For decades, Excel has been synonymous with data analysis. For many organizations, especially small and medium-sized businesses, it represented the first real step toward data-driven decision-making. Spreadsheets brought autonomy, speed, and a new analytical mindset to business teams. Data was no longer confined to technical departments; it became accessible to managers, financial analysts, sales teams, and operations.
Over time, however, the same tool that empowered analytical thinking began to reveal its limits. Data volumes increased, sources multiplied, and business questions grew more complex. What once fit comfortably inside a spreadsheet began to demand infrastructure, governance, and distributed processing. This created a common dilemma: how can organizations evolve from spreadsheet-based analysis to a truly data-centric culture without losing agility or business understanding? This is where Big Data platforms such as BigQuery emerge—not as a replacement for Excel’s analytical logic, but as its natural evolution.
How people tend to solve it
When data complexity grows, most organizations respond by clinging to familiar tools. Excel continues to be used even as datasets reach millions of rows, file versions proliferate, and analyses rely on local copies, fragile macros, and manual workflows. The spreadsheet slowly becomes a database, an ETL tool, a version-control system, and a dashboard all at once.
This approach works only within the realm of Small Data—small, static, structured datasets with low update frequency. Problems arise when businesses begin to generate real-time data, integrate multiple sources, and analyze long historical records. In these scenarios, Excel stops being an enabler and becomes a liability. Silent errors, lack of traceability, limited collaboration, and performance bottlenecks become routine. Many organizations try to solve this by increasing spreadsheet complexity or relying on a few Excel experts, which centralizes knowledge and weakens decision-making. The illusion of control persists, while the underlying data reality grows increasingly fragile.
How it should be solved
To understand the shift from Excel to BigQuery, it is important to acknowledge Excel’s historical role. Excel democratized analysis by allowing users to test hypotheses and explore data without complex systems. That mindset remains essential; what changes is the scale.
BigQuery represents a turning point because it preserves the analytical paradigm—SQL, aggregations, filters, joins—while moving it into a native Big Data environment. While Excel operates in the world of Little Data, constrained by local memory and manual interaction, BigQuery is designed for massive volumes, distributed storage, and parallel processing.
The difference is not only about size, but about architecture. In Excel, data is copied to the analyst. In BigQuery, the analyst queries the data where it lives. This eliminates duplication, reduces inconsistencies, and establishes a single source of truth. Queries that would be slow or impossible in spreadsheets run in seconds over billions of records.
Beyond performance, BigQuery introduces collaboration, governance, and reliability. Queries are reproducible, access can be controlled through policies, and integration with BI tools, machine learning, and automation becomes seamless. Analysts spend less time cleaning and moving data and more time asking better questions and generating insights with real business impact. Excel is not eliminated—it is repositioned as a tool for exploration, modeling, and communication, rather than the core of the data architecture.
Conclusion
The evolution of the modern data analyst is not a rejection of the past, but a continuation of it. Excel taught generations of professionals how to think analytically, challenge assumptions, and turn numbers into decisions. BigQuery extends this legacy by enabling the same reasoning at scale, with speed and reliability aligned to today’s data complexity.
Being data-centric no longer means abandoning spreadsheets, but understanding their limits and integrating them into a broader ecosystem. Organizations that make this transition gain clarity, consistency, and the ability to anticipate trends. Analysts who master this evolution move beyond file management and become strategic professionals capable of navigating both detail and scale. From Excel to BigQuery, what truly evolves is not the toolset, but the maturity with which data is used to drive decisions.
References
Davenport, T. H.; Harris, J. G. Competing on Analytics: The New Science of Winning. Harvard Business School Press, 2007.
Mayer-Schönberger, V.; Cukier, K. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt, 2013.
Few, S. Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press, 2009.
Google Cloud. BigQuery Documentation and Architecture Overview., 2023.
Kimball, R.; Ross, M. The Data Warehouse Toolkit. Wiley, 2013.