Gray magnifying glass icon. In the center are circles in red, yellow, and blue. Some are filled in and others are circle outlines. The background is black.

If you think you have a problem with your product data, you probably do. Common symptoms of a product data issues might look like this:

  • Broken faceted search on your website
    Incomplete or inaccurate data appearing in filtered categories
  • Automation errors from elsewhere in your product data journey
    For example: PLM, DAM, ERP, or downstream partners
  • Instances of incomplete or “dirty data”
    Sent in by internal stakeholders, customers, distributors, or really good friends
  • Use of custom-built tools and/or Excel to store and update product data
    You know human-generated errors are present but you can’t exactly point to where
  • Documentation of product data issues, but no idea where to start
    Which fixes are highest priority? Which fixes be automated?

Why you need a product data audit

Product data takes a long, convoluted journey through most businesses. Many people are involved in the process. The more products you have and channels you send to, the more likely it will have errors.

Product data is the oil of your ecommerce engine. If it is dirty or missing important elements, it can disrupt ecommerce performance.

Before you can add new sales channels or scale ecommerce, you must wrangle your product data. You must know which channels you plan to sell through and what data fields they require, in what format. Your data must be clean, ensuring that your business can produce, manage, and syndicate it.

Most businesses don’t have their product data in order. But the first step in getting there is a product data audit.

Dozens of small circles in red, yellow, and blue. Some are filled in completely; others are outlined. Sporadic gray circles are labeled "dirty data." Gray dotted line circle outlines are labeled "missing data."
Illustration with a gray background. At the top, black document icons disperse into dots that are yellow, blue, and red. Some of these dots are connected by lines of the same colors. At the bottom, a darker black background. The colorful dots fall into homogenous groups. Red dots are stacked neatly with red dots. Blue dots get stacked neatly with blue dots. Yellow dots go neatly with other yellow dots.

What is a product data audit?

A product data audit is an actionable analysis. It identifies the major problems in your data. It helps determine what’s fixable via automation—and what needs help from skilled copywriters or product managers.

For many companies, this may seem daunting or even impossible. You may have tens of thousands of products and hundreds of data attributes for each product. That translates to millions or more individual data points to address.

Ntara’s approach makes this massive task more manageable. We select representative samples of each major data source. Then, we apply a series of proprietary data tools and processes to get an accurate picture of your data. This eliminates the need to examine every individual data field.

How Ntara approaches product
data audits

An Ntara data audit will score your product data performance across six key categories.

  • Parent/child segmentation
    How have you categorized standard product features (shirt) versus choices (size, color)?
  • Duplication in data
    How much duplicate data exists across your rows, unique IDs, or unique descriptions?
  • Data consistency
    How well are you maintaining data clarity with data elements, data types, and variables?
  • Data completeness
    How many holes do you need to fill across your product dataset?
  • Marketing content
    Do you have product descriptions, benefits, cross-sells, upsells, kits, configurations, etc.?
  • Marketing maturity
    Is your data complete enough to meet downstream channel requirements?

This six-pillar analysis shows what’s causing your biggest product data challenges.

Illustration of a product data audit scorecard. On the top left, with a black background, are the words "product data audit." Underneath are the categories that data is graded on: parent/child segmentation, duplication in data, data consistency, data completeness, marketing content, marketing maturity. To the right are three columns. The first is red and has earned 1 of 3 stars. The second is yellow and has earned 2 of 3 stars. The final is blue and has earned 3 of 3 stars.

What you get from a product data audit

After the audit, you’ll get a custom optimization plan. It will outline the current state of your data and the level of effort required to fix it.

Black icon of graph and dollar sign.

Composite data score

Compete audit report, showing data analysis by category and detailing overall score

Black icon of a spray bottle

Readiness assessment

Is your data ready for PIM or does it need cleansing first?

Black icon of a wrench and a hammer crossed

Data fixes

Listed in priority, detailing what can be automated and what gets fixed manually

Black icon of a checkmark inside a box

Additional considerations

Suggested data points to capture and where they should come from

Who’s going to fix it

We can help you manage fixes or hand off the plan to your internal team 

Learn more about standardizing your product data.

In this article, learn how to reduce your risk through data cleanup and data management.

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