Forem: Mercy-Shalom Adedayo

8 Excel Functions Every Professional Should Know (With Real Use Cases)

Mercy-Shalom Adedayo — Wed, 15 Apr 2026 13:45:30 +0000

Spreadsheets are everywhere; finance teams, HR departments, product managers, marketers, and solo freelancers all rely on them. But most professionals use Excel at only a fraction of its potential, manually repeating work that a single formula could handle in seconds.
This article isn't about becoming an Excel expert. It's about knowing the eight functions that cover roughly 80% of everyday professional spreadsheet tasks, the ones that reduce errors, speed up reporting, and free your brain for actual decision-making.
Each function below includes a plain-language explanation, a working formula example, and a grounded, real-world use case. Whether you work in sales, operations, HR, or anywhere in between, at least a few of these will change how you work.

1. SUM()

Quickly calculate totals. Add any range of numbers instantly.

What it does
Adds all numeric values within a specified cell range. One of Excel's most fundamental functions, and the most used.
Syntax

=SUM(B2:B31)

Real-world use case
You manage a retail store and track daily sales in column B. Rather than manually summing 30+ rows on a calculator, =SUM() gives you total monthly revenue instantly. Add a new row for today's sales? The total updates on its own; no recalculation is needed.

Pro tip
You can also use =SUM(B2:B31, D2:D31) to add across multiple non-contiguous ranges in a single formula.

2. AVERAGE()

Measure typical performance. It finds the mean of any numeric range.

What it does
Returns the arithmetic mean of a range: the sum divided by the count of values. Essential for benchmarking and trend awareness.
Syntax

=AVERAGE(B2:B31)

Real-world use case
As a team lead reviewing monthly performance, you want a baseline, not just totals. =AVERAGE() reveals the typical daily output, helping you distinguish a strong week from an outlier day. It's the foundation of setting realistic performance targets.

3. IF()

Automate decisions. It applies logic without manual review.

What it does
Tests a condition and returns one result if true, another if false.The backbone of conditional logic in any spreadsheet.
Syntax

=IF(C2>=100000, "Target Met", "Below Target")

Real-world use case
You have a sales report with 50 reps. Rather than manually reading each row to flag who hit their quota, =IF() automatically labels every entry as "Target Met" or "Below Target." This feeds directly into dashboards and management summaries without any manual input.
Pro Tip:
Nest multiple IF statements, or use =IFS() to handle more than two outcomes, e.g., "Exceeded", "Met", "Below", "Critical".

4. VLOOKUP()

Retrieve data from tables. It searches and pulls matching records.

What it does
Searches for a value in the first column of a range and returns a value in the same row from a specified column. Replaces manual lookups entirely.
Syntax

=VLOOKUP(A2, ProductTable, 3, FALSE)

Real-world use case
You have an order sheet with product IDs and a separate pricing table. Instead of manually cross-referencing hundreds of rows, =VLOOKUP fetches the correct price for each ID automatically, cutting hours of work down to seconds and eliminating copy-paste errors.
Modern alternative
If you're on Excel 365 or 2019+, consider =XLOOKUP(); it's more flexible, doesn't require the lookup value to be in the first column, and handles errors more gracefully.

5. CONCAT()

Combine text fields. Merge multiple cells into one clean string.

What it does
Joins text from multiple cells into a single string. Great for combining names, IDs, addresses, emails, or any label-based data that comes split across columns.
Syntax

=CONCAT(A2, " ", B2)

Real-world use case
Your CRM export has "First Name" and "Last Name" in separate columns. Before sending a mail merge or generating a report, =CONCAT()automatically stitches them into full names, making the dataset clean and presentation-ready without manual editing.
Also useful
=TEXTJOIN(", ", TRUE, A2:A10) is powerful when you need to merge a whole range with a custom separator and skip blank cells automatically.

6. COUNT() / COUNTA()

Track data entries. Know exactly how many records you have.

What they do
COUNT tallies only numeric cells. COUNTA counts all non-empty cells — numbers, text, dates, and everything in between.
Syntax

=COUNT(A2:A100)
=COUNTA(B2:B100)

Real-world use case
You're managing a survey sheet that fills over time. =COUNTA() on the respondent column instantly tells you how many submissions you've received, useful for tracking response rates in real time without scrolling to the last row.

Quick comparison

| Function     | What It Counts     |
|--------------|--------------------|
| COUNT        | Numeric values     |
| COUNTA       | Non-empty cells    |
| COUNTBLANK   | Empty cells        |

7. TODAY()

Work with live dates. Keep time-sensitive data always current.

What it does
Returns today's date and updates automatically every time the workbook is opened. No manual date entry ever.
Syntax

=TODAY()
=TODAY()-A2
=A2-TODAY()

Real-world use case
In an accounts receivable sheet, you can calculate how many days overdue each invoice is using =TODAY()-InvoiceDate. The aging calculation stays accurate every time the file is opened, so you never need to manually refresh due-date columns.
Related functions
Use =NOW() to capture the current date and time. Use =DATEDIF(start, end, "d") for more complex date difference calculations (days, months, or years apart).

8. MAX() / MIN()

Identify key metrics. Pinpoint peaks and floors instantly.

What they do
MAX returns the highest value in a range. MIN returns the lowest. Useful for spotting outliers, records, or performance floors.
Syntax

=MAX(B2:B31)
=MIN(B2:B31)

Real-world use case
You have 300 rows of daily sales data. Rather than visually scanning hundreds of figures to find the best and worst days, =MAX() and =MIN() extract them instantly. Pair with =MATCH() to also return the date of the peak, making performance analysis genuinely actionable.

Quick reference

| Function            | Purpose                    | Best Use Cases                         |
|---------------------|----------------------------|----------------------------------|
| SUM()               | Add a range of numbers     | Totals, revenue, budgets         |
| AVERAGE()           | Find the mean              | Benchmarks, performance tracking |
| IF()                | Conditional logic          | Flags, labels, dashboards        |
| VLOOKUP()           | Cross-table lookups        | Pricing, rosters, catalogs       |
| CONCAT()            | Merge text strings         | Names, IDs, mail merge prep      |
| COUNT / COUNTA()    | Count entries              | Surveys, records, completeness   |
| TODAY()             | Live current date          | Aging, deadlines, scheduling     |
| MAX / MIN()         | Highest/lowest value       | Performance, outliers, records   |

Building efficiency, one formula at a time

These eight functions aren't just shortcuts; they represent a shift in how you interact with data. The difference between a professional who scrolls through spreadsheets hunting for answers and one who has them surface automatically is usually a handful of well-placed formulas.
"You don't need to master all of Excel to be effective. You need to master the parts that touch your work every day."
Start by picking two or three from this list that apply directly to your current projects. Use them consistently for a week. The muscle memory builds quickly, and so does the time saved.
As your comfort grows, these functions also serve as building blocks for more advanced patterns: combining IF with VLOOKUP, using TODAY inside conditional formatting, or nesting MAX with MATCH to find not just the top value but exactly where it lives in your data.
Efficiency in spreadsheets isn't built in a day. It's built one applied formula at a time, and today is a perfectly good day to start.

#excel #productivity #spreadsheets #dataanalysis #tutorial #beginners #officeskills

How to Get Data Into Power BI as a Newbie

Mercy-Shalom Adedayo — Sat, 04 Apr 2026 22:59:22 +0000

Introduction

In today's world, companies have data everywhere. Excel sheets, company databases, cloud apps, and websites. As a beginner data analyst, your first big skill is grabbing all that data and putting it in one place. Power BI makes this super easy with its simple tools.

Power BI is like a magic dashboard maker. It connects to tons of data spots, cleans up the messy info, and turns it into cool charts and reports. Whether it's a basic spreadsheet or fancy online storage, Power BI pulls everything together so you can see the full story.

The hero behind this is Power Query. Think of it as your data cleaner and organiser. It does ETL: Extract (grab the data), Transform (fix and reshape it), and Load (put it into Power BI for analysis). No coding needed, just clicks!

Power BI connects to all these common sources:
• Files like Excel (.xlsx) or CSV.
• Databases like SQL Server (your company's main data storage).
• Cloud stuff like Google Sheets or SharePoint.
• Websites and APIs (live data from the web).

It has ready-made "connectors" for over 100 sources. You pick one, sign in if needed, and choose Import (copies data in) or Direct Query (keeps it live and fresh).

Microsoft teaches this in their free training: Always check your sources first, pick the right way to connect, and make loading fast. Data isn't neat in one file, it's scattered, so mixing it right is key to good reports.
This blog walks you through it step-by-step:
• How to click "Get Data" and pick sources.
• Real examples with Excel, SQL, and web data.
• Tips to link everything without headaches.

By the end, you'll confidently pull data from anywhere in Power BI. No more data chaos, just clear insights!

Connecting Data from Multiple Sources

Power BI allows you to connect to a wide range of data sources. Below are step-by-step guides for each major source.

Step 1: Connecting to Excel

Open Power BI Desktop
Navigate to Home → Get Data → Excel
Browse and select your Excel file
In the Navigator window, select the required sheets or tables
Click Load (to import directly) or Transform Data (to clean first)

Step 2: Connecting to Text/CSV Files

Open Power BI Desktop
Navigate to Home → Get Data → Text/CSV
Browse and select the CSV file (e.g., 202601-divvy-tripdata.csv)
Preview the dataset in the dialog window
Click Load or Transform Data

Step 3: Connecting to PDF

Open Power BI Desktop
Navigate to Home → Get Data → PDF
Select the PDF file
Wait for Power BI to detect available tables
Select the desired table(s)
Click Load or Transform Data

Step 4: Connecting to JSON

Open Power BI Desktop
Navigate to Home → Get Data → JSON
Select the JSON file or input API endpoint
Load the data into Power Query
Expand nested fields to structure the data properly
Click Close & Apply

Step 5: Connecting to SharePoint Folder

Open Power BI Desktop
Navigate to Home → Get Data → SharePoint Folder
Enter the SharePoint site URL
Click OK and authenticate if required
Select files from the folder
Click Combine & Transform Data

Step 6: Connecting to MySQL Database

Open Power BI Desktop
Navigate to Home → Get Data → MySQL Database
Enter the server name and database
Provide authentication credentials
Select the required tables
Click Load or Transform Data

Step 7: Connecting to SQL Server

Open Power BI Desktop
Navigate to Home → Get Data → SQL Server
Enter the server name
Leave the database field blank (or specify one if needed)
Click OK
Select authentication method
In the Navigator pane, expand the database
Select required tables
Click Transform Data to open Power Query Editor

Step 8: Connecting to Azure Analysis Services

Open Power BI Desktop
Navigate to Home → Get Data → Azure → Azure Analysis Services
Enter the server name
Select the database/model
Choose connection mode (Live connection recommended)
Click Connect

Conclusion

Connecting to multiple data sources in Power BI is a key step in building reliable and meaningful reports. Data often comes from different places, such as files, databases, and cloud services, and Power BI makes it easy to bring everything together in one environment.
With tools like Power Query, you can not only connect to data but also clean and transform it into a structured format ready for analysis. This preparation stage is essential, as well-organised data leads to more accurate insights and better decision-making.
For beginners, learning to connect to and prepare data properly lays a strong foundation for creating effective dashboards. Ultimately, successful data analysis in Power BI starts with how well you gather, clean, and combine your data from different sources.