Data Driven Design

Data driven design uses data to improve design and support decision making. More and more data gets produced every day and being able to put it to use has great advantages. It can be useful for customers (informing them on how to improve usage of their system for example), for service (do we need to change that bearing soon?), and for development (what features are never used?

Examples of things you can measure are: machine run hours, failures, down time, user interaction, user preferences, usage, wear & tear, power consumption, etc. You can measure during DevOps (proto/pilot, user panel testing, endurance tests, auto-install/commissioning, manufacturing issues, etc.), or during usage/service (questionnaires, data retrieving via sensors, video recording, etc,).

Depending on where data is produced, we need to collect and transport it to central repositories so it can be accessed and used. Besides the technical challenges (e.g. moving sensor, inside radioactive area, etc.), there are also challenges like security, fire walls, privacy rules, network capacity, etc. Even more so, when collecting large volumes and perhaps sensitive data (e.g. radar output of a warship).

The data collected needs to be prepared so it can be used to analyse and make decisions on what to do with it. Preparation is for example: filtering, ordering, merging, visualizing, etc. Data analysis is trying to make sense of the data provided. Decisions may involve: stopping, proceeding, altering a design/feature, change user interface, increase quality, etc. To start collecting data, it’s important to start with the end in mind: what’s the hypothesis? What are you trying to prove/disprove? This can vary from: a user prefers this over that, or, actual wear data to decide on maintenance schedule.

You can distinguish 3 levels: data driven, data informed, data aware. Data-driven design implies that the data that is collected determines (in other words, drives) design decisions. data-informed design, where a team takes data as only one input into their decision-making process. data-aware design where decisions need to be taken not just from data but back to data collection practices.