How to Load Excel and CSV Files Using the Alteryx Input Data Tool

How to Load Data with the Alteryx Input Data Tool

The Alteryx Input Data tool is the entry point for every workflow you build. Get it right and everything downstream works cleanly. Get it wrong and you will be chasing data quality problems that are hard to trace back to their source.

For finance work this matters more than in most domains. You are often loading trial balance exports, liquidity position files, or treasury deal data where a single misaligned column can mean your totals are off, your joins fail silently, or your scheduled report goes out with bad numbers. This post covers how to configure the Input Data tool correctly for Excel and CSV files, what to check before you click run, and how to prepare your source files so loading them is straightforward.

Watch the video above for the full walkthrough. The sections below add context and the practical habits that will save you time every time you load data in Alteryx.

Why the Input Data Tool Deserves Proper Attention

Every Alteryx workflow starts with data coming in. A misread header, a skipped sheet, or a comma where a tab should be will not throw a loud error. It will just produce quietly incorrect output that you may not catch until much later.

The Input Data tool lives in the In/Out section of the tool palette on the left side of Alteryx Designer. It is the first tool in the panel. You can also search for it by typing "input" in the search bar at the top of the palette.

Drag it onto the canvas and click on it. The configuration panel opens at the bottom of the screen. This is where you tell Alteryx what file you want to read, and how.

The tool supports a wide range of file types: Excel workbooks, CSV files, text files, databases via ODBC, and more. This post focuses on Excel and CSV because those are the two formats that dominate finance team workflows.

Connecting to an Excel File

Click the dropdown in the configuration panel and select File or Database. Then use the file browser to navigate to your Excel workbook. Select the file and click Open.

Alteryx builds a connection string automatically. You will see it appear in the configuration field. The exact format of this string varies depending on how Alteryx connects to the file type, so treat what you see in the field as illustrative rather than something to type manually. What matters is what comes next: confirming the sheet selection.

Choosing the Right Sheet

Once you have selected your file, a dropdown appears letting you choose which sheet to load. This is a step that catches a lot of new users out.

Finance workbooks commonly have a cover sheet, a contents tab, several data tabs, and possibly some output or chart tabs at the end. Alteryx defaults to the first sheet by index (the leftmost tab in the workbook). That may not be the sheet with your data.

Check the dropdown carefully. Select the tab that contains the actual data table you want to work with. If your workbook has a sheet named "PRA110 Output" and another named "Working" and another named "Cover", be explicit about which one you are pointing at. Do not assume Alteryx has picked the right one.

What to Watch in the Preview Pane

Once you have selected your file and sheet, the preview pane at the bottom of the configuration panel shows you the first few rows of data. This is one of the most useful features in the tool and one of the most underused.

Look for:

• Column headers on row one. If your actual column names are appearing as data rows, or if Alteryx has named columns F1, F2, F3, you have a header issue. Tick the box that says "First Row Contains Field Names" and recheck.
• Data types in the column header bar. Alteryx infers types from what it sees. A column of dates may be read as strings if the format is unusual. A column of account codes that start with zeros may be read as integers, which will strip the leading zeros. Check the types at this stage, not after you have built ten tools on top.
• Merged cells. If your source file has merged cells in the header row, Alteryx will struggle to assign column names correctly. The preparation section below covers how to fix this before loading.

Connecting to a CSV File

The process is the same to start: drag the Input Data tool onto the canvas, open the file browser, and select your CSV file. Alteryx detects the file extension and switches its configuration options accordingly.

Setting the Delimiter and Handling Headers

A CSV file is a plain text file where values are separated by a delimiter character. The name implies commas, but in finance work you will often see files using delimiters that are not standard. Bank system exports and treasury management system outputs in particular tend to use pipes, tabs, or semicolons.

In the configuration panel you will see a Delimiter field. Alteryx often detects the right delimiter on its own, but check it. If you see all your columns collapsed into a single column in the preview, the delimiter is wrong.

Common delimiters and how to enter them:

You also have the option to control how many rows to skip at the top of the file. Some finance exports include a title row, a date stamp, or a parameter summary before the column headers start. Use the Skip First N Records option to jump past those rows before Alteryx starts reading your data.

As with Excel, confirm that "First Row Contains Field Names" is ticked if your CSV has a header row.

When the Preview Looks Wrong

If the preview looks garbled, work through this checklist:

1. Is the delimiter correct? A pipe delimited file opened with a comma delimiter will show one long string per row.
2. Are you skipping too many or too few rows? Count the header rows in a text editor if you are unsure.
3. Is the file encoded in UTF-8 or a different character set? Finance exports from older systems sometimes use Latin-1 or Windows-1252 encoding, which can produce strange characters in text fields, particularly in fields with pound signs or special characters. The Code Page option in the configuration panel lets you specify the encoding.
4. Are there quote characters wrapping fields that contain commas? A CSV with values like "Smith, John" needs the quote character set correctly so Alteryx does not split that field on the internal comma.

Fixing these things in the configuration panel costs you a minute. Finding the resulting data quality problem three tools downstream costs you much more.

Excel vs CSV: Which Should You Use for Finance Work?

Both formats are valid inputs. The choice depends on what you are doing.

Use Excel when:

• Your source data already lives in a workbook and the team maintains it there
• You need to pick a specific named sheet
• The file has data validation or formatting that signals intent (though Alteryx ignores formatting)
• You are loading a file that someone else owns and you cannot change its format

Use CSV when:

• You are working with large data volumes (for large files, CSV typically loads faster and uses less memory than Excel)
• You are automating an export from a system such as a treasury management system or a general ledger and the system outputs CSV natively
• You want a format that is less fragile: no macros, no sheet complexity, no formula dependencies
• You are building a scheduled workflow and want the simplest possible input chain

For most automation work in a finance context, CSV is the more robust choice. It is harder to accidentally break, easier to version, and faster to process at scale. Excel is fine for ad hoc work driven by analysts where the source is a maintained workbook.

Preparing Your Source Files Before You Load Them

The Input Data tool can only work with what it is given. Spending five minutes on your source file before loading it will save you repeated troubleshooting later.

Practical habits to build:

• Remove merged cells. Alteryx cannot reliably infer columns from merged header cells. Unmerge them and fill the correct label into each cell before loading.
• Put your column headers in row one. Every row above your header row is a row you have to skip or strip. If you have a report title or a date in rows one and two, remove them from the source or use the skip rows option deliberately.
• Use consistent column names. If you load two monthly files with slightly different header names ("Notional Amount" in one, "Notional" in another), your joins will fail. Standardise names at source or build a rename step early in your workflow.
• Avoid blank rows inside a data table. A blank row midway through a dataset can cause Alteryx to read incorrect row counts or split a table in tools further downstream.
• Name your Excel sheets clearly. When you come back to a workflow three months later, "Data" is more useful than "Sheet2".

None of this is specific to Alteryx. It is good data hygiene that applies regardless of the tool you are using. But Alteryx makes it easy to skip this step because the preview sometimes looks fine even when the underlying structure will cause problems later.

What Comes Next in Your Workflow

When you bring data into a Select tool to control field types and names, you want to start from a clean baseline. When you join two datasets on an account code field, both need to read that code as the same data type. When you build a Summarise tool to aggregate by entity or currency, consistent headers are what make the grouping work correctly. All of that depends on getting the input configuration right.

The Day 2 video in the series is deliberately early in the sequence because clean inputs are the foundation. Automation only becomes reliable when the data arriving at the start of the workflow is exactly what you expect, every time.

The Practical Takeaway

Do not treat the Input Data tool as a step to rush through. Before you click run on a new workflow, check the sheet selection, check the delimiter, check the data types in the preview, and check that your headers are in the right place. A two-minute check at the input stage prevents thirty minutes of debugging at the output stage.

If you want to keep building your Alteryx skills in a structured way, the Academy catalogue includes courses across data analytics and automation. The learning paths give you a guided route with a certificate at the end. If you want to see how Alteryx compares to other analytics tools, the posts on Alteryx vs KNIME and Tableau vs Qlik Sense give useful context on where each fits. And if you want early access to new videos in this series before they go public, Pro membership is the way to get there.