The Partnership Between Designers and Data Scientists

From Experience Design in the Machine Learning Era, Fabien Girardin shares that with a behavioral data-driven experiences, we exploit thick data, the qualitative information that provides insights on people’s lives, big data from the aggregated behavioral data of millions of people and the small data that each individual generates.

Traditionally, designers focus on defining the experience of the service, feature or product. They nest the concept within the larger ecosystem that relates to it. Data scientists develop the algorithms that support that experience and measure it with multivariate testing.

Often, there is a lack of shared understanding of each other’s practice and objectives. For instance, designers transform a context into a form of experience. Data scientists transform a context with data and models into knowledge. Designers often adopt a path that adapts to a changing context and new appreciations. Data scientists employ processes like human-centered design but are more mechanical and less organic. They strictly follow the scientific methods with its cyclical processes of constant refinement.

A properly formulated research question helps define the hypothesis and the types of models to develop. The models are the algorithms that get evaluated before they are deployed to production into a “data engine”. Whenever the experience supported by the data engine does not perform as expected, the problem needs to be reformulated to continue the cyclical process of constant refinement.

The scientific method is like any design approach that forms and makes new appreciations as new iterations are necessary. Yet, it is not an open-ended process. It has a clear start and end but no definite timeline.

Designers and data scientists must immerse themselves in the other’s practice to build a common rhythm. Fabien Girardin identified these touchpoints for designers and data scientists to produce a meaningful user experience powered by algorithms:

  1. Co-create a tangible vision of the experience and solution with priorities, goals and scope.
  2. Assess any assumption with insights from quantitative exploration, desk research and field research.
  3. Articulate the key questions from the vision and the research. Is the team asking the right questions and are the answers algorithms could give actionable?
  4. Understand all the limitations of the data model that gives answers.
  5. Specify the success metrics for a desirable experience and define them before the release of a test. The validation phase acts as stopping point and it must be defined as part of the objectives of the project (example: improve the recall of the recommendations by 5%, detect 85% of customer who are about to default).
  6. Evaluate the impact of the data engine on the user experience. It is particularly hard for data scientists to work “offline” on an algorithm and measure improvements that will correlate with improvements in the actual user experience.

This intertwined collaboration illustrates a new type of design. In a recent article Harry West CEO at frog suggested the term ‘design of system behavior’:

“Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systems” — Harry West

Living experiences emerge at the crossroad of data science and design. An indispensable first step is for designers and data scientists is to establish a tangible vision and its outcomes –  experiences, solutions, priorities, goals, scope and awareness of feasibility.