Alteryx Wildcard Import: How to Pull Every Excel Sheet into One Dataset

If you need to consolidate multiple Excel sheets into a single dataset, the wildcard import method in Alteryx Designer is the fastest way to do it. One Input Data tool, one configuration change, and every matching sheet flows into your workflow automatically. The video above walks through the full process in Alteryx Designer. Read on for the extra detail, the caveats most tutorials skip, and guidance on when this method is the right choice and when it is not.

The problem: consolidating Excel files with multiple sheets by hand

You have a workbook. Twelve sheets, one per month. Or one per legal entity. Or one per product line. Someone needs a single consolidated table to run analysis or feed a report.

The manual route is familiar: open each sheet, copy, paste into a master tab, fix the inevitable formatting breakage, and repeat next month. It takes time, it is error prone, and it does not scale. Even a partly automated version that builds one Alteryx Input tool per sheet has the same underlying problem. Add a new sheet and you have to edit the workflow. Miss one and your numbers are wrong.

The wildcard import method solves this. One Input tool, one configuration, all sheets captured automatically.

If you are new to Alteryx Designer itself, it is worth reading our overview of what Alteryx does and how it works before diving into this. And if you have just started using the interface, the day one navigation guide will help you find your bearings.

How Alteryx wildcard sheet import actually works

When you point a standard Input Data tool at an Excel file, you pick a specific sheet by name. Alteryx reads that one sheet and brings it into the workflow. Simple, but static.

A wildcard tells Alteryx to match a pattern rather than a fixed name. Instead of saying "read the sheet called January", you say "read every sheet whose name matches this pattern". Alteryx loops through all matching sheets, reads each one, stacks them vertically into a single data stream, and passes that combined output downstream.

The result looks the same as if you had manually appended twelve sheets together, except it happened automatically and it will happen again the same way every time you run the workflow.

Configuring the Input Data tool for wildcard sheet import

Open your Input Data tool and connect it to your Excel file as you normally would. When Alteryx asks which sheet to read, you will see the sheet name field. This is where the wildcard goes.

The syntax described here applies to the Input Data tool using a native Excel or OleDB connection to XLSX files. If you are connecting to an older XLS format or using a different connector type, test the behaviour on your version before relying on it in production.

To import all sheets, replace the sheet name with an asterisk followed by a dollar sign:

*$

The $ suffix is the Alteryx convention for sheet references in Excel connections. The * is the wildcard character meaning "match anything". Together, *$ means "match every sheet in this workbook".

If you want to limit to sheets whose names follow a pattern, you can be more specific. For example, if your sheets are named Jan, Feb, Mar and so on, you could use:

???$

Three question marks match any three characters. This filters out sheets with longer names, which can be useful if your workbook contains a summary or lookup tab you want to exclude.

After you set the wildcard and close the configuration, run the workflow and look at the output. All your sheets will be stacked into one dataset.

Using the sheet metadata field to keep context in your combined dataset

When Alteryx combines the sheets, it adds a field to each row recording which sheet that row came from. The name of this field varies by Alteryx version and connection settings. After your first run, open a Browse tool on the output and look for a field containing the file path and sheet name. In some versions it is called FileName; in others the label differs. Identify the correct field name in your environment before writing any parsing logic that depends on it.

This matters. Once all your rows are in one dataset, you have no other way to know which month, entity, or product each row belongs to, unless that information already exists as a column in the data itself.

Use a Formula tool immediately after the Input Data tool to parse out the sheet name. The exact format of the metadata string depends on your Alteryx version and connector type, so inspect the actual field value first before writing your expression. In some configurations the string looks like C:\Reports\Workbook.xlsx|||Jan$, where you can isolate Jan by splitting on ||| and stripping the trailing $. In other configurations the delimiter or structure may differ. Always check the raw value in a Browse tool before committing to a parsing approach.

Once you have a clean sheet name field, rename it to something meaningful like Month or Entity and keep it in your dataset throughout the workflow. It is the key that lets you filter, group, and analyse by source after the consolidation.

Schema consistency: the part most tutorials skip

This is where most walkthroughs stop being honest with you.

The wildcard method works cleanly only when every sheet has exactly the same structure. Same column names, same column order, same data types. If any sheet deviates, the behaviour depends on how Alteryx resolves the conflict.

Common problems to watch for:

• Column order differences. Alteryx matches fields by name when combining sheets, but if column names differ between sheets, fields from one sheet may not align correctly with fields from another. Check the output carefully after the first run and verify that values land in the right columns.
• Extra or missing columns. A sheet with an extra column will cause that column to appear in the output. Rows from other sheets will have null values for it. This is manageable if you know about it. It is a silent data quality problem if you do not.
• Data type conflicts. If one sheet stores a value as a number and another stores the same column as text, Alteryx will coerce one to match the other. Check your output field types after the first run.
• Header rows in unexpected places. If any sheet has a blank row at the top, or a title row above the actual column headers, Alteryx may misread the structure for that sheet.

The practical fix is to agree and enforce a standard template across all sheets before the workflow runs. If you control the source workbook, lock the structure and protect it. If you receive it from someone else, build a data quality check into the workflow before anything downstream depends on the consolidated output.

Making the workflow robust and repeatable

A workflow you have to maintain by hand every month is only marginally better than the manual process it replaced. A few habits will make the wildcard import reliable over time.

Check field alignment before you trust the output. After your first run, browse the data and verify that values are in the right columns. Do not assume that field matching has resolved correctly across all sheets.

Filter out summary and blank rows. Many workbooks have a totals row at the bottom of each sheet, or blank rows between sections. Use a Filter tool immediately after the Input Data tool to remove rows where your key identifier field is null or contains text like Total or Grand Total.

Exclude sheets you do not want by name pattern. If your workbook has a lookup tab called "Reference" or a summary tab called "Dashboard", the *$ wildcard will pick those up too. Either rename them to follow a pattern your wildcard will not match, or add a Filter tool on the sheet metadata field to exclude them by name after import.

Document the expected schema. Add a Comment tool to your canvas noting the required column names, types, and order. When the workbook template changes six months from now, whoever picks up the workflow will know what to check.

When to use wildcards and when to reach for something else

The wildcard method is the right first choice when:

• All sheets in the workbook share the same structure
• You want a simple, maintainable solution with no macro overhead
• The number of sheets is variable but the naming is consistent

It becomes less suitable when:

• Sheets have meaningfully different schemas that cannot be standardised without transforming each one individually
• You need to apply different logic to different sheets before combining them
• You are consolidating across multiple separate workbooks, not just multiple sheets in one file

For those situations, a batch macro gives you more control. A batch macro runs your workflow once per input, so you can apply sheet specific transformations before the combine step. The trade off is that it takes more time to build and test. For most consolidation jobs, the wildcard method is faster to implement and easier for someone else to read and maintain.

If you want to go deeper on Alteryx workflows and automation, the Alteryx Designer courses in the Academy catalogue cover beginner and intermediate levels in structured detail. The full video series this post is part of is free to watch on the YouTube channel, starting with the video at the top of this page.

If you are still copying and pasting across Excel sheets to build a consolidated dataset, configure the wildcard import, run it once, and then spend your time on the schema consistency work. Get that part right and the workflow will look after itself from there.