How to Import and Clean Up Existing Formula and Material Data

Step 1: take stock of what you have

List every source: spreadsheets, CSV exports from old software, documents, and paper (which needs transcribing first — how to digitize perfume formulas covers that project). For each, note what it contains, its format, and whether it is the latest version of that data. Make a copy of everything into a working folder and never edit the originals.

Step 2: decide the target structure

Find out exactly what the receiving system expects: its column names, units, required fields, and how it links entries to materials. Most systems import materials and formulas separately, and need materials to exist before formulas can refer to them. Write down how each of your columns maps to theirs.

Step 3: clean the material list first

• One name per material. Merge "hedione", "Hedione", "Hedione (Firmenich)" if they really are the same product; separate them if they are not.
• Codes. Assign each material a unique code if it has none.
• Strip dilutions out of names. "Ambroxan 10%" is the material Ambroxan at a 10% dilution; the dilution belongs in the formula entry, not in the material's name.
• Units and numbers. Prices to a consistent unit (per gram), decimal points consistent, no text in number columns ("approx. 5").
• Blanks. Leave unknown values empty; do not type 0 for "unknown".

Naming conventions are covered in fragrance material names and CAS numbers.

Step 4: clean the formulas

• One row per entry, with formula code and version on every row (the layout of the perfume formula template).
• A dilution on every row, 100 for neat materials. Where the old record does not say, find out or flag it — never assume neat.
• One unit per formula, converted to grams or percentages consistently. Drops and milliliters need converting by weighing or by density.
• Material references that match the cleaned material list exactly.
• Versions. If the old system overwrote formulas, you may only have the latest; label it clearly. If you have several copies, decide which is which.

Step 5: check totals before importing

For each formula, calculate the total weight and the percentage sum in the cleaned file and compare with the original. A formula that totalled 100% before cleaning and 98.7% after has lost a row. Spreadsheet problems you are likely to find in old files — broken ranges, pasted values, hidden rows — are listed in spreadsheet perfume formulas: 9 failure modes.

Step 6: import into a test copy

Import into a test installation or a test library first, not your working one. Then check:

• The number of materials and formulas imported equals the number in the file.
• A sample of formulas, including the most complex, match the originals row by row.
• Dilutions arrived as dilutions, and percentages calculated in the new system match.
• Accented characters and decimal numbers survived.
• Nothing was silently skipped; read the import report if there is one.

Only then import into your real library, and keep the cleaned files alongside the originals as a record of what was done.

Common import problems

In RUŌOD Lab

RUŌOD Lab imports CSV, Excel and JSON files. Re-importing data it already holds is matched against existing records rather than duplicating them, and a complete backup is restored through its own Backup section rather than through import. Whatever the tool, the cleaning steps above are where most of the work — and most of the value — lies. For what to check when data leaves a system, see how to export perfume formulas.

Frequently asked questions

Should I clean everything before importing anything?

Clean the material list completely first, because formulas depend on it. Formulas can then be cleaned and imported in batches, starting with the ones you use most.

What if I don't know an old formula's dilutions?

Flag the formula as uncertain rather than guessing. Remake a small trial if you have the materials, and compare it with a retained sample or your memory of the original.