Visualising Facebook: Your social data and personal infographics



Like (Photo credit: afagen)

The more we contribute, communicate share and talk online the more we leave a trail of personal data in our tracks. This may be data about what we say to whom on Twitter, when we are most active or the photos we take. Or it may be data that we have captured from a specific activity - data on every run I have done in the last two years is stored by Runkeeper, for example. To have such a constantly growing, structured personal data set is very new and it offers real opportunities for brands and platforms. But also for individuals themselves.

The quantity and depth of data that we are structuring about our lives even on one network comes as a surprise to many people. Taking Facebook as an example - the data we create about ourselves and our networks is vast, and often hidden from the consumer - you just can’t imagine what it might be. The first step to help you understand the amount of data you have stored and how it might be useful is to visualise it - and search engine Wolfram Alpha have now produced a report that takes this information and presents it back to you.

For any user what you uncover about yourself, what Facebook knows about you, is interesting. For example, the word I have used most frequently on Facebook is ‘run’. The peak time for me to upload photos is apparently 9pm on a Saturday. And the most common first name and surname among my friends is ‘James’.

But what is more interesting to start to explore is how this Facebook data is able to understand data better than we might be able to. Take how it clusters my friends. Just looking at connections (and their connections) you can start to map out how my friends group themselves and really start to understand something about me.

Friend Network: Matt Rhodes

You can see three clear groups:

  1. A tight cluster of yellow connections - people who are all interconnected and clearly all know each other. These are people I’ve been friends with since University.
  2. A relatively tight cluster of blue connections - less interconnected but the groups of people I’ve made friends with in 10 years in London.
  3. A more spread our cluster of green connections - a loosely connected set of people that I have worked with.

There are also the odd random connection that I have seemed to pick up along the way.

So Facebook can accurately and clearly summarise my friendships and how they interact. And you could probably make inferences from that about how likely I am to mix people across these groups - only a small number of people connect between the clusters, suggesting I am more likely to socialise in these groups separately (which to be honest I am).

There a lot of data out there, data that we are leaving in our wake with every social interaction. Currently this data is being used by the platforms and by brands, but the exciting opportunity is to see how individuals can take more ownership of their own data and get more value from it. The first step is to start to understand what data there is out there and how it is structured. The Wolfram Alpha Facebook reports make an important first step to revealing this.

WalmartLabs - taking Big Data into retail


Walmart Labs

Walmart, the world’s largest retailer, acquired social media firm Kosmix just over a year ago, creating @WalmartLabs, with the intention to use this specialist R&D unit to define the future of commerce by merging social, mobile and retail.

So far WalmartLabs has released two interesting developments using social:

ShopyCat gift recommendation engine Walmart Labs• ShopyCat - the gift recommendation engine

This Facebook application uses your Facebook profile to suggest suitable products for you, based on the interests and hobbies of your friends. An interesting aspect of this approach is that the app will offer links to other retailers if Walmart do not stock a suggested item in their own stores.

The notion that the app may steer customers away from Walmart may seem unusual, but the brand sees more long-term gain in making the service as useful and relevant as possible to its customers.

• Get on the shelf - innovative product pitching

‘Get on the shelf’ was a contest that allowed innovators to pitch their products to Walmart customers, who then voted for the ones they would like to see Walmart stock.

Over a million votes were cast, narrowing the field down to three products that will now be available to purchase in Walmart: a DIY-screw replacement system for glasses; an airtight plate cover for food storage; and the overall winner - a socially conscious bottled water whose company donates its profits to provide clean water supplies.

The next step - Big Data

These examples are innovative approaches to using social media to encourage sales and generation of inventory, but the area that I think will prove the most fascinating is how WalmartLabs will leverage “Big Data” to develop the retailer’s ability to predict market demand and so optimise their supply.

Understanding and fulfilling local demand

This is where the situation becomes truly interesting - stores will be able to optimise their inventory according to their area’s specific tastes and seasonal demands.

One of the examples WalmartLabs’ Venky Harinarayan offers is that of college football. By monitoring social media buzz during college football season, Walmart is able to determine when discussion about college football in a certain locality is beginning to heat up. This lets them know when they should be stocking products that are related to the season and local teams.

Creating demand and making recommendations

As ShopyCat has demonstrated, recommendation engines enable customers to discover new and relevant products, either for themselves or their friends. As I mentioned above, ShopyCat currently directs customers to alternative suppliers, but from understanding customer behaviour and using Big Data, a logical evolution would be for these alternatives to become increasingly niche as Walmart develops supply according to consumer taste.

The ability to bring all of these channels together in-store via mobile will be significant. WalmartLabs are developing in-store navigation using mobile, so I would expect to see apps that offer customers information and the location of recommended items, or prompts for items of interest that are already in close proximity. A reminder of a friend’s upcoming birthday and interest in fishing, while you are passing the sports section, for example, would help you make a relevant purchase while saving time and hassle.