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Launching the first hybrid, governed data store built on an open data lakehouse architecture.

Scale your AI workloads, anywhere.

Are you one of the VIP’s that attended IBM’s 2023 THINK Conference in Orlando? Whether you attended virtually or in-person, you cannot miss IBM’s biggest announcement: watsonx.

Watsonx takes the technology behind Watson (yes, the Watson that won Jeopardy) to the next level, with even more advanced machine learning algorithms and data processing capabilities. From being able to process mounds of unstructured data, fluent in natural language processing, scalable, and user-friendly, WatsonX is taking lead in helping businesses making more accurate and informed decisions.

There are three primary capabilities within watsonx:, watson,, and watsonx.governance. Each capability offers a tailored view into generating enterprise-ready AI models, generative AI for machine learning, and improved governed data access.


UX Designer


SQL Editor on IBM's Hybrid Cloud


October 2022- August 2023

Overcoming expensive disparate data sources

The volume of data across industries is exploding. The aggregate volume of data store is targeted to grow over 250% over the next 5 years. Many Data Scientists and AI/ML Engineers have data stored in multiple locations and silos in complex forms and in poor quality. makes it possible for enterprises to scale analytics and AI with a fit-for-purpose data store, built on an open lakehouse architecture, supported by querying, governance and open data formats to access and share data. challenge

How did we get there?

Now that we’ve unveiled the power of IBM’s AI data platform, I’m here to give you an inside look on what it’s been like as a designer on the team (Hint: it’s been amazing! 😄) I would like to have said this process was linear, but some of the most profound design insights came later in the process.

Our timeline followed IBM’s Enterprise Design Thinking Framework with a focus on user outcomes, relentless innovation, and diverse empowered teams.

IBM’s loop of observing, reflecting, and making helped us understand the present and envision the future of data Lakehouse.


We began initial concepts for Lakehouse in 2021, and started implementing designs in 2022. In this case study, I will focus on the process in co-designing the watsonx

SQL Editor.

IBM Design Process

A preview of our design process

What's SQL?

Let’s break down some definitions…📚

Structured query language (SQL): is a programming language that enables users to create, read, update, and delation relational data.

Every application that you use (let's take Instagram, Tik Tok, Twitter for example) use relational databases to store information at scale. These databases store data in rows and columns in structures called tables. This data is stored, processed, and secured in engines.

Who uses SQL?

Data Scientists, Data engineers, and AI/ML Engineers, are some of the primary people that use SQL everyday to query their company’s data and drive valuable insights.

To design the most optimum experience for our users, I was commitment becoming an expert in the field. I took Harvard's SQL Computer Science course to become proficient in running and writing queries.


Walking in a data scientist's shoes 

To design the most optimum user experience, it is essential that we gain empathy and becoming SQL experts. As a result, my team took courses in Data Engineering and frequently met with IBM Engineers to ensure we understood how to use a SQL Editor our selves.

One of my favorite parts of the process was looking at other IBM Products, like Db2 and Watson Query, as a foundation for our SQL Editor. These products served as valuable building blocks to form insights on how we can enhance the existing SQL experience.

Data & AI team brainstorming at the IBM Silicon Valley Lab 

A glimpse into our prototyping iterations

One of my favorite parts of the process was looking at other IBM Products, like Db2 and Watson Query, as a foundation for our SQL Editor. These products served as valuable building blocks to form insights on how we can enhance the existing SQL experience.

Our early drafts consisted of mockups in Mural. We found that this was the best way to visualize the information architecture of the SQL Editor, and a clear way for our PM’s and Engineers to collaborate with us.

Mural mockup

Key features: 

  • Identifying key SQL components: query history, saved queries, editor capabilities 

  • Place key ideas 


  • "Users will have multiple workflows open at once. Can they alternate between Query History and Saved Queries more flexibly? 

After prioritizing different features and the overall information architecture, it was time to move to Figma! As UX Designers at IBM, we depend on the Carbon Design System.


One of my favorite parts of the design process is converting our low-fi designs to Carbon components. With our robust library, there are patterns for every user need.

Mural Mockup.png

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Mockup 2_edited.jpg

Carbon Design Mockup

Key features: 

  • Visualize features with the Carbon UI

  • Clearly explained our design decisions with PM's and developers 


  • "I love the platform's dark mode UI. How can users switch in between engines seamlessly?"

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IBM announced watsonx at the May, 2023 Think conference with extreme excitement. As a designer, I was proud to work on a product that provides build-in governance across data ecosystem, with institute ways to open data and table formats to analyze data sets. Additionally, 60% of workloads are 2.2x faster than our strongest competitors.

Designing has been one of the most formative design experiences I have had. While we were moving fast to make design process, we had Data Scientists, AI/ML Engineers, and Database administrators at the forefront of every design decision. I cannot wait to see the enterprise improvements that are made from watsonx. 

Final design.png
Design decision breakdown:


Access all data through a single point of entry with a shared metadata layer across clouds and on-premises environments.

Price optimization:

Optimize costly data warehouse workloads across multiple query engines and storage tiers, pairing the right workload with the right engine.

Query workloads with AI: leverages foundation models to simplify and accelerate the way users interact with data. Use natural language to explore, augment, and enrich data from a conversational user interface.

Launch in minutes:

Connect to storage and analytics environments in minutes and enhance trust in data with built-in governance, security, and automation.

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