Microsoft Excel is commonly described as a spreadsheet. In practice, it is a tool used to organise, calculate, and analyse data. It is widely used by accountants, auditors, analysts, administrators, and teachers.
Lol.. just about anyone with rows of data to analyze or summarize.
At a basic level, Excel is a grid made up of rows and columns. Each cell can contain data such as numbers, dates, or text, or it can contain formulas that calculate values from other cells. Around this grid, Excel provides functions for calculations, tools for cleaning data, and features for creating charts and summaries.
It is used in finance for modelling, in human resources for payroll, in universities for grading, and in many other areas where data is involved.
I did not learn Excel in university, and I still don't think there's a Kenyan university teaching Excel as an undergraduate course. I learned it at work, when I had tasks to complete and data to handle.
*The Audit Desk: Where It Started
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In audit work, accuracy is required. Figures must match, and calculations must be correct. When I started working with client data, Excel became necessary.
At this stage, I used basic functions such as SUM and AVERAGE, along with simple arithmetic. I also learned how to link worksheets so that values in one sheet could update another automatically.
Example — Tax Computation Across Linked Sheets
Gross Revenue: 4,820,000
Allowable Deductions: 1,340,000
Taxable Income:
= Revenue – Deductions → 3,480,000
Tax (30%):
= 3,480,000 × 0.30 → 1,044,000
Net Profit:
= Revenue – Tax → 3,776,000
Linking sheets meant that when one figure changed, all related calculations updated automatically. This reduced errors and saved time.
This was my first clear lesson: Excel is not just for calculations. It shows how values are connected.
II. Payroll: Learning Lookups
Later, I worked with payroll data for over 400 employees. Each employee had records such as salary, deductions, and tax details.
Sometimes, information was missing in one sheet but available in another. Manually searching for matches would have been slow and error-prone. This is where VLOOKUP was useful.
Example — Retrieving Missing Data
=VLOOKUP(A2, HR_Master!A:E, 4, FALSE)
This formula:
Takes a staff ID
Searches for it in another sheet
Returns the matching value (e.g., KRA PIN)
This reduced manual work significantly.
More importantly, it showed that data must be structured properly. For lookups to work, there must be a common identifier, such as a staff ID. Without it, data cannot be matched.
III. Teaching: Working with Large Tables
When I moved into teaching, I used Excel to manage student results.
The challenge was not complex calculations, but handling long tables of data. With many rows and columns, it was easy to lose track of what each column represented.
The Freeze Panes feature solved this by keeping headers visible while scrolling.
The average shows overall performance. The standard deviation shows how spread out the scores are.
This helped in understanding not just the result, but how students performed as a group.
Teaching also highlighted another point: numbers need context. A value is more useful when compared to something else.
IV. Data Analytics: Cleaning Data
In data analytics training, I worked with larger and less structured datasets.
Real data often has problems:
Duplicate records
Missing values
Incorrect formats
Numbers stored as text
Before analysis, this data must be cleaned.
Example — Data Cleaning
Remove duplicates:
Use “Remove Duplicates” tool
Identify blanks:
=IF(ISBLANK(B2), "Missing", B2)
Convert text to date:
=DATEVALUE(A2)
Convert text to number:
=VALUE(C2)
These steps ensure that Excel reads the data correctly.
After cleaning, Pivot Tables can be used to summarise data quickly. They allow you to group and analyse information without writing many formulas.
A pivot table does not give answers directly. It helps organise data so that patterns can be seen.
What Excel Taught Me
Learning Excel did more than improve my technical skills. It changed how I think about data.
At first, I saw numbers as fixed facts. Over time, I learned that:
- Data depends on how it is collected
- Errors often come from poor data, not formulas
- Missing or inconsistent data affects results
- Numbers need context to be meaningful
For example:
A failed lookup may indicate missing records
A wide variation in scores may show uneven learning
Incorrect date formats can prevent proper analysis
These are not software issues. They are data issues.
Conclusion
I started using Excel without formal training. I learned it through work in audit, payroll, teaching, and data analysis.
Excel itself is just a tool. What matters is the way it encourages you to think:
- Where did the data come from?
- Is it correct?
- How is it connected?
- What does it show, and what does it not show? That is the main value I took from working with it.
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