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.
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.
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).
In this document we briefly explore the what, why, and how of sentiment analysis – let’s jump straight into it.
So what is Sentiment Analysis?
I would be surprised if you haven’t already come across the term and possibly know/use it already. It has become increasingly popular with the advert of big data in context of social networks and is a tool frequently used by brands to monitor and measure ‘chatter’ about themselves and/or market.
Essentially it offers a way to extract and measure the opinion of this chatter (normally filtered by a specific topic or on a channel) e.g. if you wanted to know the general opinion of your brand you could analyse all the tweets that link to your brand somehow, tallying the general opinion for each and reporting the result.
For a more detailed explanation, check out Wikipedia