The More Things Change, The More They Stay the Same
As 2025 approaches, reflecting on nearly 35 years in the workforce reveals
remarkable technological advancements. In 1990, workplaces transitioned from a
handful of desktop computers to equipping every employee with laptops. Wired
connections gave way to high-speed wireless internet, and flip phones evolved into
powerful touchscreen devices.
Amid this rapid innovation, one software tool has remained a constant: Microsoft
Excel. Introduced in 1985, Excel revolutionized how businesses managed data,
performed calculations, and created reports. It became a universal solution,
cementing its place as an indispensable tool across industries. For many, “throw it
in a matrix and create a pivot table” became a standard approach to problem-
solving.
Yet, as businesses face growing data complexities and the rise of artificial
intelligence and machine learning, the limitations of Excel are becoming apparent.
The question arises: is it time to rethink Excel’s role in modern, data-driven
organizations?
Excel’s Legacy and Its Double-Edged Sword
Excel’s accessibility and flexibility have been its greatest strengths, enabling non-
technical users to manipulate and analyze data effortlessly. However, these very
attributes can lead to business silos and fragmented data processes. For instance,
manual workflows dependent on Excel often result in multiple, inconsistent
spreadsheets circulating within an organization. Executives might wait days—or
even weeks—for data crucial to decision-making. This fragmented approach not
only hampers efficiency but also raises concerns about data quality and
trustworthiness, challenges that modern businesses cannot afford to ignore.
Excel’s Limitations in the Age of AI and ML
As artificial intelligence (AI) and machine learning (ML) reshape the business landscape, Excel
struggles to keep pace. Here’s why:
- Data Volume Constraints
Excel falters with large datasets, making it unsuitable for data-intensive
applications that require millions of rows for accurate modeling. - Lack of Automation
AI and ML thrive on automated processes, whereas Excel’s manual nature
introduces inefficiencies and complexities. - Limited ML Capabilities
Unlike programming languages like Python or R, Excel lacks robust machine learning
libraries, necessitating data migration for advanced analysis. - Security and Governance Risks
Shared Excel files often lack the robust security needed to comply with modern data
protection regulations, exposing organizations to potential breaches. In addition, there
could easily be multiple versions of the same workbook floating around offering little to no
version control.
Navigating Change: Beyond Excel
“Change happens when the pain of staying the same is greater than the pain of
change.” – Tony Robbins
I use the above quote often in my business and personal life. It’s a powerful
statement, and so true. Any change will likely introduce some level of pain. But not
changing course can inflict even more. Transitioning away from Excel isn’t just a
technical challenge—it’s a cultural shift. Employees accustomed to Excel’s
simplicity may resist learning new tools. There can easily be a mix of employee
emotions including anxiety and uncertainty. For organizations to succeed, they
must prioritize adoption and empowerment alongside technical transformation.
Start with a limited proof of concept addressing just a handful of business use
cases to gain insight into the potential opportunity, while allowing employees to
learn new technologies.
Some Practical Steps to Move Beyond Excel
- Set the Vision
Executive leadership must champion the shift toward a data-driven culture
across the enterprise. This type of transformation is companywide. Start
small with a proof of concept (POC) that demonstrates the value of modern
tools on a limited dataset. - Modernize Your Enterprise Data
• Centralize Data: Implement a centralized data warehouse to ensure
consistency and accessibility across departments.
• Adopt Specialized Platforms: Invest in tools like SQL databases,
Snowflake, Databricks, and data visualization platforms that surpass
Excel’s capabilities. - Democratize the Data
• Agile Deployment: Use agile strategies to align changes with business
priorities, fostering collaboration and transparency.
• Encourage Data Sharing: Promote cross-department collaboration and
provide pair programming to prevent data silos. - Embrace Transformation
• Hands-On Training: Showcase client-specific use cases to demonstrate
how new solutions address and solve business challenges.
• Promote Adoption: Empower employees by building their confidence in
modern tools through ongoing support and training. It’s a marathon, not a
sprint.
Conclusion: To Excel or Not to Excel?
Excel remains a valuable tool for quick calculations and ad hoc analysis. However,
its limitations make it unsuitable for organizations seeking to harness the full
potential of AI and ML.
The journey beyond Excel begins with small steps—proof of concepts, centralized
data platforms, and cross-department collaboration. While change is challenging,
it’s necessary for businesses to evolve and thrive in a data-driven future.
About the Author
Brian Silvestri is the Chief Growth and Strategy Officer at Data Surge LLC. With
over 30 years of experience, Brian is a passional leader and entrepreneur
promoting team collaboration to promote initiatives and solve complex business
challenges. His experience includes executive, senior management, and senior
technical expertise.
About Data Surge
At Data Surge LLC, we specialize in helping businesses modernize, democratize,
and transform their data capabilities. Our team of data scientists and engineers
ensures a seamless transition to modern solutions. Visit www.datasurge.com for
more information.
