My wife and I recently visited a local store to buy some end-of-season patio furniture. This particular retailer scoops up end-of-season items from home improvement stores. We found a couch in a deep “chili” red with two matching chairs save the slipcovers. Not able to turn down the deal, we purchased the set and ordered replacement slipcovers online from a home improvement retailer that also carries the patio set.
But when the covers arrived, they appeared brighter and didn’t match the couch. The color on the packing slip was listed as “dragonfruit,” but the SKU was that of the chili covers. Upon further examination, we noticed the SKUs on the product packaging, packing slip, and online listing were all different. So off we went to the store to return these overly-bright red covers. Once there, the in-store system didn’t recognize the SKU. Apparently, dragonfruit was not a color that existed in their catalog. My mismatched patio set would indicate otherwise.
After much discussion surrounding the mismatched SKUs and documentation, customer service finally overrode the system, authorized the return, and expeditiously processed a new set of chili covers to be rush delivered to our door. Two days later, we were once again the not-so-proud owners of brightly-colored dragonfruit slipcovers. (It was clear to me that they had a master data problem. However, the customer service representation with whom I spoke regarding the newest errant shipment had little interest in my explanation of the data governance breakdown within their supply chain. It’s hard to believe that others aren’t as excited about master data mysteries as I am!)
Would the third time be the charm? After returning the second and receiving my third luxurious set of dragonfruit covers, I began to wonder if I might get the correct color in time for next summer’s prime patio season. So, I opted instead to order from a different online retailer, who I might add, also carried the chili ones I so eagerly coveted. Two days later, my box arrived carrying two slipcovers in the brightest-of-possible dragonfruit reds. Our furniture, we settled, would forever be delightfully mismatched.
Insufficient data governance can lead to many issues, including unnecessary returns that drain supply chain resources. But overwhelmingly, the biggest impact is a hit to customer retention.
Stories like this happen every day as customers order furnishings, replacement parts, or even items they’ve used for years. Why does it happen?
When items that are sold through one channel or store don’t match the item numbers in another, inaccurate order fulfillment can lead to great customer dissatisfaction. This customer service pain happens when companies have unaddressed, underlying supply chain issues. At Chainalytics, we see this manifest when one or more of the following issues occur:
- Misalignment of products across various selling platforms. Often issues arise when item numbers across online and in-store channels don’t match. This can also occur when distribution and fulfillment operations are not aligned.
- Fulfillment process breakdown. Sometimes pickers in a warehouse rely solely on bin locations rather than product numbers to fulfill an order. Scanning product packaging as part of fulfillment can signal discrepancies between the packing list and product packaging before the item is shipped.
- Incorrect new product setup. When the product development team sets up new products, they create master item attributes. But sometimes, these attributes are not synchronized across online, fulfillment, and sales systems. Separate company divisions, channels, and sales regions often add to the complexity. When each group makes product introduction decisions without a consistent methodology for data governance, then financial, sales, and supply chain groups can be negatively impacted.
- Lack of data governance. When companies have separate divisions for eCommerce and in-store sales, there is often a transparent lack of product SKU compatibility. Have you ever tried to return jeans you purchased from a retailer online to one of their brick-and-mortar locations? You will often need a receipt to provide proof of purchase as item tags, packaging, and in-store prices will not match.
Insufficient data governance can lead to many issues, including unnecessary returns that drain supply chain resources. But overwhelmingly, the biggest impact is a hit to customer retention. Many customers can forgive mistakes made on occasion, but few will forgive the same mistake made numerous times. Without proper data governance, it is nearly impossible to eliminate these issues.
To improve customer satisfaction and reduce the unnecessary burden on your supply chain, companies need a holistic data governance process that includes:
- A dedicated data governance team. Different divisions within a company have unique product and customer nuances. A successful corporate-wide framework for data management and governance will require you to receive input from stakeholders outside of IT. To create a framework where data has context and can be used properly, companies will need a dedicated team that can represent the corresponding organizational structure.
- An information lifecycle snapshot. Companies first need a baseline to establish information maturity and identify key issues plaguing data inconsistency. Initially, highlight issues that have a significant impact on your ability to meet your customer demands. To get a clear understanding, create a map of each system and how its unique data interacts with other systems. This “day in the life” snapshot will define how data is collected, handled, processed, and maintained at each state of its lifecycle — identifying disconnects at each systems’ touchpoints, and allowing companies to build an approach to mitigate the impact.
- Use of DMAIC approach to incorporate technology. Data governance technology is critical to maintain data integrity as products, divisions, and customers evolve. Beyond creating a metadata repository, companies need tools that create visibility and structure flexible information workflows for data mastering. Enabling tools from vendors such as Informatica and SAP can help create a central, master data model that reconciles data across disparate systems. These platforms allow companies to centrally manage and catalog data, enforce consistency and improve overall data quality. Using a DMAIC approach that defines, measures, analyzes, and improves your governance as you incorporate these tools will allow you to address not only your metadata issues but also eliminate future data consistency issues.
If you are wondering if you might have a data governance issue, try becoming your own customer. Having first-hand experience of the service issues your customers face can pinpoint the most significant data governance issues. Reviewing customer service complaints or IT backlog requests can also highlight the areas that need immediate attention. This is where we can help. Our extensive industry knowledge can help you to establish a centralized data governance program that includes data quality, master data management, security, and information lifecycle management.
My tales of these dragonfruit slipcovers have become master data legend among my team, routinely re-told when I have events at my house. At least someone is excited to see them!
Kirk Waldrop is Vice President of the Supply Chain Operations practice at Chainalytics where he is responsible for leading engagements related to logistics and operations strategy, facility design and optimization, 3PL advisory and selection, warehouse technology advisory and selection, order-to-cash, procure-to-pay, customer segmentation, and transformation planning and implementation.
