Home Products Services Solutions Product Data Quality Company Resources Demos Contact Us


PDQ versus CDQ

What's the difference between PDQ and CDQ?

Customer data deals solely with customer information such as names, addresses, and social security numbers. Customer data fields always have common characteristics (such as name, address, etc) describing the customer. Moreover, because the data is usually based on a reference directory, it is easy to verify.

Sample Customer Data

ID First Name Second Name Street No. City State/ County Country Zip Phone
2347811 David Shannon Lomb Memorial Drive 85 Rochester NY USA 14623-5603 1-585-475-7892

The problems caused by incorrect customer data are obvious: Direct mail doesn't reach its destination, customer billing goes undelivered, and millions of dollars are wasted annually. By contrast, incorrect product data can be hard to identify and even harder to rectify.

Product data describes the product features and specifications, which vary greatly from product to product and from category to category. The descriptions are frequently inaccurate, unclear, and inconsistent, making it very difficult to identify the same or similar products. For example, the product descriptions below from a global company’s purchasing departments located in Korea, Israel, the United States, and Germany actually describe the same product, an Allen screw.

1. DIN 912 10x1x30-2.9 mat304
2. ALLEN SCR M10x30 stainless steel
3. SOCKET BOLT M10x1 LG30 SS
4. M10x1mmx30mm SHCS-SS

What makes product data more complex to handle?


Product data contains a large amount of technical information which differs according to the product category and type. Product data fields are category-contingent with varying attributes and values (such as type, size, and material). For example, a pump's technical attributes and values differ greatly to those of a gun drill.

One of the key challenges of product data is to adopt or develop a suitable categorization system and assign the right attributes and value to each category
(taxonomy). Effective categorization of product data requires a wide array of domain experts as well as taxonomists.

Cleansing and categorizing product data is further complicated when key product identifiers (such as manufacturer number or part number) are not available. In this all too common scenario, there are usually no external reference dictionaries to refer to. There's no simple method of verifying the existence of a particular product, let alone correctly classify it.

What is product data quality?

High-quality product data is reliable, comprehensive, consistent, and up-to-date information that can be used across the organization. Rationalized product data provides the basic information necessary to single out one product as being unique from many other products in the group. These unique identifying characteristics include part number, price, and short description, as well as product attributes, images, PDFs, and detailed descriptive information such as features and benefits, and more.

In the following example, the unclear and inconsistent product descriptions relating to the Allen screw (shown above) have been normalized into a consolidated, structured, classified, standardized table.

Sample Product Data

Category Type Screw Size Thread Pitch Head Style Fastener Length Drive System Material
Screw Machine Screw M10 1mm Cylindrical 30mm Female Hex Stainless Steel


Normalized and structured product data enhances searchability. For example, diameter may also be described as segment length, thickness, or width. Choosing one term to represent all permutations of the word diameter enables quick comparison between and within manufacturers. Learn more. (PDF 475kB)

InQuera forges content from product data

InQuera's data refinement process creates consolidated, tabulated product data through —
  • Aggregating product data from different languages, sources, applications, and formats
  • Classifying data into categories
  • Normalizing and cleansing the data
  • Extracting product attributes
  • Enriching the data
  • Identifying duplicate or similar products
  • Creating content
If your product data is incomplete, unclear, or inconsistent, it could be costing your company a fortune.

Contact an InQuera representative for a product data quality check-up.


Copyright © 2006 InQuera, All Rights Reserved.