The Crude Reality of Product Data Costs - Indix



The Crude Reality of Product Data Costs

Let’s imagine, for a second, what would happen if everyone that needed gasoline for their vehicles decided to prospect, drill, refine and process oil themselves instead of leaving it to companies like Shell and Exxon Mobil?

It would obviously lead to chaos and not make any sense from an operational, economical, or logistical perspective.

Yet when it comes to data, which has often been likened to oil, the individual approach to collection, processing, and distribution is exactly what’s happening.

The product information space is an especially problematic one where hundreds of millions of dollars’ worth of resources are collectively being spent annually by companies of all types to address their product information needs.

In order to get an idea of the scope of the problem, let’s look at the high-level components/capabilities that are major cost-drivers in the world of product information:


  • Product catalog/feeds ingestion and processing infrastructure and resources
  • Seeding, crawling. and parsing capabilities for companies that need to aggregate crawled data


  • Classification to individual taxonomies
  • Matching of identical products
  • Enrichment to include attributes, descriptions, imagery, and other creative elements
  • Association of products to each other (bought/sold together, similarity of attributes, etc.)
  • Standardization of data, formats, and specifications
  • Search and identification algorithms


  • Product catalog/feeds egress infrastructure
  • Destination format compliance management
  • Freshness and permissions management


  • Storage and retrieval infrastructure
  • Security and redundancy components

There are likely many more I haven’t listed but almost every company that is dealing with product data today is incurring avoidable or unnecessarily high costs from one or more of the items above.

What’s worse is that such duplicated efforts across brands, retailers, and commerce enablers of all types and sizes to get and use the same data is not only distracting them from their core mission but is also likely driving up costs for the end consumer of that product or service.

In subsequent posts, I’ll examine and dive into what’s driving this type of fragmented behavior and what can be done about it but in the meanwhile I’d welcome anyone dealing with product data to look through this list and think of ways to reduce their current spend by at least 15 percent.

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