Data Quality: Taking it From the Supply Chain to the Consumer

By   /  December 28, 2017  /  No Comments

data qualityLast year, GS1 US went live with its National Data Quality program to improve data accuracy for the optimal exchange of supply chain product information among trading partners and for improving efficiencies and driving savings across business processes. Today, it’s adding up the number of companies who have gone through at least one aspect of the Data Quality program and ramping up its goals to extend the program to include quality verification of consumer-facing product attributes. That’s part of an effort to deliver outstanding consumer experiences.

The foundational elements of the Data Quality program include:

  • Processes and procedures for industries to establish and maintain accurate data of essential product attributes across categories and channels over time
  • Education and training focused on making sure those with Data Governance process responsibilities understand how to apply standards like the GS1 GTIN Management Standard regarding the unique identification of trade items in open supply chains
  • Attribute audits enabling companies to validate their Data Governance processes and institutional knowledge by routine, physical comparisons of an actual product to the most recent information shared about that product. The audit basically proves that the first two elements are working as they should within a business.

As of now, about a handful of companies have passed all three pillars of the program, but close to 50 have gone through at least one area of the program, according to Angela Fernandez, VP, retail grocery and food service at GS1 US. Some of them may have already spent years putting in place Data Governance and Data Quality programs and processes and so began with the audit to get a benchmark of their data accuracy rate, she says, hoping to understand where they already were doing well or to validate assumptions of where they need to improve, for instance.

“Quite honestly [50] is a big number since we have to go onsite and do the audit, and depending on the number of SKUs and categories the brand owner wants us to conduct, we could be there for weeks,” she says.

Better Data Equals Better Business

GS1 US points out a number of problems that can be avoided when supply chain and logistics Data Quality issues are discovered and surmounted. The use of inaccurate transactional data can cost up to 25 percent more in labor, for instance. It also says that participants have found that making seemingly small corrections to data for a product attribute – catching a package measurement that was just 1.5 pounds off, for example – could save a company $100,000 in cost avoidance in annual transportation costs. Along the same vein, a mere one-quarter-inch error in case height measurements in freight and warehouse measurement processes could lead to 1,000 fewer product cases consigned per truckload. The result of loading 20 fewer cases per pallet equals having to use six more trucks than would otherwise be necessary.

Specifically, after launching a Data Quality initiative that involved having its suppliers use GS1’s Global Data Synchronization Network (GDSN) for sharing product information, Independent Purchasing Cooperative (IPC), a SUBWAY franchisee-owned and operated purchasing cooperative, achieved accurate data for 83 major products. Eighty-nine percent of products had had data issues when comparing product data from IPC’s internal systems to that of distributor systems. It now estimates approximately a million dollars in cost avoidance in material handling and transport costs alone.

Such issues speak to the need for the National Data Quality program, and Fernandez expects continued growth.

“Some organizations are definitely doing it well and we provide them some framework to help extend and enhance their programs, and we will continue to do so as we add attributes for them to look at and validate against,” she says.

As for those just beginning to think about Data Quality issues, the program is a great place to start to think about different ways of approaching them, she says.

“The more we can work around a common framework and put nuances on it as needed, we believe companies will be successful in fulfilling their digital and physical endeavors,” she says.

The Consumer Angle

There is growing pressure from consumers that affects what’s next for GS1 US efforts. In a world where buyers shop using the web or apps on smart phones, it’s becoming more important to ensure Data Quality verifications of consumer-facing attributes.

“The food service and retail grocery communities came together to say that now they have the foundational data for moving boxes through the supply chain,” she says. “Now let’s focus on consumer data like nutrition and allergens.”

The premise GS1 is working under is that what is physically on a package a consumer buys is accurate.

“But as that data gets shared or integrated into a platform like a web site or application, there is data that may be retrieved from elsewhere than the brand owner, so it may not be accurate,” she explains.

The brand owner might have the information – say, the image of its nutritional facts panel in digital format – but for some reason just did not share it digitally with the retailer selling its product, who then grabs the picture from somewhere that doesn’t have the most recent and correct information. Or perhaps the brand owner lacks a solid Data Governance process and can’t send the consumer-facing information along right away because it needs more time to gather it internally, and the retailer partner can’t wait for it.

GS1 wants to extend the National Data Quality program so that the brand owner is always in control of the consumer-facing data. The goal is to help them:

“Pass the data to trading partners and consumers in the right manner by utilizing the most current and accurate data set inside the organization, and that when they do pass it through they share all the data elements,” Fernandez says. ”We want to make sure the completeness of the data for the consumer and trading partners are fulfilled.”

Fernandez says that programs like the Grocery Manufacturers Association’s Smart Label initiative that enables consumers to get additional details about food, beverage, household, pet, and personal care products via QR codes, web searches, apps, and other formats can be complementary to these efforts.

“They developed a list of consumer attributes, some regulated and some not, and coalesced on a format on how to display that data so it’s consistent,” she says. “It’s one seamless experience for the consumer.”

In fact, some early National Data Quality program brand owner participants who looked at their Data Governance models and conducted attribute audits found it easier to move forward with the Smart Label initiative. “They had rules and processes in place on capturing and storing data, so they only had to focus on distribution of that data per the Smart Label requirements,” she says.

Food, beverage, health and beauty, and care products comprise just one part of the plan though; GS1 also is working to understand attributes that could be important to correctly display for consumers for over-the-counter type pharmaceutical products and with apparel and general merchandise companies.

“What is important there, especially with e-commerce platforms, are things like size, color, style. Those are the things we want to get correct so that the image displays the right style of the actual item that is being sold, for example,” she says.

A pilot was conducted earlier this year around attributes these industries believe will matter to the consumer experience but development work continues, including extending attributes and validating them.

 

Photo Credit: maradon 333/Shutterstock.com

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.

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