data-driven london week

view of the Shard from ShoreditchMost mornings this week, I awoke in the mystical land of Hackney, and battled hordes of hipster-cyclists to make my way to the Google Campus – a refuge of data-folk. At least, that’s how I like to remember it.

As I blogged last week, several ScraperWikians attended and spoke at a range of events, all put on to the tune of “Big Data.” I spent Monday evening with a friendly meetup group talking about the importance of data in marketing. And on Wednesday, I watched a very smart presentation by Thomas Stone (hopefully, soon-to-be Dr. Stone) from prediction.io, which looks to be an interesting, open-source project for developers to call upon machine learning without the need for proprietary lock-in.

Alongside Stone, I also learned about Games Analytics from their COO, Mark Robinson. The gist of the talk was that games – particularly online games – give their producers the chance to deeply understand how players actually use their product. Through continuous contact with the players, they can learn: what stops them from playing, where they find it difficult to continue, how many times they log-in before purchasing… What I liked about this, was the lack of hand-wavey discussion about “data leading to insights.” Instead, Robinson’s talk focused on how this data can lead to quite practical decisions, such as making levels of a game quicker at the start, reducing the cost in places, and increasing it in others.

Between those two events, I had the tremendous privilege of joining around 120 others for the W3C’s Open Data on the Web. The remarkable brain-power per square inch at the workshop was mentioned quite a few times, and – although I tend to feel disinclined to perpetuate that kind of talk – I must agree. The Campus hosted architects, businesspeople, developers, hackers and scientists, from government bodies, universities, NGOs and foundations mixed with large companies (including IBM, Adobe, Tesco and Google).

I was particularly drawn to discussions about building and growing businesses on data. I’m intrigued by, and think ScraperWiki is well-placed to, work on addressing the use of open data to augment private data – for example: taking aggregated customer data, and matching with government stats, open geographic data, public social media, etc. I’ve got a few ideas for some tooling to the new ScraperWiki platform, which I’d like to explore in a few weeks.

I don’t feel there is enough space here to do proper justice to the topics covered, but suffice it to say I’m glad I had a chance to go, and was able to take part in the afternoon’s Barcamp (our team discussed the application of the recent revolution of distributed coding workflows to data handling – in other words, Github for data).

I would also like to point out a few of the sessions, and recommend the papers to read:

I don’t have a link yet to Tescos’ talk (just the abstract) about their huge sets of data (product, customers, locations, journeys…), but if anyone has, or as soon as I find it, I’ll put it here!

About Zach Beauvais

Zach is an amateur greenwood worker, mainly carving spoons, bowls and kuksas. He's worked professionally as an online editor and community manager.
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