Improving the Accuracy of Spend Analysis through Data Quality

Improving the Accuracy of Spend Analysis through Data Quality A DataFlux White Paper Organizations can spend as much as 60 percent of revenue to acquire the goods and services necessary to conduct business. Procurement professionals are being supplier collaboration. Companies now realize more than ever the effect of procurement strategies on their profitability and viability. All organizations have data on their products, inventory, parts and services and most organizations have more product data than they have customer data. Product data lacks a Increasingly, this information is becoming even more important to the overall health standard structure, which of a business. Yet this means that poor-quality product data is also becoming traditionaly makes increasingly problematic. The unique challenges in the management of product data product data quality a can inhibit the search for supply chain optimization, spend management and a more difficult goal to achieve. unified view of the enterprise. The problems with product or item data stem from the wide variety of structure and conventions for this type of information. While customer data has a relatively small set of defined and universal attributes (name, address, email address, phone number), product data is much more complex. For example, a single company may have multiple definitions and descriptions of something as simple as a 60-watt light bulb within its product data. Just imagine how complex, inconsistent and unreliable product data can be if it arrives from a dozen different suppliers in your trading network. Organizations are also grappling with the fact that enterprise resource planning (ERP), supply chain management (SCM) and other applications have done little to solve these issues. These applications can encapsulate the processes that drive a business every day, yet they typically have no integrated data quality capabilities to find and eliminate bad data. But creating additional ERP or SCM applications on top of existing applications to correct these issues essentially develops redundant silos of product information, and further complicates an already complex task. Issues such as duplicate product numbers, obsolete product IDs and inconsistent item descriptions exist across all departments within every organization, impacting every level of the operation. An inability to understand the products that are being 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 creates difficulties in controlling the costs of production, promoting the productivity of the company, and the delivering finished goods. After all, the data within your applications drives every decision, from long-range strategic planning to day-to-day operations. 1 Addressing the Product Data Quality Problem Despite the difficulties that product data poses, a number of organizations are taking steps to directly address the data quality issues of product data. Given the complex nature of product data and the lack of standards across, and even within, organizations companies often look to data quality technology to standardize, validate and verify the integrity of this information. Data quality technology typically grew out of the customer data realm. In fact, data quality software started as a way to cleanse and de-duplicate database marketing and customer relations data. A standard process would include: Data analysis Use data profiling or data discovery to uncover strengths and weaknesses in the data. Organizations must analyze, improve and Data improvement Start to address the known problems in the data control the quality of data through automated standardization, verification, matching, clustering and on products, inventory enrichment practices. and services. Data controls Since new data is always arriving in an organization, apply some monitoring techniques to find and flag bad, suspicious or non- compliant information. This data quality process can provide the foundation for better management of product information. A data discovery effort can quickly determine if there are potential duplicates in the data set or if data lacks standards across systems. The most vexing problem for product data quality programs is the second phase, data improvement. How can you improve data without standards for organizing, classifying and managing product data? Recently, companies have embraced industry-standard commodity coding systems like UNSPSC (United Nations Standards Products and Services Codes) and eCl@ss to provide a vendor-neutral, objective way of classifying data. A standard code, when applied to a product or inventory item, can be used a way to reference and sort this data across any application. For example, within UNSPSC, the code 10122101, has both the same commodity code appended to the record, every organization supporting this code can compare prices between various pig food suppliers more effectively. Or, a company can reconcile every product data entry within their applications that has the 10122101 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. 2 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 probles 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 their expenditures based on commodity type, then drill into that type to look at product groups. Table 1 provides an example hierarchy of a UNSPSC code for personal digital assistants (PDAs). Table 1 Sample commodity coding structure. Commodity classification provides a common language for product data. organization can more readily understand how much is spent on PDAs. From here, a tool for spend analysis will give them the ability to determine what types of PDAs they are buying, who they are buying from, and how much they are spending with be part of the same group. Everything from an inkjet printer to a high-end server might fall into the same category. Today, most commodity classification is done as a service, usually through an offshore services engagement.