Data Driven Design

The practice of using scientific methodology to collect data, that can in turn, guide decision making.

Where to Start

There are four goals to data driven design:

  1. Understand the domain

  2. Identify the decisions users make

  3. Decompose the decisions into decision and design requirements

  4. Design solutions that support the complexity of the work environment

I am not going to lie and tell you that Data Driven Design is some proprietary, super special, secret sauce. But it can change the world. And it can change the way your user's experience your product or service. The unique combination of methodologies and practices from Psychology, Human Factors, Marketing, and Product Development offer a way to underpin decisions and strategy with real data. It's the rigor and power of the data that makes Data Driven Design truly special.



Ok, Tell Me a Little More...

To ensure the soundness of the development and deployment of products, services, or business strategies, I use a Data Driven Design approach which is grounded most heavily in Human Factors and Cognitive Systems Engineering. Cognitive Systems Engineering is a multifaceted approach directed toward creating solutions to complex problems, while Data Driven Design (DDD) focuses on how people make decisions in naturalistic environments.

The DDD method focuses on supporting the complex behaviors and decisions end users make by understanding the challenges and circumstances under which they occur. If a system is designed for complex issues and unanticipated events, habitual tasks are captured along the way. Therefore, the DDD supports user decision-making and user behavior across a multitude of circumstances. For example, if a system is designed for tough use-cases, by default it will capture routine and everyday use. Furthermore, the system will support decisions and behaviors made by a wide range of end-user types (e.g. novice to expert).

The process of practicing DDD leverages existing scientific methodology such as ethnography, cognitive tasks analysis, experimental psychology, and marketing research throughout the product development cycle. This approach is therefore self correcting, as the multiple data sets provides multiple opportunities to vet your design and approach. And the approach can be applied to just about anything, designing systems, products, services, organizations, or multidisciplinary solutions.

Knowing how to write good research questions, how to collect, analyze and represent your data is where my unique experience comes into play. And one of the strongest benefits of DDD is the approach can be applied to product development, service design, strategy development, and more. If you are interested in learning more about the Data Driven Design approach please contact me!