George Mount, an Excel MVP and the founder of Stringfest Analytics, shares insights on leveling up from Excel to advanced analytics. He discusses the career benefits of his books on data analytics and how Excel is foundational for finance professionals. The conversation touches on using Python to enhance data science skills, the rise of citizen data scientists, and the synergy between Excel, AI, and low code tools. George also highlights the importance of data organization before diving into AI, making this a must-listen for anyone in finance.
Finance professionals can leverage their Excel expertise as a foundation to transition smoothly into advanced analytics tools like Python and R.
Understanding the interplay between AI and data quality is crucial for finance professionals to effectively harness AI's capabilities while ensuring data integrity.
Deep dives
Bridging the Gap Between Excel and Data Science
The transition from Excel to advanced analytics tools like Python and R is essential for finance professionals aiming to enhance their data skills. George Mount emphasizes the importance of using existing Excel knowledge as a foundation to quickly learn new techniques and methods in data science. His book, 'Advancing into Analytics,' was motivated by his own experiences in navigating the complexities of these advanced tools, which often lacked comprehensive learning resources. As users become more comfortable with these new tools, they will be able to replicate efficient processes that were previously challenging in Excel.
The Evolving Role of Excel in Data Analytics
Despite the rise of more advanced tools, Excel remains a critical platform for data analysis, particularly with the introduction of features like Power Query and Power Pivot. George highlights that the familiarity and user-friendly nature of Excel still make it a go-to resource for many professionals. His latest book, 'Modern Data Analytics in Excel,' focuses on empowering users with the latest features to unlock the full potential of data analytics within Excel. By reinforcing these skills, finance professionals can navigate their transition into data analytics more effectively.
Learning Pathways for Aspiring Data Analysts
For finance professionals looking to advance their careers, George suggests starting with a deep understanding of Excel and gradually moving into tools like Power BI and Python. He proposes focusing on core data analytical capabilities in Excel before diving into programming aspects, which can be intimidating. By identifying their desired pathway—whether it's business intelligence, data science, or data engineering—individuals can scaffold their learning to align with their professional goals. This strategic approach not only builds confidence but also enhances their value in an increasingly data-driven workplace.
Integrating Generative AI in Data Analysis
The advent of generative AI presents both challenges and opportunities for finance professionals, particularly in the context of data quality and analysis. George stresses the necessity of understanding how AI works with data to effectively leverage its capabilities while also maintaining a focus on quality over quantity. As AI tools become more integrated into workflows, ensuring the accuracy and integrity of data is crucial to avoid the pitfalls of automation. By mastering both traditional analytics skills and modern technologies, finance professionals can position themselves as vital contributors to their organizations, navigating the competitive landscape of data analysis.
George Mount is an Excel MVP and author of Modern Data Analytics in Excel (he describes it as a guide to becoming a data analyst in Excel). His latest book, Advancing into Analytics: From Excel to Python and R could be subtitled “becoming a data scientist in Excel. As founder and CEO of Stringfest Analytics, he provides analytics education and upskilling including works with finance departments at the top ten banks. In this episode he answers how someone in FP&A – killing it in Excel – can go further in their career while using Excel as home base.
In this episode:
Two books and the ROI you get as a finance professional from reading it
Using Excel as your home base for FP&A
When to use Excel vs Python
citizen data scientists (or citizen data analysts) in Python
Using low code/no code tools
Excel, copilot and Python as a new “trinity” for FP&A
Getting your data house in order before getting to AI
A surprising Excel favorite befitting an MVP
Connect with George Mount on LinkedIn: https://www.linkedin.com/in/gjmount/
Further Reading