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Cleaner examples

These examples show the kinds of business problems Cleaner is designed to solve.

The goal of each example is not to show every possible variation. It is to show how to think about a Cleaner Configuration: identify the inconsistency, define the intended standard, and build a focused set of Rules that produces a reviewable result.

Example 1: Remove unwanted spaces​

Situation​

A Customer Name Field contains values with leading or trailing spaces. To a person, the entries look nearly identical. In reports or downstream systems, however, those values may behave as different values.

Goal​

Standardize the Field so the same text is stored consistently.

Cleaner setup​

Use a Trim spaces Rule, turn on Trim spaces at the start and end, and apply it to the affected Field.

Review focus​

After the Run, confirm that:

  • values that had unwanted spaces are now clean
  • already-correct values remain unchanged
  • the cleaned output behaves consistently in sorting, grouping, or matching

Example 2: Standardize placeholder values​

Situation​

A Status Field contains several placeholder-style values that all mean the value is not available, such as N/A, NULL, and -.

Goal​

Store those placeholders as blanks so they are easier to review consistently.

Cleaner setup​

Use a Blank value cleanup Rule. Enter the placeholder examples in Blank value examples, separated by commas.

Review focus​

After the Run, confirm that:

  • all intended placeholder variations were made blank
  • legitimate real values were not changed accidentally
  • the blank values are acceptable for the next step in the workflow

Example 3: Replace recurring text values​

Situation​

An Order Status Field contains a recurring value such as Pending Review, but your team wants the standard value to be Pending.

Goal​

Bring the recurring value to the preferred business label.

Cleaner setup​

Use a Find and replace text Rule. Enter the value to find in Find and the preferred value in Replace with.

Review focus​

After the Run, confirm that:

  • the expected value was replaced correctly
  • values with different business meaning were not changed
  • the final value is the one your team wants to use going forward

Example 4: Standardize dates before matching​

Situation​

An Invoice Date Field contains dates in more than one expected format, and downstream matching works best when dates have one consistent format.

Goal​

Convert parseable dates into a consistent date output.

Cleaner setup​

Use a Standardize data type Rule. Choose Date as the Output type and enter each expected format in Accepted date formats, separated by commas.

Review focus​

After the Run, confirm that:

  • the expected date formats were standardized
  • the Problems count is reasonable
  • any values that could not be parsed are reviewed before the output is used

Example 5: Prepare data before validation or matching​

Situation​

A dataset is technically complete enough to use, but small formatting inconsistencies are likely to create noise in later validation, grouping, or matching steps.

Goal​

Standardize the relevant Fields first so later work is easier to review.

Cleaner setup​

Use a focused set of Rules that only target the Fields needed by the next workflow step.

Review focus​

After the Run, confirm that:

  • the cleaned Fields are now more uniform
  • downstream validation or matching is likely to behave more predictably
  • the Configuration improves consistency without changing the intended meaning of the data

Example workflow pattern​

A common workflow pattern is:

  1. inspect the source File and identify the inconsistency
  2. build a focused Cleaner Configuration
  3. test on a small representative File
  4. run Cleaner on the intended File
  5. review the result carefully
  6. proceed to validation, reporting, export, or another WebHammers step as needed

How to use these examples​

Use these examples as planning models.

When you are building your own Configuration, write down three things before you start:

  • the Field or Fields you want to clean
  • the inconsistency you are trying to remove
  • the exact form the cleaned values should take

If you can describe those clearly, you are usually in a good position to create a successful Cleaner Configuration.