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Data manipulation

Data management and qualityIntermediate Level

Data manipulation is changing data to make it more useful, such as cleaning, converting, combining and restructuring product data in bulk.

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What is Data manipulation?

Data manipulation is changing data to make it more useful: sorting, filtering, cleaning, converting, combining and restructuring it. In e-commerce it means editing product data in bulk, such as standardizing units, splitting a field into attributes, rewriting titles or adding a markup to prices. The term can also mean falsifying data, which is a different and unethical practice. Typical product data manipulation includes find-and-replace across thousands of rows, converting "10cm" and "100 mm" to one unit, building titles from attributes, mapping supplier categories to your own, and removing duplicate rows. It overlaps with data transformation, which usually refers to converting data from one structure to another during an import or export. Doing this by hand in spreadsheets is slow and error-prone, so most teams use bulk tools with rules they can repeat. Our guide to bulk catalog operations shows common patterns.

Why Data manipulation matters for e-commerce

Product data rarely arrives ready to use. Suppliers send different units, formats and category names, and every channel wants its own structure. Data manipulation turns that raw input into clean, consistent product data. When it happens by hand in spreadsheets, the same fixes are repeated every import and errors slip through. Repeatable bulk rules on one central catalog make the work faster to repeat and easier to check.

Examples of Data manipulation

  • 1Task: Standardize units, Before: 10cm, 100 mm, 0.1 m, After: 10 cm
  • 2Task: Build titles from attributes, Before: Free-text titles, After: Brand + Product type + Color + Size
  • 3Task: Split a field, Before: "Cotton, blue, size M" in one field, After: Material, Color and Size attributes
  • 4Task: Price update, Before: Old list prices, After: Channel price with a fixed markup
  • 5Task: Remove duplicates, Before: Two rows with the same EAN, After: One row per product
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How WISEPIM Helps

  • Edit many products at once with bulk editing in WISEPIM, from find-and-replace to setting attribute values.
  • Build titles and descriptions from attributes with Template Strings and Content Logic.
  • Run recurring imports and exports on a schedule with automations (hourly, daily, weekly or monthly).
  • Undo a bulk change with Operation history on paid plans, or roll back a whole import with Rollback Import.
  • Set channel prices with pricing rules instead of editing prices product by product.

Common mistakes with Data manipulation

  • Editing the same supplier file by hand after every import.
  • Making bulk changes without a backup or a way to undo them.
  • Fixing data in an export file instead of in the central source.
  • Mixing units and formats in one field.
  • Testing a bulk rule on the whole catalog instead of a small sample first.

Tips for Data manipulation

  • List the edits you repeat after every import and turn them into rules.
  • Test each bulk change on a small sample before running it on everything.
  • Store one unit per attribute and convert only on export if a channel needs it.
  • Keep raw supplier data separate from your cleaned product data.
  • Make sure you can undo bulk changes before you start.

Trends around Data manipulation

  • AI that suggests cleanup rules and fills missing values.
  • Natural language commands for bulk edits, such as "convert all weights to kilograms".
  • Scheduled automations instead of manual spreadsheet work.
  • No-code tools that let business users build data rules without developers.

Tools for Data manipulation

  • WISEPIM: PIM with bulk editing, Template Strings, Content Logic and scheduled automations for product data.
  • Microsoft Excel and Google Sheets: Spreadsheets for small, one-off edits.
  • OpenRefine: Free tool for cleaning and transforming messy data.
  • Python with pandas: Code library for large or complex data manipulation.
  • SQL: Query language for manipulating data in databases.

Related Terms

Access Control (PIM)Attribute Dependency ManagementAttribute InheritanceAttribute ManagementAttribute mappingAttribute Scoping

Also Known As

Data processingData modificationData wranglingData preparation

Frequently Asked Questions

In data work, data manipulation means changing data to make it easier to use: sorting, filtering, cleaning, calculating, merging tables and reshaping fields. In a research or reporting context, the term can also mean altering data to mislead, which is a form of fraud.

They are close. Data modification usually refers to changing stored values, such as updating, inserting or deleting records. Data manipulation is broader and also covers sorting, filtering and reshaping data without changing the source.

Data transformation converts data from one structure or format to another, often during an import or export. Data manipulation covers any change that makes data more useful, including transformation, cleaning and bulk edits inside your own system.

These are agencies or freelancers who clean, convert and restructure data for you, for example to prepare a product catalog for a new platform. They suit one-off projects; for ongoing work, repeatable rules in your own tools are usually cheaper.

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