EveryDay Warehouse
EveryDay Warehouse is a national big-box retail chain focused on affordable household essentials, home improvement goods, electronics, groceries, furniture, seasonal products, and bulk savings.
Overview
Everyday Warehouse operates through a centralized merchandising and supply-chain system designed to evaluate, onboard, distribute, and manage products across hundreds of retail locations and regional fulfillment centers.
During the item onboarding step, vendors upload prospective item information into our system. Inevitably, some items might be missing required attributes or contain incorrect data. Prior to the Data Quality project, our end users (DQ analysts) had to manually validate and compile these errors into spreadsheets and send them to vendors via email for correction. This process proved to be time consuming, inefficient, and prone to vendor's confusion and neglect.
The Data Quality project will be a one-stop-shop internal application that would enable our users to quickly validate potential data errors and send them to vendors for rectification. In this case study, we will focus on the initial phase of discovery and mid fidelity wireframes for the MVP scope.
My Role
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Created mid & high fidelity wireframes and presented them to business stakeholders, product manager, and end users to gather feedback and reiterate on my design solutions
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Delivered business-approved final design to the engineer team
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Conducted User Acceptance Testing post-launch to ensure that engineering development matched design hand-off specifications
The Process
I was onboarded to the DQ Project one quarter after its conception. My first step in the discovery process was to begin attending the weekly meetings with the cross functional teams and several end users.
Through the initial conversations, I had learned that the engineering team had created a backend framework that would flag potential errors in existing and new items based on a set of algorithms. Our immediate next step was to create an MVP that targets the capability to display records of potential errors to users and enable error validation.
User Flow
Based on the knowledge I'd gained so far, I proceeded to put together a user flow to help visualize the new process for error validation
Wireframes
I designed the wireframes in mid fidelity and presented them to the teams in several rounds of design review. These are some key takeaways worth noting during this process:
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In the first iteration, every row of record had a call-to-action button to flag it as a false positive or real error. Users indicated that they prefer to review records in bulk (typically by Vendor and Error type), so I proceeded to add the ability to mass select records via check boxes
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To avoid inundating vendors with multiple emails reminding them to correct their data as records are flagged on the daily, our team opted for the system to automatically send one email per week to each vendor with their bucketed item data issues. The functionality to manually send "Real Errors" to vendors was thus removed at this stage.
The Next Steps
Once the wireframes were finalized in high fidelity and ready for development, the next opportunity for the project would be explore the functionality to run ad-hoc data quality check on a specific subset of items for sales and seasonal events.