3 Common Ways Companies Tackle Enterprise Data Quality Problems

Regardless of size, businesses with enormous amounts of data all deal with one common problem: how to manage data quality and make smart, valuable use of it. With enterprise systems, the question of how to manage data quality, in particular product data quality, can become complicated with the need to have it in various systems and forms.

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The product data may need to be used in various locations, across different systems, or used outside the enterprise by different users (such as buyers or vendors). But what may be even more important to understanding product data quality is knowing that there is a high degree of variability in the information.

Why is this? Product data deals with categories of all sorts (color, style, number), which can delve into many variations. Many companies resort to handling this manually or performing a custom sort, making the product data quality usable.

Here are three ways many companies usually deal with the massive variability of product data:

  1. Change records with multiple columns from various sources – allows business to understand data from different sources by conforming to one common standard
  2. Aggregate data into standard classifications
  3. Make all data conform to corporate standards, using common terms and phrasing for easier understanding

There are much easier, smarter solutions to deal with product data quality. Data Ladder has developed ProductMatch specifically for the enterprise needs of companies who handle large amounts of product data. Applying semantic technology, ProductMatch is a scalable, automated solution that recognizes, cleans, and matches product data from multiple sources. For more information, visit Data Ladder.

 

 

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