Product Data Audit Services

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?