If you’re just getting started with Software Asset Management (SAM), you’ll quickly come across the term normalization. In simple terms, normalization ensures that your software data like product names, versions, publishers, and editions is consistent and accurate across your entire organization. Without it, your reports might treat the same software as different products like listing “MS Office,” “Microsoft Office,” and “Office 365” separately, even though they’re all the same thing.
To make this process easier, ServiceNow provides Normalization Suggestions for software discovery models, as a functionality that helps keep your software data in check. If you’re new to SAM, understanding how these suggestions work will be key to maintaining clean, reliable, and audit-ready software data.
What Are Normalization Suggestions?
Normalization suggestions are like little reminders from ServiceNow that help you make sure your software discovery data is standardized and accurate.
Every week, ServiceNow compares the data you’ve manually entered (or "normalized") with the official standards from its Content Service—a trusted source for accurate software details. If there’s any mismatch (or even if everything matches perfectly), ServiceNow creates a normalization suggestion. This suggestion tells you whether the data in your discovery model matches the official standards or not.
You can think of these suggestions as a kind of "spell check" for your software data. They flag any inconsistencies and give you the chance to fix them before they cause problems.
When Are Normalization Suggestions Created?
Normalization suggestions are created automatically during the weekly normalization process in two main scenarios:
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When something in your discovery model doesn’t match the suggested values.
For example, if your discovery model lists the Publisher as "Microsoft Corp," but the standard value from Content Service is simply "Microsoft," the system will suggest an update to correct the difference. -
When everything in your discovery model matches the suggested values.
Even if your data is perfectly aligned with the official standards, the system will still create a suggestion—just as confirmation that everything is in order. It’s a way to ensure transparency and give you peace of mind.
What Happens When You Accept or Reject a Suggestion?
Once a normalization suggestion is created, you have two choices: accept or reject it. Here’s what happens in each case:
Accepting a Suggestion
When you accept a normalization suggestion:
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The relevant fields—like Publisher, Product, Version, Edition, Platform, and Language—are updated to match the standard values from Content Service.
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Your discovery model’s status changes from "Manually Normalized" to "Normalized." This means it’s officially aligned with the standard values.
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The suggestion’s status will change to Accepted.
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The normalization date will reflect the date you accepted the suggestion.
In simple terms: accepting a suggestion means you’re aligning your discovery model with the most accurate and up-to-date information available.
Rejecting a Suggestion
If you reject a suggestion:
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Your discovery model will keep its current, manually entered values.
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The model will stay in "Manually Normalized" status.
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The suggestion’s status will change to Rejected.
You might reject a suggestion if you have a valid reason for keeping your data as-is. For example, perhaps the software you’re tracking has a custom version or naming convention that’s specific to your organization, and it doesn’t fit the standardized naming from Content Service.
Automatic Acceptance of Suggestions
To make normalization even easier, ServiceNow has a feature called auto-accept normalization suggestions.
When the above system property (called com.snc.samp.auto_accept_normalization_suggestion) is enabled (and it’s enabled by default), the system will automatically accept normalization suggestions for you if there’s no difference between your manually entered values and the suggested standard values.
This means if your data already matches perfectly, you don’t need to manually review every single suggestion—the system takes care of it automatically.
