and the new generation of tools
- [Series] How to Build Data Pipelines in No-Code? A 6-Step guide with examples
- What is Operational Analytics? (and why is it essential to your business?)
- What is Reverse ETL? (and why apply it to your business?)
without code?
