A lot of people look at today’s AI boom and worry: “Are my Excel skills still useful? Will AI take my job and my spreadsheets?” The truth is the opposite. There’s never been a better time to lean into Excel and become what I like to call a “citizen” data analyst, data scientist, and data engineer.
With AI as the glue holding it all together, Excel becomes more than a simple spreadsheet—it’s a gateway to advanced analytics, automated workflows, and even machine learning. In this post, we’ll explore how each of these three roles can be powered by Excel, making your skillset more future-proof and exciting than ever before.
What is a “citizen” data professional?
A “citizen” data analyst, scientist, or engineer is someone who doesn’t necessarily hold a formal title or work exclusively in a data team.
Instead, they’re everyday business users, managers, operations staff, or finance specialists who build their own analytical models, automate workflows, and even work with predictive models… all from their PCs.
By employing the power of modern Excel features and integrations, these individuals avoid the long queue of IT requests and high barriers to entry that once surrounded advanced data projects. The result is a self-sufficient, highly productive professional who can respond quickly to new questions, handle complexity on the fly, and help their organization remain agile and future-ready.
Let’s walk through each of these three roles and the kind of magic that citizen data professionals spread across their organizations—with Excel as their tool of choice.
Citizen data analysts: Turning Excel into a BI powerhouse
First, let’s see how Excel empowers the citizen data analyst. With Power Query, you can connect to countless data sources and transform messy data into something usable—without repeating the same cleanup steps every time. Power Pivot helps you model data, establish relationships, and create your own measures that feed into visuals and dynamic dashboards. Add in dynamic arrays and advanced charting, and suddenly Excel becomes a compact business intelligence platform right on your desktop.
For example, let’s say each month you receive several CSV files: an orders table, a returns table, and a product table. Normally, you’d waste time opening them one by one, sorting fields, and matching product details to orders with lookup functions.
Instead, you can use Power Query to clean and transform these files automatically. Then, you can load each into the Power Pivot data model, where you define relationships across tables. With your relational model in place, you can create reusable measures and build dynamic charts and reports that refresh with a single click. No more waiting on IT or relying on expensive BI tools—decisions can now be made instantly.
For a concise introduction to the core tools enabling the citizen data analyst in Excel, take a look at my book Modern Data Analytics in Excel:
Becoming a citizen data scientist with Python and LAMBDA()
Excel has transformed into a robust platform for citizen data scientists, largely thanks to its seamless integration with Python. This powerful combination allows users to perform essential data science tasks directly within Excel, such as setting random seeds for reproducibility, working with diverse statistical distributions, and splitting datasets into training and testing sets. By embedding Python into the familiar spreadsheet environment, Excel makes these standard practices accessible without the need to switch between different tools.
The Python integration also unlocks advanced visualization capabilities that go beyond Excel’s native chart options. Users can create sophisticated plots like pairplots and jitterplots, enabling deeper statistical analysis and data exploration. These visualizations help uncover intricate patterns and insights, enhancing the overall analytical power available within Excel and making complex data analysis more intuitive and visually appealing.
Beyond Python, Excel’s LAMBDA() function empowers users to design their own complex mathematical formulas. For instance, you can create a custom weighted average function or develop intricate financial models tailored to specific requirements. By allowing the creation of reusable and advanced calculations, LAMBDA() elevates Excel to a Turing complete language, significantly expanding its computational capabilities and enabling more sophisticated data manipulations directly within spreadsheets.
To further enhance your Python skills and deepen your understanding of data science fundamentals, consider exploring resources like my book Advancing into Analytics. Although it was written before Python was integrated into Excel, the core concepts and techniques remain highly relevant and are now even more accessible within Excel’s enhanced ecosystem:
Excel’s rapid evolution, highlighted by Python integration and LAMBDA(), underscores its commitment to empowering users as citizen data scientists, providing a versatile and flexible platform for advanced analytics right at your fingertips.
Automating Excel-based flows and pipelines as a citizen data engineer
The final frontier of “citizen” data roles lies in data engineering: automating data pipelines, connecting multiple systems, and ensuring data reaches the right place at the right time. Excel empowers citizen data engineers primarily through Power Automate, which allows users to create workflows triggered by time schedules, data updates, or external events. By integrating Power Automate with Excel, users can synchronize spreadsheets with various workflows and tasks, such as importing data from different sources, updating records, and orchestrating complex processes without writing extensive code.
Complementing Power Automate, Office Scripts in Excel serves as a modern, cloud-first scripting and automation tool, offering a robust alternative to traditional VBA macros. Office Scripts, built on TypeScript, enable users to automate repetitive tasks like formatting incoming data, refreshing calculations, and generating reports.
A practical example of a citizen data engineer in action might involve setting up a Power Automate flow to monitor a SharePoint folder for new CSV reports. When a new file is detected, the flow can automatically append its contents to a master Excel workbook. Subsequently, an Office Script can standardize the data formats, refresh relevant calculations, and update summary dashboards. Finally, the workflow can send notifications via Microsoft Teams with the latest metrics, ensuring that stakeholders receive timely updates without manual intervention.
Beyond simple automation, Excel’s integration with Power Automate and Office Scripts enables seamless connectivity with a wide array of services and platforms. Power Automate’s extensive library of connectors allows Excel to interact with databases, cloud services, social media platforms, and more.
To further develop your skills as a citizen data engineer using Excel, explore resources such as Microsoft’s Power Automate documentation and Office Scripts tutorials. Engaging with community forums and participating in hands-on projects can also accelerate your proficiency, allowing you to harness the full potential of Excel as a powerful data engineering tool.
AI: The bridge empowering citizen data professionals
As Excel continues to evolve into a comprehensive platform for citizen data scientists, analysts, and engineers, AI emerges as the crucial bridge that unifies and enhances these roles. Microsoft’s Copilot suite of tools seamlessly integrates AI into Excel, transforming it from a powerful spreadsheet application into an intelligent assistant that significantly lowers the barriers to advanced data tasks. This AI-driven enhancement ensures that users can leverage sophisticated functionalities without requiring deep technical expertise, making advanced data work more accessible and efficient than ever before.
For citizen data analysts, Copilot in Excel revolutionizes the way they handle and interpret data. Imagine needing to create a complex PivotTable or generate a dynamic dashboard from multiple data sources. With Copilot, you can simply describe what you want in natural language, and it will construct the necessary formulas and visualizations for you.
For example, a marketing analyst can ask Copilot to identify the top-performing campaigns over the last quarter, and it will automatically aggregate the relevant data, apply the appropriate filters, and present the findings in an easily digestible chart. This capability not only accelerates the analysis process but also ensures accuracy and consistency in reporting, allowing analysts to focus on deriving insights rather than getting bogged down by technical details.
Citizen data scientists benefit immensely from the integration of AI within Excel, particularly through Advanced Analysis with Python in Copilot for Excel. This feature provides real-time code writing, debugging, and assistance for Python directly within the familiar spreadsheet environment.
Suppose a data scientist wants to build a regression model to predict sales based on various factors. Copilot can guide them through the process by generating the necessary Python code to split the dataset, train the model, and evaluate its performance, all within Excel. If issues arise, Copilot offers debugging support, suggesting corrections and optimizations on the fly. This seamless integration of Python into Excel not only streamlines the workflow but also empowers data scientists to perform sophisticated analyses without switching between different tools or environments.
For citizen data engineers, AI tools like Copilot on Power Automate streamline the creation and management of data pipelines and workflows. Setting up automated processes that synchronize data across different platforms can be complex and time-consuming. However, with Copilot, engineers can describe their workflow requirements in plain language, and the AI will generate the appropriate Power Automate flows.
For instance, an operations specialist might need to automate the process of importing daily sales data from an external API into Excel, transform it using Office Scripts, and then update a Power BI dashboard. Copilot can handle the entire setup, ensuring that data flows smoothly and reliably without the need for extensive coding knowledge. This not only saves time but also enhances the reliability and scalability of data engineering tasks.
In summary, AI, through Microsoft’s Copilot suite of tools, serves as the essential bridge that unifies and amplifies the roles of citizen data analysts, scientists, and engineers within Excel. By assisting with formula construction, visualization selection, workflow automation, advanced Python-based analysis, and so much more, Copilot lowers the barriers to entry for advanced data tasks, democratizing access to powerful analytical capabilities. This integration not only enhances productivity and accuracy but also fosters a more innovative and data-driven culture within organizations.
Conclusion
As we’ve explored, Excel is more than just a spreadsheet. It’s a dynamic platform empowering citizen data analysts, scientists, and engineers to drive meaningful insights and automate complex workflows. With the integration of AI through Microsoft’s Copilot suite, the barriers to advanced data tasks are significantly lowered, making sophisticated analytics, machine learning, and data engineering accessible to everyone, regardless of their technical background.
To close, I’d love to hear from you. Which of these roles—data analyst, data scientist, or data engineer—are you most excited about pursuing with Excel? Which aspect of AI integration intrigues you the most, and how do you envision it enhancing your current workflow? What new skills are you eager to develop, and how can AI help you achieve your data goals more efficiently? Please share your questions and thoughts below.
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