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Filterer overview

Filterer

Filterer helps you keep the Records you want, remove the Records you do not want, or isolate a specific subset for the next step in your workflow.

Filterer is most valuable when the business question is clear and repeatable. Instead of manually opening a spreadsheet and applying ad hoc filters, you can save a Configuration that applies the same selection logic each time similar data arrives.

What Filterer is for​

Use Filterer when your main task is to answer one of these questions:

  • Which Records should stay in this dataset?
  • Which Records should be removed?
  • Which Records should be separated into a smaller working set?
  • Which Records belong in a specific downstream process, export, or review queue?

In practical terms, Filterer turns a broad File or dataset into a targeted result.

What Filterer does not do​

Filterer is for Record selection. It does not clean values, validate Field-level quality, map Fields to a different structure, or provide a Field keep/drop/reorder step.

Use another Tool first when the source values need to be cleaned, validated, deduplicated, mapped, or reshaped before the filtering decision is ready to review.

What a Filterer Configuration does​

A Filterer Configuration defines the logic for deciding which Records belong in the output.

A strong Configuration usually includes:

  • a clear purpose
  • the input Fields that matter for the decision
  • Rules that reflect the business logic
  • a practical way to review whether the result is correct
  • a name that makes the purpose obvious later

The goal is not to create the most complicated Rule set possible. The goal is to create the clearest repeatable Rule set that solves one specific problem well.

Typical Filterer workflow​

A common workflow looks like this:

  1. Review the incoming data and confirm the Fields you will rely on.
  2. Create or open a Filterer Configuration.
  3. Define the Rules that determine which Records should be kept or removed.
  4. Review each Rule preview and the order of the Rules.
  5. Save the Configuration.
  6. Run the Configuration on the intended File or dataset.
  7. Review representative Records and row counts in the result.
  8. Reuse the same Configuration the next time the same business need appears.

Examples of good Filterer use cases​

Common use cases include:

  • keeping only Records in a target date range
  • excluding test, training, or demo Records
  • isolating one status, region, category, or owner group
  • creating an exception list for human review
  • preparing a subset for upload into another system
  • separating Records that meet a narrowly defined operational Rule

What makes a Filterer setup effective​

The best Filterer Configurations share a few traits.

One clear business purpose​

A Configuration should answer one main question. For example:

  • "Keep only currently active customer Records."
  • "Exclude test Records before export."
  • "Isolate orders that need manual review."

If a Configuration tries to solve many unrelated problems at once, it becomes harder to test, explain, and maintain.

Explicit Rule logic​

Your Rules should reflect how the business would explain the decision in plain language. If you cannot explain the intended output in one or two sentences, the Configuration may still be too broad.

Representative testing​

Do not judge a Configuration by one perfect Record. Test it against realistic examples, including edge cases such as blanks, unexpected values, boundary dates, or mixed statuses.

Easy review after the Run​

You should know what "looks right" before you run the Tool. That may include expected Record counts, known examples that must remain, or known examples that must be removed.

When Filterer is not the best starting point​

Filterer is not usually the best first step when the core problem is something else, such as:

  • inconsistent formatting that should be standardized first
  • duplicate detection and Record survivorship
  • Field mapping or reshaping
  • Field-level validation against Rules or allowed values

In those cases, start with the WebHammers Tool built for that task, then use Filterer later if you still need to isolate a subset.

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