Preparing Data for Power BI: Using Excel to Clean, Structure, and Validate

Doing data analysis in Power BI often begins with raw data that’s not immediately ready for insight. If you’ve worked with Power BI, you already know that analysis rarely starts with a clean, structured dataset.

More often, it begins with a messy export from a CRM system, a manually maintained spreadsheet, or a data dump from some internal tool. Before you can build visuals, write DAX, or design reports, your first job is to make sense of that raw data.

This is where Excel comes in. It’s fast, familiar, and surprisingly powerful when it comes to data cleaning and preparation. Whether you’re fixing inconsistent entries, splitting out reference tables, or applying logic to flag issues, Excel gives you a hands-on way to shape your data before importing it into Power BI.

In this article, we’ll explore how Excel can streamline your Power BI workflow by helping you clean, structure, and validate your data upfront.

Why Clean Data Matters in Power BI

Bad data doesn’t just slow you down, it can mislead you. Here’s why preparing your data matters:

Power BI can’t fix what’s broken. If your data includes duplicates, inconsistent names, or invalid formats, those issues will show up in your reports. This can potentially leading to incorrect insights and messy reports.
Power BI expects tidy, tabular data. It works best with structured tables that reflect a relational model. If your data is spread across multiple tabs or a flat-file format, you’ll spend more time transforming it than analysing it.
Cleaning in Excel can reduce complexity later. Power BI’s Power Query is powerful, but it is slow and procedurally based, so it’s not always the best place to start. Doing some early prep in Excel can make your Power BI setup leaner and easier to manage.

When to Clean in Excel vs. Power BI

Excel is ideal for ad hoc cleaning, quick fixes, and visual inspection, especially when you’re working with unfamiliar data or exploring new sources. Where Excel shines in flexibility and speed, Power Query in Power BI excels at building repeatable, automated transformations. It’s better suited for handling large or frequently updated datasets.

That said, Excel remains a go-to tool for good reason. It’s approachable, widely used, and surprisingly powerful for early-stage data prep. Here’s why it works so well as a starting point before moving into Power BI:

It’s familiar to most users. There’s no learning curve or setup, just open the file and get to work.
You can see and interact with your data. Being able to scroll, filter, and edit directly makes it easier to spot inconsistencies and patterns.
It’s great for breaking out transactional and reference data. With tools like filters and “Remove Duplicates,” it’s easy to separate products, customers, or other dimension data from your fact table.
Logic is applied visibly. Whether you’re fixing inconsistent values or flagging issues, Excel lets you do it in a transparent, easy-to-follow way.

There’s no one-size-fits-all. Many Power BI users begin in Excel by shaping their data, building confidence in the logic and then shifting to Power BI for automation and scale. Both tools play an important role in a well-rounded workflow.

So, where do you start? Breaking Down the Excel Data Cleaning Process: Step-by-Step

Once you’ve decided to use Excel as your initial staging ground, the next step is rolling up your sleeves and working through the data itself. Excel makes it easy to dive in and depending on if you’re just getting familiar with a new dataset or untangling an exported mess from another system.

The key is to approach the process methodically. By following a structured workflow, you can go from a disorganised spreadsheet to a clean, Power BI-ready data model with far less frustration.

Below is a step-by-step process I use regularly when preparing data in Excel. Each step is simple but intentional and together, they lay the groundwork for faster, more reliable reporting in Power BI.

Step 1: Inspect and Familiarise Yourself with the Raw Data

Before making any changes, spend time understanding the structure and contents of your dataset. Scroll through the rows and columns to get a feel for how the data is organised. Are there consistent column headers? Are values in a predictable format? Look for red flags like:

Inconsistent entries (e.g. “AU” vs. “AUSTRALIA”)
Mixed data types in a single column
Misaligned rows or extra header rows
Empty or sparsely populated columns

At this stage, you’re not changing anything but just observing and taking notes. If you’re unfamiliar with the source, ask clarifying questions about what each field represents. This is also where you identify whether you’re working with transactional data (e.g. sales logs) or reference data (e.g. a list of products or customers), which will help guide how you structure your tables later.

Step 2: Remove Unnecessary Rows and Columns

Once you know what to keep, it’s time to remove what you don’t. This includes:

Extra header rows (common in exported files)
Blank rows or columns
Totals and subtotals
Notes, comments, or embedded instructions

Use filters or go row-by-row to carefully delete these elements. The goal is to leave behind a clean, flat table where each row represents a single record (like one transaction or product), and each column represents a single attribute (like date, amount, or category). This structure is crucial for Power BI to recognise the data and process it efficiently.

Tip: When working with Excel formatted files it is always good practice to select all values and then paste values. This will remove any hidden formatting, formulas or any other hidden information.

Step 3: Standardise Formats and Clean Values

With the dataset trimmed down, focus on consistency in data types and values. For example:

Dates: Make sure all dates are stored as actual Excel date values. Use Text to Columns or DATEVALUE to convert text-formatted dates if needed.
Numbers: Remove any formatting (like currency symbols or commas) that could be misinterpreted. Convert text-formatted numbers using VALUE.
Text fields: Use formulas to fix common issues:
- TRIM() to remove leading/trailing spaces
- PROPER() to apply consistent capitalisation (e.g. “AustralSouth Australia” instead of “SOUTH AUSTRALIA”)
- SUBSTITUTE() to replace common errors or inconsistent terms (e.g. “SKU” vs. “Product ID”)

This is also a good time to correct spelling errors and unify naming conventions. Clean values make filtering, grouping, and matching in Power BI far easier.

Tip: Excel spreadsheets are always full of hidden columns and rows that are left behind when you delete values, make sure you delete them.

Step 4: Split Data into Separate Tables

Raw data often comes in one large, flat file, but that doesn’t mean it should stay that way. In Power BI, it’s best practice to break this into a core transactional table (e.g. sales transactions) and one or more reference tables (e.g. customers, products) that connect back to the main transaction table.

To do this in Excel:

Copy key columns from the main dataset (like Product Name or Customer ID)
Use Remove Duplicates to create a unique list
Add missing descriptive fields from other sources or manually
Format each list as an Excel Table and give it a clear name, such as ProductList or CustomerDirectory

These reference tables help build relationships in Power BI and reduce redundancy in your model.

Step 5: Add New Columns for Categorisation or Flags

Sometimes, raw data doesn’t come with the context you need for analysis. Excel is a great place to add new columns that:

Flag specific records using formulas, such as:
- =IF([Amount]>1000, “High Value”, “Standard”)
Group values, such as:
- =IF(LEFT([Region],2)=”AU”, “Domestic”, “International”)
Add calculated fields, such as:
- Total profit (=[Revenue]-[Cost])
- Year or month extracted from a date (=YEAR([Date]))

These added fields make analysis in Power BI easier by reducing the need for complex DAX measures up front. They also let you filter or group data meaningfully from the beginning.

Step 6: Filter and Double-Check for Errors

After cleaning and shaping the data, do a final pass to validate everything.

Use techniques like:

Sorting columns to spot outliers or blank values
Conditional Formatting to highlight duplicates or unexpected entries
Filters to review specific subsets (e.g., sales with missing dates)
Basic formulas like =COUNTBLANK() or =[Column1] * 1 on number columns, this will produce errors on text values.
This step acts as a last line of defence. Fixing errors now prevents frustration later, especially once the data is in Power BI and driving visuals or reports.

Real-World Scenario: Preparing Sales Data for Power BI

Scenario:

Imagine you’ve recently joined a company specialising in the sale of classic and collector cars, motorcycles, and other vehicles. The sales department maintains records in CSV format, capturing details like transaction dates, customer information, vehicle models, and sale prices.

However, the data is inconsistent with some entries have missing fields, others use varying formats for dates and currencies, and there are duplicate records due to multiple imports.

Approach:

Your first task is to prepare this data for analysis in Power BI. You begin by converting the CSV files into Excel format for easier manipulation.
Next, you widen all columns to ensure data visibility and remove any empty rows that don’t contribute meaningful information. Sorting the data by transaction dates helps in identifying chronological inconsistencies.
To address duplicates, you use Excel’s “Remove Duplicates” feature, ensuring each transaction is unique. Formatting numerical columns, especially those representing sale prices, standardises the currency representation, making it consistent across the dataset. You also eliminate double spaces within text fields to maintain uniformity.
In cases where customer names are split across two columns, you combine them into a single column for clarity. This consolidated and cleaned dataset now serves as a reliable foundation for Power BI.

By importing this data into Power BI, you can create dashboards that track sales trends, identify top-selling vehicle models, and analyse customer demographics, all based on accurate and standardised information.

If you’re looking for a place to practise, check out the example data files here: github.com/OswinOz/ExcelProject. It’s a great way to put these techniques into action and see how a bit of prep in Excel can save you a lot of time in Power BI.

Conclusion

Before you dive into visuals or start writing DAX, the real work often begins in Excel. Cleaning, structuring, and validating your data at this stage sets the tone for everything that follows in Power BI. Excel offers the flexibility to explore your dataset, fix inconsistencies, and apply logic in a visible, intuitive way. This makes it the perfect starting point for building trust in your data.

By following a structured, step-by-step approach, inspecting raw data, removing clutter, standardising formats, splitting tables, adding logic, and validating your work you create a cleaner, more reliable foundation that Power BI can build on. This doesn’t just improve report accuracy; it reduces friction throughout the entire workflow.

Think of Excel as your launchpad. It helps you make sense of messy exports, test ideas quickly, and ensure your data model is rooted in clean, consistent logic. And once you move into Power BI, you can focus on what really matters: finding insights, spotting trends, and supporting more informed decisions.

Resources for Getting Better at Excel Cleaning

Ready to practise? Try working with real data:

Develop your own cleaning templates for future projects

Spend time up front cleaning your data—your Power BI reports will thank you for it.