Citizen Data Scientists: 4 Ways to Democratize Data Science in 2024

Hi there! With data playing an increasingly vital role across every industry, companies are needing more people who can work with data to drive decisions. However, there just aren‘t enough trained data scientists to meet the growing demand.

This is where "citizen data scientists" come in – and they are the key to filling the data skills gap in 2024 and beyond.

In this article, I‘ll explain what citizen data science is, why it‘s so important right now, tools to empower citizen data scientists, and best practices to maximize value. My goal is to provide you with a comprehensive guide to successfully leveraging citizen data science in your organization this year.

Let‘s get started!

What is Citizen Data Science and Why Does it Matter?

Back in 2018, research firm Gartner first defined citizen data science as:

"A person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics."

In simple terms, citizen data scientists are business users who can utilize data and analytics to solve problems and extract insights, despite having little formal data science training.

Citizen data scientists combine their deep understanding of the business with increasingly accessible analytics tools to drive data-driven decisions. Even though they aren‘t formally trained as data professionals, citizen data scientists are able to add tremendous value through their domain expertise.

Now, interest in citizen data science solutions has absolutely exploded in recent years. According to Google Trends, global searches for the term "citizen data science" have nearly tripled between 2012 and 2022:

There are a few key factors fueling this rapid growth:

• The data science talent shortage is more pressing than ever. Demand for data professionals continues to vastly exceed supply. As of 2020, there were over 3 times as many data science job postings as job searches according to QuantHub.
• Data scientists command very high salaries. With demand far surpassing supply for data science skills, organizations pay a premium for talent. According to the U.S. Bureau of Labor Statistics, the average salary for data scientists is now over $100k.
• Analytics tools are becoming more intuitive and user-friendly. Solutions like business intelligence platforms and AutoML are becoming easier for non-technical users to leverage, minimizing the need for coding expertise.

Leading industry analysts strongly endorse the citizen data scientist approach for democratizing analytics:

• Gartner heavily promotes citizen data science in research reports and conferences. They clearly recognize this trend‘s massive potential.
• IDC research director Chwee Kan Chua highlights the value of "allowing even non-technical business users to be ‘data scientists’" in a recent interview.

It‘s clear that citizen data science will be crucial for creating the analytics capabilities and culture organizations need. Next, let‘s explore the technologies empowering citizen data scientists.

4 Technologies That Empower Citizen Data Scientists

A variety of cutting-edge platforms are emerging to democratize data science and empower citizen data scientists within organizations, including:

1. Metadata Management Solutions

First and foremost, citizen data scientists need the ability to easily find, access, understand, and enrich the data required for analysis.

Metadata management solutions like data catalogs (e.g. Alation, Collibra) and self-service reporting tools (e.g. DashCadr, Sisu) are key for discovering and leveraging data.

According to 2022 Gartner surveys, using a data catalog is the #1 driver of analytical culture across organizations. Providing a "single source of truth" accelerates insights.

2. Automated Machine Learning (AutoML)

AutoML platforms help automate repetitive, manual tasks involved in machine learning model building like data preprocessing, feature engineering, model selection, hyperparameter tuning, and more.

Leading options like DataRobot, H2O.ai, and Google Cloud AutoML simplify the process so citizen data scientists can develop and deploy ML models faster.

According to Gartner, AutoML adoption grew over 65% in 2022, significantly expanding access to machine learning.

3. Augmented Analytics

Augmented analytics solutions utilize AI techniques like natural language processing and machine learning to automate data insight generation.

With solutions like Qlik, Microsoft Power BI, and Tableau, users can get answers to natural language questions and receive automated analysis without coding.

By 2025, Gartner forecasts that over 50% of analytics queries will be generated using augmented capabilities rather than manual processes.

4. Low-Code Platforms

Low-code development platforms enable citizen developers to rapidly build, test, and deploy data solutions through visual, "drag and drop" interfaces instead of traditional programming.

These platforms drastically reduce the need for extensive coding expertise. Leading options include Appian, Mendix, and OutSystems.

According to recent IDC forecasts, low-code adoption will grow over 25% annually over the next 5 years as companies aim to expand development capacity.

The bottom line is that these technologies are making data science and analytics more accessible and intuitive for non-technical users – the citizen data scientists. Next we‘ll explore how to implement citizen data science successfully.

4 Best Practices for Maximizing Citizen Data Science Value

Based on the experiences of leading companies, here are the top 4 best practices I recommend for maximizing the value of citizen data science programs:

1. Facilitate Collaboration Between Roles

The most successful strategy is to provide spaces for citizen data scientists to collaborate closely with expert data professionals on initiatives.

Combining domain expertise from the business side with technical foundations from the data side will drive the deepest insights and value.

Fostering connections between teams through knowledge sharing programs helps build trust and alignment. Don‘t silo – facilitate teamwork.

2. Invest in Relevant Training

Provide citizen data scientists with training on potential pitfalls like biased models as well as education on effective usage of self-service tools.

Equipping citizen data scientists with knowledge minimizes errors, builds skills, and enables them to work independently. Training pays dividends.

Prioritize training on data literacy, analytical thinking, tool usage, and data ethics based on assessments of team needs and strengths.

3. Classify Data Sets by Accessibility

Catalog data sets into categories based on how widely accessible they can be, considering factors like sensitive data, regulations, and security.

Not all data can or should be made available to all employees. Smart data governance ensures citizen data scientists have access to what they need while protecting the rest.

4. Build Effective Data Sandboxes

Create sandbox environments with synthetic data where citizen data scientists can safely build skills and rapidly test models or analysis before deploying to production systems.

Sandboxes enable a "safe space" to learn and accelerate experimentation. They are invaluable for maximizing team productivity and minimizing risk.

The key to success is empowering citizen data scientists with the tools, knowledge, and support needed to drive impact. By investing in people and technologies, organizations can reap massive value from the democratization of data – while smartly mitigating potential downsides.

The future of business is data-driven, and citizen data scientists are key to developing the capabilities required. I hope this guide provided you with a comprehensive overview of citizen data science and how to implement it successfully. Let me know if you have any other questions!