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


By Daniel Teachey

With the precarious economic situation, companies across the board are trying to slash costs wherever they can. Because organizations can spend as much as 60 percent of their revenue to acquire the goods and services necessary to conduct business, procurement professionals are being asked to reduce the organization's overall spend, some by as much as 20 percent in a year. Companies now realize more than ever the effect of procurement strategies on their profitability and viability.

Since all organizations have data on their products, inventory, parts and services – and most organizations have more product data than they have customer data – this information is becoming increasingly important to the overall health of a business. However, poor-quality product data have been causing problems as long as enterprises have been collecting this kind of information. The unique challenges in the management of product data can inhibit the search for methods to optimize the supply chain, improve spend management and create a more unified view of the enterprise.

The problems with product data stem from the actual structure and conventions of this type of information. Unlike customer data, which have a relatively small set of defined and universal attributes (name, address, e-mail address, phone number), product data are much more complex. For example, the definitions and descriptions of a 60-watt light bulb may be completely different within the same company. (See Table 1.)

Table 1 - Light bulb classification confusion

 

Commodity classification provides a “common language” for product data

 Product

Product Description

Light bulb

60-watt, frosted

Light bulb

60-watt, brass base, 10,000 life hours

Light bulb

Incandescent, A19 bulb shape, 60 watts

If something as simple as a light bulb can cause procurement confusion, just imagine how inconsistent or unreliable product data can be if the information arrives from dozens of different suppliers in your trading network. Each supplier could have a different item number, and the description fields may contain contradictory or non-standard information.

Organizations are also grappling with the fact that enterprise resource planning (ERP), supply chain management (SCM) and other applications have done little to actually help address these issues. These applications encapsulate the processes that drive a business every day, yet they typically have no integrated data quality capabilities to find and eliminate bad data. Furthermore, creating additional ERP or SCM applications on top of existing applications – which essentially develops redundant silos of product information – only exacerbates an already complex task.

Issues such as duplicate product numbers, obsolete product IDs and inconsistent item descriptions exist across the organization, affecting every level of the operation. An inability to understand the products that are being sold can dramatically hinder the organization's ability to plan for new products in the future. Similarly, a confused, disparate view of direct and indirect spending can foil the most well-intentioned spend management efforts.

The bottom line is that poor-quality product data create difficulties in controlling the costs of production, reduce the productivity of the company and affect the delivery of finished goods. After all, the data within your applications drive every decision you make, from long-range strategic planning to day-to-day operations.

Addressing the Product Data Quality Problem

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