Cornerstone Platform is an internal tool, which empowering a different types of Amex users with access to data that is relevant to their job from one consistent and trusted source (system of record, aka SOR). Users landscape: executives, marketers, data analysts, risk managers, decision scientists, and data engineers.
My Role: From March 2017-August 2017, I worked in Global Information Management department under Risk Information Business Unit in American Express, leading end-to-end UX/UI design.
Achievements: Increase efficiency and reduce operational cost.
I lead and conduct a UX survey to collect quantitative data, a.g. Who are the main users? We then identify the main users' types and opportunities to improve user experience. I visualize the raw result into this elegant and easy to read one-page data summary. I share this design with product owners and they further present it to cross-functional team.
Visualize data and user benefits
Based on user research, then I analyze quantitative data to create user profiles, I also work with the business side to tie the users' paint points into business needs. Below are examples of user benefits illustration I create for Vice President of Cornerstone. The designs were well received and then broadly shared with C-Levels such as Executive Vice President of Amex.
A Case Study-Design Metadata upload UX
There is no way for data engineers to review the quality of metadata, neither see guidance on how to improve metadata description quality. NLP will integrated with metadata template, and provide a score to guide users on the next step. What specifically determines the NLQ score? -Relevancy, Grammar, Volume and Duplication are the parameters for determining the score.
This is the first feature I lead for the big data platform (Amex Cornerstone), and one of the challenges is how to communicate the design process to stakeholders, who are the director of data quality, the product manager, and the tech lead. To solve the first challenge, I create this one-page plan, which conveys multiple metrics, such as timeline, deliverables, problem canvas, and checkpoints to multiple stakeholders.
The design challenges are "How to utilize all existing functionality, and make them work cohesively?" "How to let Data Ingestion team know how to register metadata at first glance?","How to give Data Ingestion team an easy way to review metadata description score?" Instead of designing the solution directly, I would usually create a holistic view of the user landscape to easily identify gaps and business opportunities. I also further interview the actual users, and create the data engineer persona to consolidate the qualitative data and opportunities
With a deeper understanding of users pain points and business needs, I create a vision and make sure everyone is on board of the themes and the first minimum viable experience for immediate launch. At the point of arrival, users should be able to review a history of the uploaded file. They should be able to track the request status and should have options to close/archive/re-use request.
For the immediate need, we are going to build the fundamental features: uploading metadata description and reviewing reports. I ask these questions to make sure the flow is intuitive for data engineers: What are the key steps to get a report? How to share users the knowledge on improving data quality effectively? Does a low score block the end users from moving forward to the next step? With a clear understanding, then I create hi-fidelity wireframes to present solutions. The design gained positive feedback from product owners. "The UI is very pleasant and easy to use" - Director, Data Quality, Guru Ramasamy
For post-MVP, we propose to build advance features such as 1. Track history of uploaded metadata. 2. Enhance edit features, such as overwrite individual field or feed etc. 3. Create a guidance with practice.
Conceptual Design for the Next Version of Amex Big Data platform
Besides designing for the ongoing train, I also collaborate with another UX designer to create a new vision of the next version of the big data platforms.
For the Phase 1, we focus on two types of users, analytical users, and data engineers. We conduct ethnographic research to further understand users expectation. We ran 16+ interviews with members from each persona across a range of teams, roles and initiatives. I visualized the existing user journeys focusing on tools, workflows, partners, and pain-points.
I sketch out concepts for two themes: 1: Getting Set Up & Informed- What is Cornerstone? How does it set me up for success? 2: Finding & Trusting Data- Where is my data and can I trust it to do my job?
I create multiple mockups of onboarding experience and search experience, and then share the concepts with PMs to iterate them.