Bad Data – The Problem with Procurement

Making sound procurement decisions depends on accurate, reliable data. Here's how to start addressing the product data quality problem


Despite the many difficulties associated with product data management, many progressive organizations are taking steps to directly address these data quality issues. Given the complex nature of product data – and the lack of standards across, and even within, organizations – companies often look to existing data quality technology solutions to standardize, validate and verify the integrity of this information.
Data quality technology historically grew out of the customer data realm. In fact, data quality software started as a way to cleanse and de-duplicate marketing and customer relations data. A data quality technology can accomplish the critical phases of the data management process. A standard process would include:

  • Data analysis: Use data profiling or data discovery to uncover strengths and weaknesses in the data.
  • Data improvement: Start to address the known problems in the data through automated standardization, verification, matching, clustering and enrichment practices.
  • Data controls: Since new data are always arriving in an organization, apply some monitoring techniques to find and flag bad, suspicious or non-compliant information.

For example, if there were three distinct records for a Joe Smith, a Joseph Smith and a Joe Smythe, but all of them mapped to the same exact address, telephone number and e-mail identity, data quality tools would be able to reconcile these three entries into a single record. Conversely, if there were five records, all for Joe Smith, all with identical information, the de-duplication process would eliminate those extra records.

As data quality technology became more sophisticated, IT and business managers began to use this technology for product, item, inventory and other non-customer data sets. In the product data realm, a data discovery effort can quickly determine if there are potential duplicates in the data set – or if data lacks standards across systems. For example, it would discover the same light bulb is listed three times in the purchasing system, based on product attributes such as manufacturer, part number and cost. However, the most vexing problem for product data quality programs is the second phase – improving enterprise data with a standard method for organizing, classifying and managing product data.

Recently, companies have embraced industry-standard commodity coding systems like the United Nations Standards Products and Services Codes (UNSPSC), eCl@ss and GS1 to provide a vendor-neutral, objective way of classifying data. Custom classification codes are also being developed internally by many organizations. A standard code, when applied to a product or inventory item, can be used as a way to reference and sort these data across any application.

For example, within UNSPSC, the code 26121520 has both the same commodity description and meaning for every organization (in this case "copper steel wire"). With this code appended to the record, every organization supporting this code can more effectively compare prices between various copper steel wire suppliers. Or a company can reconcile every product data entry within their applications that has the 26121520 code and begin to see how much the company is spending on that type of product.

These standards are a way of acknowledging that product data can – and will – have unique representations within the systems. However, by providing a single, universal method for classifying that information, the data quality problems inherent in product data will not cause significant problems within business processes.

Using Commodity Classification to Improve Spend Analysis

Commodity classification lets organizations group related items at a detailed level, validating comparisons between items within the group. One of the primary hierarchies used in every spend analysis implementation is a commodity classification structure. This structure allows a company to analyze its expenditures based on commodity type, then drill into that type to look at specific product groups.

Table 2 provides an example hierarchy of a UNSPSC code for personal digital assistants (PDAs).

Table 2 - Sample commodity coding structure

 

Commodity classification provides a “common language” for product data

 UNSPSC Code

Product Classification

43000000

Communications and Computer Equipment and Peripherals

43170000

Hardware and Accessories

43171800

Computers

43171804

Personal Digital Assistants (PDAs)

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