This online ordering platform for medical equipment and supplies was hampered by a homegrown CMS platform that was overextended to manage product, supplier, and facility user data. The business had reached the end of its ability to scale that tool and asked Earley Information Science to assist.
Their data model based on their initial product offering created SKU proliferation, system constraints, process inefficiencies, and inconsistent data as new product types are onboarded. Time-to-market was slowing, products required too many last-mile escalations to launch on time, and the processes to manage their product data were broken.
Finally, complex relationships between data objects were unclear, requiring deep knowledge of the CMS system code to debug, posing burdens for catalog maintenance and growth. The business needed a more robust platform to handle this complex load.
The initial data model wasn't built with scalability in mind requiring internal acts of heroics to maintain the product assortment. The cost of managing their product data was skyrocketing, and growth was stagnant.
EIS worked with the business to improve the data model, encouraging data reuse while implementing best practices for managing the system, the assortment, the data model, the user experience, and the data flow through the system.
By applying proper navigation hierarchy design choices, EIS also improved findability and SEO by ensuring that the products were visible to both search and web navigation.
Finally, EIS built the governance around future web hierarchy design, data collection, metadata and content collection, and system evolution to ensure the system stayed scalable and flexible to M&A opportunities, assortment expansion, and new channel opportunities.
Navigation taxonomy testing identified strengths and opportunities for redesign to improve customer experience. This also allowed for overall better data quality and speed to market, increasing revenues.
Change Management Plan, RACI matrix, and process mapping established a framework for data governance to streamline and optimize product data management operations.
Overall, product data operations became more efficient, achieved better data quality scores, and had more scalable methodology and processes to continue to see value in the future.