IBM Data Science Experience

An end-to-end experience for data scientists to research, create, and collaborate. 

Data Science Experience won the Red Dot Design Award 2017 in the Platform as a Service category, and was nominated for a Webby Award in 2018 for Best Use of Machine Learning.

Problem

Current data science tools and resources are fragmented—resulting in a disjointed experience and making it difficult for both aspiring and experienced data scientists to effectively work with data.

Solution

IBM’s Data Science Experience (DSX) is an end-to-end experience that aims to unify data scientists’ workflows—allowing them to easily reference relevant and validated resources, use preferred data science tools, and add effective collaboration to their workflow.

We began devising a solution in February 2016, and this documents our work from initial research to limited release. The project is continually ongoing as we work toward our final vision. 

What is Data Science?

In order to learn about data science, we needed to understand the people, the tools, and the workflow. Together with the research team from other Analytics Platforms studios, we gathered data from over 50 data professionals to fully understand and empathize with our target audience.

Personas

We soon came to learn that the role of data scientist is very diverse and sophisticated. Based on our interviews and observations, we narrowed down the roles that different data professionals fall into and identify as. Our product aims at not just addressing the data scientist, but the other personas who work and think in similar ways.

Competitive Analysis

Next we looked at several products in related fields and asked our internal data scientists to plot each of them according to their complexity and the amount of community involvement users could partake in. We then interviewed the participants in order to validate some of our assumptions about their use of these tools:

• When is each product used in the data science process?

• What are the pros and cons of each product?

• What is it like to be a member of the community for a given product?

Data Science Workflow

Our research led us to realize we needed a clearer picture of the full data science process to target the main pain points and opportunity spaces. Utilizing quotes from the interviews and observations from the contextual inquiries, our team pieced together a flow diagram showing the cyclical process of a data science project from start to finish. This evolved into an As-Is Scenario Diagram highlighting the major pain points that came about from research synthesis.

Pain Points

We narrowed the pain points down to three key ones:

• Difficult to Find Reliable Resources: Data Scientists have to spend a lot of time combing the internet for resources—research papers, academic algorithms—and determine if they are trustworthy.

• Fragmented Experience: Current attempts to unify the data science tools available for users to choose specific platforms. There isn't one central environment that allows data scientists to use the tools and languages of their choice, and data scientists spend precious time having to learn a new tool instead of diving straight into analytics and modeling.

• Collaboration: Data scientists find it difficult to understand and pick up a project where another data scientist had left off. Also, many companies hire a small number of data scientists, making it hard for one to find a colleague for feedback.

Sketches

Working from the storyboards, we partook in several sessions of blue sky brainstorming, creating sketches around the three areas of research, experimentation, and collaboration for data scientists.

Wireframes

Our design team was then divided into three squads to dive deeper into the respective areas of data research, experimentation, and collaboration for data scientists. We converged our efforts weekly.

Prototypes & Hi-Fi Mock-Ups

The user testing informed and validated several design decisions, leading to prototypes. I built several clickable prototypes in Axure as well as interaction mockups in Adobe After Effects. These prototypes were also tested on users.

Motion Design

For the pre-login webpage, I created promotional animations using Adobe After Effects.

Promotional Video