Hi Everyone,
I’ve compiled a set of best practices I use to clean messy tabular flat‑file data, most often from CSVs or PDF‑to‑Excel exports for general ledger, payroll, sales, and similar reports. These requests usually involve applying the same cleanup steps across many files or sheets and combining them into a single table under tight deadlines and inconsistent data structures.
These tips focus on a quick and fast cleanup rather than long‑term or repeatable workflows, so I avoid macros or advanced concepts.
Let me know if these are helpful or if there are other approaches worth considering.
Common Tools
Input Data Tool (Link)
- I often output the file name as a field to use for validation or pivoting in the Summarize tool.
- Use “Start Data Import on Line” or the Select Records tool when the first lines of data or headers are buried below report formatting.
- I enable “First Row Contains Data” for instance where I know the headers are not able to be used. This is also helpful if you’re using the workflow for multiple datasets and you do not want to manually identify the needed columns each time.
- If using the headers in the files later, use the Dynamic Rename tool and the mode Use Headers From First Row. Or, use the tool to label staging fields quickly; for example, renaming all the fields “Pre” if you know you will use those column names later.
Record ID (Link)
- Add a Record ID as soon as the structure stabilizes.
- This has many benefits, such as:
- Track how rows move, disappear, or duplicate.
- Serve as a join field.
- Functions as a sort field.
- Make it simple to filter for specific records when troubleshooting or validating.
Data Cleansing (Link)
- I find benefit with Remove Null Columns, but avoid Remove Null Rows as I have experienced valid rows drop unexpectedly.
- I avoid Replacing Nulls with Zeros or Blanks because doing so changes how the end users interprets missing values. It can also make tracking difficult when comparing to source material.
- I do trim whitespace, however, there are cases when keeping them is beneficial. For example, if a field has fixed-width padding then you can use that to isolate it using a length formula. If I were to have removed the whitespace, then I would lose the fixed-width as an anchor.
- If applicable, I apply Modify Case, preferably to Title Case, if it helps with readability and the end user approves of it.
- While this tool is straightforward, I recently came across a use case where the order of a union mattered.
- When joining between multiple sheets, I realized only one sheet had the headers intact, so that sheet had to be ordered first. Otherwise, the effectiveness of the Multi-Row Formula tool I was using would be disrupted.
- To set the order, enable “Set a Specific Output Order” in the Configuration pane.
Multi-Row Formula (Link)
- This tool is critical and comes up in almost every request. This is because the reason someone reaches out for help is often because they were having trouble filling in important fields across the report so that they can pivot and filter.
- For example, the use case could be to take the account or department number that only appears once in the beginning of the section, or several account numbers throughout the report, and populate it with every record.
- To configure the tool for this use case, select “Update Existing Field” for the field you want filled in. Do not select “Group By”.
- To fill down a null value from the previous row (most common): IF IsNull([Field]) THEN [Row-1:Field] ELSE [Field] ENDIF
- To pull up a value from the next row: IF IsNull([Field]) THEN [Row+1:Field] ELSE [Field] ENDIF
DateTime (Link)
- Converting dates to the right format is important for the end user to be able to filter and calculate dates within Excel.
- One frustration with this tool in Alteryx is that it requires you to rename the output column. My suggestion is to suffix the original field with “Pre” and then name the final name of the date field within the DateTime tool. Then drop the original field once validated. This preserves field lineage and avoids naming confusion after.
Summarize (Link)
- The Summarize tools is used for subtotal and total validations.
- Also, use the Summarize tool if a column which informed a level of detail is being removed and you need to consolidate the records to a lower level of detail. For example, if the end user requests to remove any employee level of detail.
Containers and Annotations (Link)
- I use containers to group workflow phases and temporarily disable work during testing.
- I rename annotations on tools I expect to revisit so the purpose is clear at a glance. I turn off the ones
Design Patterns
File and folder structure
- I found some version of this folder logic helpful for me for most Alteryx projects: Data > Workflow > Output > Deliverables.
- Outputs could be intermediate YXDBs for use between workflows or files that I want to review or format before delivery.
- Deliverables holds the the final requestor-facing copy. I keep it separate from Output so that I do not accidentally overwrite work an end user has done.
Review expected deliverable with the end user
- Ensure that you are only working with data that is important to the requestor or end user.
Deliver a sample for proof-of-concept
- If the data is large or there is urgency, working on a sample and sending it to the end user to validate is helpful could prevent future rework before working through the whole dataset.
Use YXDB file type for efficiency
- I consolidate data into the YXDB file type early if the data source spans many files, periods, or formats. The YXDB file type is developed by Alteryx and processes significantly faster than Excel. If I have multiple Alteryx workflows, I’ll output the intermediary files between them as YXDBs.
Isolate columns rather than clean
- Rather than try to clean up an existing field, I find it’s often less of a headache to isolate the values I need into a new field.
- For example, rather than try to clean up the “Credit” column, I’ll prefer to create a new “Credit” column using the Formula tool and extract the data I need from the original column.
- Similarly, if a column has multiple pieces of information that needs to be separated, such as Year and Month, I’ll create a new Year column and a new Month column to capture those details.
- This limits the number of considerations needed to make when trying to remove data, as you cannot usually see all the irregularities you’ll encounter in a large dataset, prevents unintentional impact onto other fields, and gives me more control if I want to make a change later.
- I can use the original column for reference and then deselect it when complete.
Troubleshooting by isolating records
- Use the Filter tool to isolate the problematic Record IDs before the issue appears and follow them through the workflow.
Validating totals before handoff
- Subtotals and grand totals from the original dataset need to be removed to prevent double counting. Once isolated, I’ll recreate the totals using the Summarize tool when I’m complete. Then, I’ll use the Join tool to compare them to what was listed in the report.
- For the Join, I label the totals from the report “Expected” and the ones I create called “Actual”.
- To identify errors across many records, I create a field to calculate Expected – Actual. For the difference, I use the absolute function ABS() to normalize negative and positive differences and then use the Round() function to normalize the amounts to two decimal places; otherwise, minuscule differences will show up as errors.
- For example,
- Difference Amount = Abs(Round([Expected Amount – Actual Amount], 2))
- Then, I use a filter to identify any differences > 1, as the results from > 0 often include differences due to rounding.
To summarize, these tips reflect my approach to data prep for tabular reports when the ask is urgent and the source is imperfect. If you have approaches that you also find beneficial, any suggestions, or corrections, please drop a message.
