The Rise of the Citizen Data Scientist: Empowering Business Users with Analytics

In today‘s data-driven business landscape, organizations increasingly rely on insights gleaned from analyzing vast amounts of information to make better decisions. However, a shortage of expert data scientists has made it difficult for many companies to fully leverage their data. This is where citizen data scientists come in – business professionals who can perform analytics and generate valuable insights without needing formal data science training.

Citizen data scientists are one of the hottest trends in analytics today. Gartner predicts that by 2025, the number of citizen data scientists will grow five times faster than the number of expert data scientists. This rapid growth is fueled by the democratization of data science through user-friendly analytics tools and the pressing need for businesses to become more data-driven to remain competitive.

So what exactly is a citizen data scientist? They are power users embedded in business domains who can perform analytical tasks traditionally done by expert data scientists, such as data preparation, analysis and visualization. The key difference is that while expert data scientists have specialized skills in statistics, math and coding, citizen data scientists leverage self-service analytics tools to guide them through the process without needing to code.

Citizen data scientists typically have deep domain expertise in areas like marketing, finance or operations. This business context is crucial for identifying the right problems to solve with analytics and interpreting results accurately. They don‘t replace expert data scientists but rather complement them, often serving as a liaison between IT/data science teams and business stakeholders.

The Citizen Data Scientist Opportunity by the Numbers

Just how big is the citizen data scientist opportunity? Several statistics point to the growing prevalence and importance of this role:

• 1.5 million citizen data scientists by 2025: Gartner predicts that 50% of organizations will have systems that include both expert and citizen data scientists working collaboratively by 2025, with the number of citizen data scientists growing five times faster than experts.

• 60% of data science tasks performed by citizen data scientists: Gartner also estimates that by 2025, citizen data scientists will surpass data scientists in the amount of advanced analysis produced – accounting for 60% of all data science tasks.

• 54% have or plan to implement citizen data science: An IDC survey found that 36% of organizations already leverage citizen data scientists, with another 18% planning to do so within 12 months. This highlights the rapid adoption of the role.

• Most common titles are business analyst and data analyst: A KDnuggets poll of citizen data scientist job titles found the most common were business analyst (22%), data analyst (18%), business intelligence analyst (13%) and data engineer (10%).

This data underscores that citizen data scientists are not just a fleeting fad but a key component of a modern, data-driven enterprise. With the right tools, training and guardrails, they have the potential to dramatically scale an organization‘s analytics capabilities.

The Benefits of Leveraging Citizen Data Scientists

Empowering citizen data scientists yields several important benefits for organizations:

Scaling data science efforts: With expert data scientists in short supply and high demand, many analytics initiatives get backlogged. Citizen data scientists can help pick up some of the slack, tackling lower complexity problems so that expert data scientists can focus on more advanced, specialized challenges. This enables organizations to get more data science projects done without needing to hire armies of experts.

Bringing domain knowledge: While expert data scientists bring technical chops, they may lack a deep understanding of the business. In contrast, citizen data scientists can frame business problems and opportunities through the lens of analytics more effectively. Their domain expertise helps ensure that the right data is used and results are interpreted in proper business context.

Generating insights faster: Being embedded in business units, citizen data scientists can often spot opportunities and deliver insights much faster than a centralized data science team. The ability to quickly go from business problem to analytical solution accelerates data-driven decision making. Gartner found that citizen data scientists improve the agility of analytics initiatives.

Freeing up expert resources: By handling a portion of routine analytical tasks, citizen data scientists enable expert data scientists to tackle more complex, high-value initiatives. Experts can focus on cutting-edge applications like deep learning while still providing guidance to their citizen counterparts. This symbiotic relationship maximizes the efficiency and effectiveness of enterprise data science efforts.

Fostering a data-driven culture: Citizen data scientists serve as analytics evangelists within their business domains. As they demonstrate the value of data-driven insights and decision making to their peers, it sparks greater interest and adoption of analytics across the organization. This grassroots approach, coupled with leadership support and data literacy programs, drives the cultural shift needed to become a data-driven enterprise.

Enabling Citizen Data Scientists with Self-Service Analytics

One of the key drivers behind the rise of citizen data scientists is the proliferation of user-friendly, self-service analytics tools. These technologies enable users to perform data science tasks without needing to write code. Here are some of the key capabilities to look for in self-service analytics platforms:

• Automated data preparation: The most time-consuming part of analytics is typically preparing data for analysis. Look for tools that can automate data profiling, cleansing, joining, enrichment and transformation. Aim to minimize the need for manual data wrangling.

• Drag-and-drop visual interfaces: Rather than writing SQL queries or Python scripts, citizen data scientists should be able to explore and analyze data using intuitive visual interfaces. Drag-and-drop tools for building reports, dashboards and analytical models are a must.

• Natural language querying: Some emerging technologies enable citizen data scientists to ask questions and interact with data using natural language. Simply type a query like "show me sales by product and region" and the tool will generate the appropriate visualization. This makes analytics even more accessible.

• Pre-built analytical templates: Citizen data scientists can benefit from pre-built templates for common analytical tasks and machine learning models. These accelerators help them get started quickly without needing to build everything from scratch. Templates should be customizable to specific business needs.

• Collaboration and sharing: Look for analytics workspaces that enable citizen data scientists to easily collaborate with colleagues and share their work. The ability to annotate and comment on assets, co-author projects, and publish insights to stakeholders is important. Ensure there are robust access controls and security measures in place.

• Automation and augmented analytics: More advanced platforms are starting to leverage artificial intelligence (AI) and machine learning to automate insights discovery and guide citizen data scientists. This includes functionality like automated data visualization, anomaly detection, forecasting and what-if scenario analysis.

Some of the popular commercial self-service analytics platforms well-suited for citizen data scientists include Microsoft Power BI, Tableau, Qlik, Alteryx and KNIME. There are also emerging open source options like H2O Driverless AI, DataRobot and RapidMiner that bring automation and augmented analytics capabilities.

The key is to select tools that provide the right balance of ease-of-use and analytical sophistication for your citizen data scientists. Ensure the tools can access all relevant enterprise data sources and meet security and governance requirements.

AI Techniques that Empower Citizen Data Scientists

In addition to self-service analytics platforms, several artificial intelligence techniques are helping to further empower citizen data scientists. AI can automate and simplify complex data science tasks, enabling users to generate insights faster and more accurately than ever before. Here are some of the key AI capabilities to be aware of:

Automated machine learning (AutoML): AutoML technologies automate the process of building machine learning models, from data preparation to model selection and hyperparameter tuning. Citizen data scientists can simply specify their target variable and AutoML will generate an optimized model pipeline without requiring any coding. Some AutoML tools even explain model results in plain language.

Natural language processing (NLP): NLP techniques enable citizen data scientists to interact with data using natural language queries and commands. For example, a user could ask "what were total sales in the Northeast region last quarter" and the NLP engine would generate the appropriate SQL query or visualization. Some tools also leverage NLP to automatically summarize key insights in narratives.

Knowledge graphs: Knowledge graphs map the relationships between entities in data, revealing complex patterns and enabling more sophisticated question answering. Tools that leverage knowledge graphs can suggest relevant insights to citizen data scientists based on the data they are exploring. This helps uncover hidden connections and accelerates time to insight.

Augmented analytics: Augmented analytics uses machine learning to automate many of the manual tasks in the data science workflow. This includes profiling and validating data quality, recommending the best visualizations, identifying statistically significant correlations, and detecting anomalies and outliers. By intelligently guiding citizen data scientists, augmented analytics helps them make smarter, faster decisions.

The exciting thing about AI is that it will continue to evolve and become more accessible to non-expert users over time. As Natural Language Processing, computer vision, AutoML and other techniques mature, they will further lower the barriers to entry and empower citizen data scientists to solve even more complex problems independently.

The Future of Citizen Data Scientists

As the business world becomes increasingly data-driven, citizen data scientists will play a crucial role in scaling analytics efforts and driving better decision making. Gartner predicts that by 2025, 50% of organizations will have systems that include both expert and citizen data scientists working collaboratively.

In the near future, the analytics tools used by citizen data scientists will become even more user-friendly and automated. Vendors are heavily investing in AI capabilities to guide users through analytical workflows and optimize models. Soon, citizen data scientists will be able to easily train machine learning models without writing a line of code.

As citizen data scientists grow in sophistication, expert data scientists will shift to focusing more on specialized, complex challenges while also serving as mentors and advisors. This symbiotic partnership will become increasingly important to deriving the most value from enterprise data assets.

However, succeeding with citizen data scientists long-term will require more than just tools and training. Organizations must actively foster a culture of data literacy, governance and collaboration in analytics. Citizen data scientists will need ongoing support from executive sponsors, expert partners and their peers to drive true business impact.

Some key focus areas for empowering citizen data scientists going forward include:

• Cultivating data literacy: Formal data literacy programs, ideally personalized by role, will be crucial to enabling citizen data scientists. McKinsey found that organizations with the highest data literacy scores see a 3-5% boost in enterprise value. Micro-learning content, use case-based learning and competitions like analytics hackathons can all help gamify data literacy.

• Building a community of practice: Establishing forums for citizen data scientists to connect, share best practices and learn from each other drives engagement and skills development. Look to identify citizen data science champions who can evangelize the discipline and mentor others. Celebrate successes and recognize top performers to inspire further adoption.

• Formalizing analytics governance: Balancing the agility and autonomy of citizen data scientists with appropriate governance is an ongoing challenge. Clearly defining roles, responsibilities and processes for the various personas involved in the analytics lifecycle is critical. Implement checks and balances to ensure proper oversight and quality control of citizen-generated analytics.

• Managing the analytics portfolio: Aligning citizen data science initiatives with strategic business priorities is key to maximizing impact. Implement processes to ideate, prioritize and track analytics use cases based on value and feasibility. Measure and monitor the ROI of citizen data science efforts to demonstrate their bottom-line impact and justify further investment.

• Ensuring responsible and ethical AI: As citizen data scientists increasingly leverage AI and machine learning capabilities, organizations must ensure responsible development and deployment practices. Educate citizen data scientists on how to detect algorithmic bias, ensure data privacy, and use AI transparently and ethically. Implement model validation and monitoring processes to mitigate unintended consequences.

Ultimately, the organizations that can successfully marry the domain expertise of citizen data scientists with the technical acumen of analytics experts will be primed to win in the age of AI. Citizen data scientists represent an untapped opportunity to transform every business person into an analytics change agent driving smarter decisions.

Empowering Citizen Data Scientists for Impact

The era of citizen data scientists is here, and it holds great promise for making data science more accessible and impactful than ever before. By empowering business experts to lead analytical initiatives aligned to key objectives, organizations can accelerate data-driven decision making while liberating expert data scientists to innovate.

However, organizations must strike the right balance between agility and governance to reap the full benefits of citizen data scientists. Proper training, tools, oversight and collaboration are all essential to ensure citizen data scientists apply their domain expertise correctly and impactfully with analytics.

The most successful organizations will be those that embrace citizen data scientists as partners to expert teams, not adversaries. They will invest in data literacy and community building to foster a culture of analytical excellence. And they will implement processes and platforms to enable this new breed of business professionals to unlock the full potential of data.

Is your organization ready for the citizen data scientist revolution? The future of analytics is now – and it belongs to those who empower business experts to lead the way with data-driven insights. By harnessing the power of AI and self-service tools, citizen data scientists will be crucial to creating a truly intelligent enterprise.