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Streamlining data exchange processes to keep pace with customer demands

Editor’s note: Jeff Cowan will speak on the topic “Streamlining Data Exchange Processes to Keep Pace With Customer Demands” at the MDM & Data Governance Summit in Chicago, on July 12 at 1:15 p.m., hosted by Information Management.

It’s a familiar scenario playing out at organizations across many different industries. Data quality is treated as if it is a singular, isolated project—a simple to-do list item to be checked off after a few weeks of work, never to be revisited again.

Over the past several years, Regan Van Tassel at Ecolab has been working to change this outdated perception of data management. Van Tassel advocates for ongoing data governance, not only in her own organization, but throughout the industries in which they participate, based on GS1 Standards.

The most widely-used supply chain standards in the world, GS1 Standards are used in industries such as retail grocery, foodservice, and healthcare for data consistency and clarity, as well as repeatable, universal processes that can significantly enhance efficiency between trading partners.

On July 12, join us for a presentation at the MDM & Data Governance Summit Chicago that overviews the key lessons learned from Ecolab’s data quality journey. Here are some of the most important discoveries during their GS1 Standards adoption process:

Gain executive buy-in early

From the beginning, Ecolab’s data governance program had executive support at the highest level. Their leadership team fortunately recognized that data inaccuracies can have significant negative impact on shipping and logistics, inventory management, sales and forecasting revenue—virtually everything within the modern supply chain.

Leverage data synchronization

Almost immediately after implementing GS1 Standards for product identification, Ecolab moved to publish its data in the Global Data Synchronization Network ™ (GDSN®), a network of continuously updated product information that brands and their trading partners can use to share data more efficiently, as opposed to uploading data to multiple company portals or translating spreadsheets.

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Once Ecolab collected all of the information based on the GS1-recommended format, the company has managed the quality of its data, using GDSN error reporting tools for built-in governance around product data.

Be proactive, not reactive

A good example of how Ecolab prioritizes data quality is its focus on proactivity. For instance, Van Tassel’s team built an error reporting function into its internal data systems, so they could more easily mitigate any issues before they became problems in the GDSN.

Additionally, Ecolab started looking at how many data errors there were per product, how many products had data errors, and the nature of the errors. Information was broken down by division. What made a world of difference was creating a tightly controlled system internally to assign Global Trade Item Numbers (GTINs), the product’s core identification number, and only a select group of people had access to the system.

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Analyze results continuously

Ecolab now uses one data quality scorecard that they produce and distribute a couple of times a month. It helps them track their progress and see where they’re making improvements and where more attention is needed. It’s made a big difference in terms of knowing where to target the team’s time and where they’re going to have the most impact. By integrating the various error reports, the data management team found that by fixing one issue, they could correct up to 300 errors.

Ultimately, Ecolab exemplifies what it means to embark on a data quality journey with a realistic vision, and clear definition of success. With a multi-prong approach to data quality based on the principles of the GS1 US National Data Quality Program, Ecolab has achieved an overall 68 percent improvement in data quality between 2017 and 2018. This includes reducing the number of GTINs with errors by 18.5 percent and ongoing improvements in trading partner scorecarding results.

Currently, Ecolab is implementing a product information management (PIM) system that will manage product data across divisions in a single repository, feeding into internal- and external-facing platforms for universal content consumption by customers, distributors, and internal departments globally.

The PIM system will be integrated with a digital asset management system to store images and marketing assets, replacing manual spreadsheets and databases in an effort to improve data quality and efficiency across the network.

To learn more about the use of GS1 Standards for data quality and data governance processes, visit www.gs1us.org/dataquality.

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