KYVORIANLOGICIELS · SYSTÈMES
FRYour project
ILLUSTRATIVE IMAGE / AI
05 / Data & connections

Data cleanup pipeline

A small repeatable pipeline for preparing a file, normalising formats and separating exceptions before a data migration.

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THE PROBLEM TO MAKE SIMPLER

Before the import, make the data understandable.

An export may be readable but still contain several versions of one name, ambiguous dates or missing identifiers. We make these problems visible rather than silently deleting them.

FOCUS / 05

We work on one agreed source format using deterministic rules. Ambiguous matches remain subject to human review: the pipeline does not make uncertain identity decisions for you.

TRY THE IDEA

Interactive illustration with fictional data. Not a live product or a client result.

Fictional three-row sample
ReferenceCodeState
ab-014 FRExtra space
AB-014FRReview match
CD-008—Missing code

THE FIRST SCOPE

What we scope.

  1. 01One agreed CSV or spreadsheet format
  2. 02Up to eight explicit cleanup rules
  3. 03An exception report and a separate output file
  4. 04A rerunnable script with instructions
AT THE END OF THE SCOPE

Something to use.
And enough to take it over.

01

Readable transformation rules

02

Repeatable script and acceptance example

03

Exception report for manual review

BEFORE STARTING

Two questions
that matter.

Are original data changed?

Work is performed on a controlled copy. The output is separate and transformations are documented.

Can you guarantee an error-free database?

No. Quality depends on the source and rules. We explain the checks and the anomalies still requiring review.

↳

In the same domain.

THE RIGHT NEXT STEP

Start with a situation.
Not a ready-made answer.

Tell us what slows down your day. Together we can define the first useful scope.

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