What I like best about MS Excel is how effortlessly it turns raw, messy data into something meaningful and usable. A few things really stand out:
Power with simplicity
You can start with basic tasks like lists and totals, and gradually move into advanced analysis—formulas, PivotTables, Power Query—without changing tools. It grows with your skill level.
Speed and efficiency
Tasks that would take hours manually—calculations, sorting, filtering, comparisons—can be done in seconds. This is especially valuable in research, finance, pharmacy data, or administrative work.
Flexibility across domains
Excel works just as well for academic research data, clinical study results, inventory management, budgeting, or simple record-keeping. Few tools are that versatile.
Strong data analysis and visualization
Charts, conditional formatting, PivotTables, and dashboards make trends and patterns obvious, which helps in decision-making and presentations.
Universal acceptance
Almost everyone knows Excel at some level. Files are easy to share, review, and reuse—important for collaboration, submissions, and reports.
In short, Excel feels like a practical problem-solver: not flashy, but incredibly reliable and powerful once you know how to use it well. Review collected by and hosted on G2.com.
What I dislike about MS Excel is that, despite its power, it has some real limitations that show up once your work becomes complex or large-scale:
Error-prone formulas
A small mistake in a formula (wrong cell reference, drag error, hidden overwrite) can silently change results. In research or financial work, this can be risky if not carefully validated.
Poor handling of very large datasets
Excel slows down or crashes with large files (hundreds of thousands to millions of rows). It’s not designed to be a true database or big-data tool.
Version and compatibility issues
Files may behave differently across versions (Windows vs Mac, older vs newer Excel), especially with advanced functions, macros, or Power Query.
Limited reproducibility
Unlike programming tools (R, Python), Excel workflows are harder to document and reproduce step-by-step, which is a drawback for academic and clinical research.
Manual dependence
Many tasks still rely on manual steps (copy-paste, drag-fill). This increases the chance of human error and makes automation less robust unless VBA or Power Automate is used.
Not ideal for collaboration
While Excel Online has improved, simultaneous editing, version control, and audit trails are still weaker compared to modern collaborative data tools.
In essence, Excel is excellent for analysis and reporting—but once accuracy, scale, automation, or reproducibility become critical, its weaknesses become more noticeable. Review collected by and hosted on G2.com.
