The need for Data Designers and their role in the design process

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.

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Rise of the middleman economy – Conversational UI’s, Bots, and ChatBots

Some ramblings about Conversational UI’s, Bots, and ChatBots.

At present there is a lot of attention on Conversational User Interfaces, Bots, and ChatBots – especially interesting/exciting for those who are interested (design and build) in how people interact with computers.

To reduce ambiguity it’s worth distinguishing between ‘Chat’ and ‘Bot’. Here I consider Chat as the interaction model whose interface is predominantly through natural conversation, the medium  is the Conversational User Interface (Conversational UI  or CUI for short).
A Bot is a agent (software application/service) that can carry out a task (semi-)autonomously on behalf of the user. Therefore the ChatBot is an Bot who interfaces with the user via conversation but achieves some task autonomously.

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Leverage Context For Better Recommendations

Recommendation Engines has become the ‘hello world’ of Data Products (or more generally the data era). Popularised by Amazon that not only found them to be more attractive (user engagement) that curated reviews and also figured out how to make it work at scale and in (near) real-time (using a technique known as item based collaborative filtering).

Once then, every digital commerce site has leveraged the idea and every Machine Learning/Data Mining book reviews the idea and implementation.

At a high, and very simplistic, level, it works by finding the distance between two entities (either people or items e.g. restaurants/food) based on a set of features (e.g. cuisine, food, song/movie genre, song artist/movie director/etc) and using your (or similar person’s) history of entities you’ve previously engagement with (bought, visited, etc) predicts what other entities you would like e.g. if 90% of your iTunes library is Jazz then other Jazz songs will have a higher weight than Rock, thus you will be recommended Jazz songs.

There are times when recommendations need something more than history of engagement, something more timely. I’m sure we have all experienced this, it’s your wives birthday and you shop on Amazon to find your recommendations have been embarrassingly polluted with items you would rather your workmates not see. One suggested improvements for recommendation engines is to use context (if possible). Google does this well (advantage of having established a strong presence of lifestyle and productivity products) e.g. if you’re looking for flights then Google Now will use this derived intent to keep you up-to-date with the latest flight deals.

But this can be achieved by other means, and the example I have in mind, we can leverage the mobiles attributes of being connected, aware, and present to determine if the recommendation is for a individual or group of friends e.g. your out with friends, using your phone to look for somewhere to eat – the phone has your contacts, location, awareness of who you’re with (neglecting privacy in this instance) – instead of using just your recommendations, it should extend the preference out to those in close proximity. It’s not hard to see how this extends to going to the movies, something to do, or music to play.

The rise of Data Designers

It’s a little ironic that I wrote this post while procrastinating doing the assignment for my data analytics course.

… we’re at the break of the most exciting era of computing, the convergence of pervasive computing, our increased ‘dependency’, and data (capturing and handling) will diminish the concept of a computer as a box (keyboard, mouse, and monitor) and transfer it into more of a living ‘thing’ (or service).

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The rise of the agents

A Software Agents is a autonomous software program than acts on behalf of the user with no or minimal interaction. The concept has been around for sometime but will become ever so important as we move into a truly programmable world and our digital footprint/dependency reaches tipping point (for some it already has for those checking their phones 155 times per day).

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What if bottle caps could talk

Having been playing around with ‘connecting’ things a fair bit these days, you find yourself asking the question ‘what could we do if this thing could talk’ for a lot of objects you interact with. Here is one simple example.

Even though our little man is 3 in November, I still find myself verging on paranoid when it comes to administrating medicine – the amounts and frequency.

So ‘what could we do if the cap could talk’; one idea I have is broadcasting the cap has been opened with an receiving app logging the time; so you know exactly when you can need give your little one more meds. Of course you could have the time logged on a LCD display but having it as simple as broadcasting via GATT (Bluetooth LE) it could be at an acceptable price point and mean you’re not having to swap batteries out every other day.

Hydrate (hacking the watercooler)

Around 2008 I build a little app for my wife to help her track how much water she drank; it was mildly successful but failed probably because of the effort outweighed the feedback (reward). One plausible solution to this could be to ‘gamifying’ it (essentially adding elements of competitiveness, social commitment, and a stronger variable reward for using the app – think Nike+) to help establish a habit. … but this is 2014 – computers should disappear where possible freeing the user to concentrate on living rather than staring at screen i.e. Context-Aware/Anticipating Computing. Which brings me to Hydrate, the prototype that automatically tracks users consumption of water and nudges them if they have gone too long without sipping on a little bit of water.

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