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Data experience

In this case study: cross-functional workshops, problem definition, user journey mapping, how might we, ideation, validating UX concepts in usability testing sessions, synthesising insights, surveying, stakeholder management 

My role

Senior Product Designer


The client

June 2023 - present


The Product

The platform is an Operating System (OS) that brings together all of the services and products available to all 14 agencies in the company, becoming a daily destination for its 114k employees in internal teams and their clients. 

This OS helps maximize the company’s internal know-how. The number 1 goal is to unify the multitudes of ways agencies work and streamline their collaboration, especially when jointly delivering on client projects. The platform offers shared core functionality such as: authentication, administration, project management, product market place, developer hub, data catalog. An advanced design system powers it all, unlocking tenant theming and enabling agency teams to build their products. 

To support daily business needs such as pitching, running campaigns, collaborating with clients, the platform offers a project management solution, with products/applications that can be added to the workflow and run directly inside the OS. It lets users add team members, due dates, tasks, workflow templates etc. Here employees and clients collaborate and coordinate in fully customized and themed environments.

In this case study, we’ll focus on the data catalog, where data scientists can find answers to their clients problems by discovering data available across the company and from providers, getting access and learning about how it’s used by  interacting with colleagues through data communities.

The Problem

The current data experience is problematic for many reasons.

  1. Data resources in the company are distributed across agencies, teams, and individual colleagues, resulting in poor visibility into available resources. 

  2. A competitive spirit and lack of channels between agencies leads to poor collaboration.

  3. Lack of best practices leads to networking and personal knowledge being prioritized instead of process. Communities could help get support and knowledge sharing 

  4. Access approval and provisioning process happens manually and is not transparent. Users don’t understand how to get access to data and when they do the approval process turns out to be lengthy.

  5. Currently, a 3rd party solution is used for data cataloging and management. This poses some problems: little or no room for customization; a tool that’s not part of the OS results in a broken experience; users struggle to find it unless they’ve been told about it. 

  6. To address these problems agencies have been building their own proprietary products.

The Goal

Design a central solution which would cater to the needs of data teams across agencies, improving the discovery, access approval, and knowledge sharing experiences.

My Responsibilities

  • Conducted extensive research, crafted user journey maps and service blueprints. 

  • Formulated key problem statements using How Might We's 

  • Validated solutions through usability testing.

  • Structured the Information Architecture and user flows

  • Created a clickable prototype illustrating the UX Concept

  • Facilitated workshops and ideation sessions with stakeholders from multiple agencies. 

  • Collaborated closely with product teams that developed similar solutions. 

  • Regularly communicated progress, research findings, and future steps to C-Level stakeholders.

  • Prepared and delivered demo presentations to the wider company and gathered feedback through surveys.



  1. Discovery

  2. Competitor analysis

  3. User journey map

  4. Service blueprint 

  5. General structure/information architecture

  6. UX design concept prototype

  7. Usability testing, insight synthesis and analysis, formulate HMWs

  8. Present to C-Level stakeholders

  9. Ideation with stakeholders and users

  10. Iteration on the UX design concept prototype

  11. Demo video and feedback 

WPP Data Catalog - Process v3 (1).png

Step 1: Discovery

In January 2023 my team and I did an extensive discovery phase. We ran 25+ interviews with data users, which helped identify pain points and opportunities. We spoke to data producers and consumers, including data scientists and analysts, data strategists and managers, product managers and owners.

Step 2: Competitor analysis

Next, taking the initial research findings into consideration, I reviewed the existing solution. Users were mainly complaining about an inefficient search, lack of a transparent access request process, and an outdated UI. Some users didn’t even know the product existed and was being used across the company. Others were saying they thought it was long abandoned and out of use. Clearly, the data experience had to ultimately become part of the central OS.

Also, I reviewed the proprietary products that agencies were building internally to solve the problems which most affected their business, e.g. one agency built access provisioning software, while another agency built a proprietary data catalog for one of their clients. I connected with these teams so that I could learn about these solutions and the problems they were solving, to make sure the new solution would solve them too. 

Aside from this, I reviewed popular data catalogs such as Kaggle, Datarade, Snowflake Marketplace and others to see how they were solving the discovery, access and community pieces.

Step 3: User journey

Aside from the standard swimlanes on the user journey map (steps, actions, emotions, needs, pains, opportunities) I usually add a couple very important swimlanes that give more context: 

  • Quotes. Here I paste direct quotes from user sessions in correspondence to each step. This allows anyone who’s reading the user journey map to step into the user’s shoes, promoting empathy. 

  • How Might We’s. In this swimlane, I try to distill the actual design problem, by piecing together the information from other swimlanes. 

  • Ideation. This is where I paste solution ideas as they come to me throughout the design process (which as much as I’d like it to be, isn’t always linear) or in ideation sessions.

Step 4: Information architecture

To show where the data experience would sit on the platform among other services, I put together a very high level general sitemap. This exercise helped navigate the conversations with stakeholders who had conflicting opinions.

WPP Data Catalog - Data IA (2).png

Step 6: UX Concept

The purpose of creating a UX Concept at this stage is to create a common understanding of the potential solution, facilitate stakeholder discussions and decision-making and enable usability testing. It consists of a prototype that illustrates the most common user flow. This flow focused on data discovery, requesting access to data resources and interacting with the AI Assistant. 

Generally, in early stages, it’s good for the concept to be a lo-fi black & white prototype, so that UI design details don’t get in the way of testing the essentials. In my case, the design system allowed for quick prototyping, so the concept ended up looking like the final polished design.

Step 7: Testing, insights, user journey updates, HMWs, presentation

In sessions with 7 data scientists we looked at the existing solution and validated the new UX concept. I asked users to think out loud as they completed prompt-based tasks around searching, filtering, browsing and viewing dataset details. 


Main insights from sessions were: 

  1. Users don’t know what exactly they’re looking for when they come to Data Catalog – they have a problem, but they don't know how to solve it, so they’re most likely to browse instead of searching or filtering. 

  2. Users want to see what resources are readily available to them and their agency. They want a more transparent access flow. 

  3. Users want to understand how a particular dataset has been used before and reach out to experts to ask questions.


After the usability testing sessions, I updated the user journey map with more quotes, needs, opportunities and ideas. After all, this is a living document that is never done and becomes richer and fuller as we learn more about the problem. 


Finally, I defined three big How Might We’s which covered most of the user journey:

  1. create a meaningful discovery experience for users that browse and search, but also users that rarely search?

  1. create a seamless access approval and provisioning flow?

  2. connect users to knowledge and experts and provide necessary support?

Step 8: Ideation sessions with stakeholders

I presented the research findings to stakeholders and facilitated an ideation session to address one of the HMWs.


Step 9: Service blueprint

As more actors come into the picture, a user journey stops being enough. So for the access approval and provisioning flow I created a service blueprint. Here I showed the actions of the user (dataset requestor) and the respective events happening in the background, either on the technology side or actions of other users (approvers, managers, data owners, legal and tech team).

WPP OPEN Data - [UX] Access Management blueprint - Requesting dataset (1).jpg

Step 10: UX Design Concept update

I updated the concept with the new flow which would allow dataset owners to build custom access approval and provisioning workflows. It would also allow data owners and other approvers to approve/reject incoming access requests.

Step 11: Demo and feedback

Throughout the process, I made sure to keep all the stakeholders updated and aware of the progress, and as involved as they had to be.  


To get more feedback from the wider company, I recorded a screencast demoing the concept. We published the video on the platform homepage so that everyone in the company could see and comment. This was a great way of getting feedback and we still keep getting comments!



User need is a verb not a noun: users don’t need another data catalog, they need to find data solutions to client problems. A search box is not going to help them – they don’t know exactly what they’re looking for. The means to finding the answer could be: browsing, searching, filtering, asking a colleague, asking the AI Assistant, stumbling across a case study that talks about how a particular client problem was solved, seeing a post that mentions a particular dataset in the community discussions, etc. It could be one or a combination of these actions.


In order to have a better picture of the user’s context and journey, we must ask these questions:

  • What knowledge does the user come with at the beginning of the experience? What events take place before the journey starts? 

  • What is happening as the user is using the product? What is happening  when the user isn’t using the product? 

  • What will happen afterwards? What will change for the user when the experience ends? 


Understanding this helps us avoid rushing to conclusions and making the wrong design decisions.

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