User-Centered Design tries to optimise the product/service around how users can, want, or need to use the product/service rather than forcing the users to change their behaviour to accommodate the product/service.
We have already seen specialisation in the field that branched out the visual designer to include both both the visual and interaction designer.
We are now seeing a need for further specialisation to include a data designer, this need is driven by the increase in complexity, connectivity, and ubiquity of computational devices, it is predicted that there will be 20 billion connected devices. The underlying factor across these devices is the data, data that influences the behaviour of each of the devices and need to communicate this data.
One of the more popular examples of the value of designing with data is Nest – the smart thermostat; Nest increases conveniences and offers real value through optimising heating patterns by use of data. Without appreciation and understand of data, this would not have been possible, instead would have been approached with wireframes and an application rather than giving it some degree of autonomy.
- Companies have invested heavily in hoarding massive amounts of data or at least felt pressure that they should … but now what – there is a need to exploit these massive data stores, for nothing else that to get ROI from their investment.
- But we shouldn’t limit ourselves to ‘big data’ and focus on the opportunities presented from the convergence of a combination of technological advancements and changes in user behaviour
- (Increase in) Data exhaust
Designing with data
The following briefly explores how a data designer would integrate into the design process.
- Insight; When people interact with the digital world, they leave footprints (data exhaust), these footprints describe the behaviour of the user which can be used to infer the user’s intention and their trials of their behaviour i.e if we follow these footprints, we can use them to tell their story…
E.g. extracting a Customer Journey Map from analytics.
- Improving the user experience and new products and services.
- An example of improving the user experience is how AI/ML is integrated into Smartphone keyboard to predict the next letter/word (making typing on a Smartphone keyboard just that little bit more bearable).
- Another example of opportunity to improve the user experience is Natural Language Generation (NLG), companies like Narrative Science offer services that can generate copy from raw data – one doesn’t have to try to hard to imagine how you could deliver personalised product information for each and everyone of your users (product information is common application for NLG).
- Another, famous, example is Nest – a thermostat that alleviates the user from having to interact with the device simply by ‘observing’ and ‘learning’ the normal behaviour and preferences.
- Validating with Data; When we talk about Designing with Data, this is probably the first image that pops into one’s head thanks to the media it has gained from the likes Amazon and Google with their infamous use of A/B testing. The truth is that analytics is a great way to validate your thinking but used as a tool and be conscious of its limitations.
- For example, A/B testing can put you in a position of, what is known as, local maxima i.e. optimising a particular solution/approach but missing any major improvements i.e. optimising a button position where a button might not be the most efficient interaction.
- Supports evolution; This is an interesting concept – sometimes nature designs things better than humans. Computer Science has modelled evolution using a technique called Genetic Algorithms, these algorithms evolve based on feedback (optimisation problem) and have been used in practice to design such things as spaceships wings (through simulation). We can borrow from biology and use this approach to evolve designs such that they don’t stay static but adapt from their use.
Summary/Prediction (aka guess)
The demand for data designers will increase over the next few years, this based on:
- Creative input to make use of raw data
- Improve existing products and services using data
- Shifting interaction models (‘invisible interfaces’, ‘bots/conversational ui’, IoT, …)
- New data products
- As a tool for insight and validation i.e. new approaches for insights and validation