Efficient Innovation

It is astonishing to think about the rapid pace of data creation worldwide. Recent IDC research predicts that data production will be 44 times greater in 2020 than it was in 2009. How to leverage, authenticate or secure this abundance of data is forcing disruption and innovation in nearly every industry today.

Customer-centric philosophy is influencing how industries evolve. Manufacturing and supply chain processes are increasingly reliant on quality data in order to satisfy these new demands. But for as long as computers have been in existence, the world of data has suffered from a “garbage in, garbage out” cycle of inaccuracies. Our systems will unquestioningly process the input data provided and produce often undesired output. With many other competing priorities, it has become a challenge for many organizations to guarantee inventory, reduce inefficiencies and ultimately satisfy consumers because of data inconsistencies.

To keep pace with today’s demands, manufacturers are leveraging data quality as a key asset in their growth strategies and embarking on data quality improvement programs—recognizing the need to work on foundational supply chain data and the processes that govern it.

Maintaining Data Integrity

The best way to approach a data quality improvement program is to understand that it is a journey. Data quality programs are continual, and organizations should seek to achieve fluid business processes that can keep up with the pace of new data constantly being created and shared. Data quality is the outcome of having good data governance, and knowledge and training to support good product data creation and management, so the integrity of product data is maintained through the product’s lifecycle and at all steps of the supply chain.

Initiating a data quality program can enhance efficiency, but it can also be a complex process. For example, GS1 US, the information standards organization and administrator of the U.P.C. barcode, found through recent company audits that data accuracy continues to be an issue linked to foundational product attributes, such as weights and dimensions. When weight and dimensional attributes are incorrectly communicated through the supply chain, it becomes a challenge for organizations to calculate the efficient transport of product. Also, with so much automation in today’s warehouses, entire loading operations can be shut down if the actual weight or size does not match the data attributes ascribed to them. This creates additional problems when inaccurate dimensional data is supplied to retailers, leaving them with either too much room on the shelf or the inability to fit all products in the allocated space.

Standards-Based Programs

With the goal of achieving sustainable quality data, it’s important to be systematic in your approach. Industry standards provide a common foundation for uniquely identifying products, capturing information about them, and sharing them with other companies. Adoption of these standards and best practices can create supply chain efficiencies and enable better interoperability with other organizations and industries.

Having a standards-based approach will establish reliable and interoperable data quality procedures to avoid the ripple effect of problems down the rest of the supply chain. For instance, one root cause of data quality issues between food manufacturers and retailers in the grocery sector is that the former is often asked to share information very far in advance of when a new product goes to market – information that is merely pieced together from preliminary data. This exposes the fact that new item introduction and getting the data correct at the onset of a product launch is crucial.

Similarly, standards are also critical to expediting new item set-up in the apparel and general merchandise industry, as providing a seamless, omni-channel customer experience grows in importance. In the healthcare industry, improving data quality is vital to support operational efficiencies and regulations such as the Food and Drug Administration’s unique device identification rules, which requires the input of precise data on medical devices into a massive national database to enhance patient safety.

Many times, businesses and industries pursuing data quality programs find that entire processes need to change for data to become more reliable. A strong data governance program that is widely understood and implemented will ensure business process consistency to increase the chances of the data remaining accurate.

Ultimately, as a result of our connected business environment, data quality is now a strategic asset. Accurate data flowing through the supply chain saves time, labor and other resources that often are spent on identifying and fixing bad data. A focus on improving data quality will promote a higher level of trust among trading partners and consumers that product information is complete, accurate and timely.