Before you start: your products need to be imported already, so Data Quality has something to measure. If data is still arriving, finish importing first and work through the Import Cleanup Wizard it opens for you.
Take a baseline before you touch anything
Do this first. Once you start bulk-editing you lose the ability to prove the cleanup was worth doing, and you lose the map of what was actually broken.
1
Write down your score and your worst grades
Open Data Quality and stay on the Overview tab. Note the overall score, the letter grade, and how many products sit in D and F on the grade distribution, then screenshot it. This is the number you compare against at the end, and the D/F pile is your working set.
2
Read the action queue on the Overview
The Overview - the screen you land on after logging in - ranks the most worthwhile fixes by how many products they touch and how much they move your score, and each row opens Products already filtered to them. Working top-down tells you what the previous owner neglected without you auditing anything by hand.
3
Count the damage with preset filters
On the Products page, click Filters next to the search bar and open the Presets tab. Each of the 46 presets shows how many products have that issue right now. Note the biggest counts: they are your cleanup backlog in priority order.
4
Find the duplicates now, not later
Switch to the Advanced filters tab and use the is duplicate operator on SKU, then again on EAN. Do this before any text cleanup: cleaning makes near-identical rows look even more alike, and a repeated SKU is sometimes two genuinely different products from two suppliers, so you want to judge them while they still read differently. See filtering products for the operator list.
5
Save the filters you will re-run
Name each filter in the Save filter set box and save it. You will run these same queries after every pass to watch the counts drop, and saved sets live on your account rather than in your browser.
Clean the text, in this exact order
Each of these is a bulk action: filter to a set, select it, click Bulk Edit in the Products toolbar, and search the action by name. The order matters more than the actions do - each step makes the next one’s matching more reliable, and running them the other way round means redoing work.
1
Trim whitespace first
Trim Whitespace is the safe opener: stray leading, trailing, and doubled spaces never carry meaning, so nothing can go wrong. Do it before anything else, because invisible spaces are what make find-and-replace and case rules miss products they should have matched. Turn on Normalize internal spaces when data came from several sources.
2
Standardize case second
Standardize Text Case gives names and titles one style instead of four. Run it after trimming so the case rule sees clean word boundaries. Check the live before/after preview: fields holding brand names with deliberate styling should not be swept into a uniform case.
3
Find and replace third
Now that spacing and case are predictable, Find and Replace actually finds things. Use it here for the one-off junk your predecessor left behind: dead promo lines, a former owner’s name, placeholder text. Leave the replacement empty to delete a phrase outright. Brand and term variants are a bigger job than a single replacement - that is make terminology consistent, and it is worth doing right after this pass.
4
Remove the links that point somewhere else
Inherited descriptions are full of hyperlinks to the old storefront, a supplier’s site, or a blog that no longer exists. Remove Links strips them in bulk, and you choose whether the linked words stay. Internal versus external is decided against this project’s sitemap, so if your sitemap is empty, remove all links instead.
5
Normalize the numbers and the units
Feeds leave prices like 19.9900 and weights recorded in grams where the rest of the catalog uses kilograms. Convert Numbers rounds and rescales built-in numeric fields; Change Unit of Measure switches units with the conversion factor worked out for you, or relabels without converting when an import set the wrong unit. Both show a preview, use it.
Give the catalog a structure again
Clean text on unstructured products still browses badly. Two jobs here: empty the uncategorized bucket, and turn look-alike rows back into single products.
1
Categorize confidently first
Filter to uncategorized products, select all matching results, click Enrich with AI in the toolbar and pick AI Categorization at the default 75% confidence. Run it now rather than before the text cleanup: it reads each product’s own content, so it places better on text you have already normalized. It only uses the category tree you already have and never invents new ones, which is what makes a default-threshold pass safe to accept in bulk.
2
Run a second, looser pass on what is left
Re-filter to whatever is still uncategorized and run it again at 60–65%. You get the confident assignments committed first and only review the genuinely ambiguous remainder, instead of reviewing everything at a low threshold.
3
Fix products that landed nowhere
If a lot of products stayed uncategorized, the cause is usually thin content rather than a bad threshold. Enrich the title and description first, then re-categorize. Place the last stragglers by hand.
4
Set the primary category
Products in several categories need one designated primary, because most platforms build breadcrumbs and canonical URLs from it. Filter by category, then run Set Primary Category from Bulk Edit on that selection - after categorization, so it has the full set of assignments to choose from.
5
Group the flat variant rows
Inherited imports arrive flat: every size and colour is its own product. Select the ones that belong together on a single page and run Group as Variants from Bulk Edit to attach them to one parent. It refuses a select-all-across-pages selection and caps at 500 products per run, so plan on working through it in batches.
Measure it, then put a floor under it
Do this last, and do it in one sitting: the score only tells a story once every bulk action has landed, and rules written now are shaped by the problems you actually found rather than the ones you expected.1
Recalculate the quality scores
Bulk actions do not always refresh quality metrics straight away. Select your catalog and run Recalculate Data Quality from Bulk Edit, otherwise you will compare today’s data against yesterday’s score and conclude nothing happened.
2
Compare against your baseline
Reopen Data Quality and put it next to the screenshot you took on day one: overall score, grade distribution, content coverage funnel. This is the number you report to whoever handed you the catalog.
3
Write two or three Quality Guard rules
A cleanup without rules decays. In Quality Guard, open the Rule Templates tab and start from a ready-made check rather than Create Rule on a blank one. Pick the problems you just spent a day fixing: typically a required identifier, a minimum description length, and a minimum image count.
4
Set them to Warn, not Block
Keep the severity on Warn for the first weeks. Warned products still export and the failure is logged, so you learn how often each rule actually fires before it can stop a sync. The rule builder’s live impact preview tells you how many products would fail before you save.
How to tell it worked
Give the trend charts a week before you read them as a trend: a single recalculation moves the score at once, but the historical chart needs days of data to show direction. For a target, Data Quality documents a B (80+) as the baseline worth holding, and the thresholds themselves are configurable if your team’s standards differ.
When it doesn’t work
I cleaned thousands of products and the score didn't move
I cleaned thousands of products and the score didn't move
Quality scores refresh on their own when you edit a single product, but bulk actions may not refresh them right away. Run Recalculate Data Quality across the affected products, then re-read the dashboard.
Thousands of products are still uncategorized after two passes
Thousands of products are still uncategorized after two passes
Either the threshold is too high for this catalog or your category names are ambiguous. Lower it and re-run on the remainder, and clarify vague category names. If products keep failing to place, the real cause is usually thin product content, enrich first and categorize after. See AI Categorization.
Group as Variants is greyed out or skipped half my selection
Group as Variants is greyed out or skipped half my selection
The action deliberately refuses a select-all-across-pages selection, so a stray filter cannot build a 2,000-variant product, and it caps at 500 products per run. Products that cannot become variants are skipped and reported in the toast. Details on Group as Variants.
Remove Links stripped links I wanted to keep
Remove Links stripped links I wanted to keep
Internal and external are decided by matching each link against this project’s sitemap. With an empty sitemap WISEPIM cannot tell them apart, so scope the action to All links and re-add the ones you want, or fill the sitemap first. See Remove Links.
The score dropped instead of rising
The score dropped instead of rising
Check the Action Impact chart on the Data Quality Overview. A sharp drop nearly always traces to a specific event, an import, a batch delete, or an automated update, rather than to your cleanup. Identify the event first, then fix the products it touched.
Related
Data Quality
The score, the grade distribution, and the fix workspace you measure against.
All bulk actions
Every bulk edit used in this guide, with its full option list.
Consistent terminology
Collapse the brand-name and wording variants this cleanup will surface.
Weekly routine
Keep the catalog clean once the one-off cleanup is done.