I don't like the term 'scientist' as it makes the role sound unaccessible and elite. Google's Hal Varian said "the sexy job in the next ten years will be statisticians" ... but I don't like that term either. I'd replace 'statisticians' with 'working with data' or something ... and then I believe it!  I think data people have a tendency to overplay the role of the 'statistics' and magic of it and underplay the importance of the 'bringing it to life' and 'helping people understand / make use of it' parts of working with data.

I thought about this because of this cool article in The Guardian about data scientists.

As it points out, "science" is defined as "systematic study of natural or physical phenomena". I guess that's us all. Perhaps I shouldn't shy away from that phrase.

The journalist describes the role well, as "someone who can bridge the raw data and the analysis - and make it accessible. It's a democratising role; by bringing the data to the people, you make the world just a little bit better." Perfect, eh?

One last quote: "the four qualities of a great data scientist are creativity, tenacity, curiosity, and deep technical skills." That list sounds pretty good to me, also. So perhaps I should rename this the 'data scientist' blog and be done :)



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I gave a talk at the Big Data Insight Group in London recently and they've just posted my talk online.

I talk about how we've helped EMI Music make use of data and about how we're doing so in zeebox.

One of the themes throughout my talk is the importance of people.

We all know there are decisions where you need data to help you make them and there are decisions where data just isn't that important. This morning XKCD did a wonderful job of illustrating it. http://xkcd.com/1036/

Buying a lamp is a creative decision.

We sat down recently to talk data and insight. Here is what we talked about, plus a little video of me talking about insight at both zeebox and EMI.

I don't like the term 'scientist' as it makes the role sound unaccessible and elite. Google's Hal Varian said "the sexy job in the next ten years will be statisticians" ... but I don't like that term either. I'd replace 'statisticians' with 'working with data' or something ...

Some fun from http://fosslien.com/ via http://www.freakonomics.com/2012/02/29/the-life-of-the-number-crunching-analyst/

I particularly like this one:

So much data, so easily displayed in such a small but easy to understand format. I need say no more.

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I spoke on a panel last night on the subject 'data as the new black gold'. There are three challenges I think this metaphor poses to the data world.

First, that of crude oil. Data is everywhere in organisations, but too often left in it's crude form: gloopy and unusable.

I've used a lot of word clouds recently. But I think of them as charts really, since they are still pretty faithful to the underlying data. The size of the word is proportional to the number of times that word is in the data set. Simple.

Simple, easy to read, but really powerful. Nice little sparklines spotted in the papers from the 20 week scan my wife just had.

I've been playing with ranking things in charts recently, rather than charting their actual values.

Who knew there were so many ways to draw an org chart? And that they could express so much about how relationships work in a company? I love it!

From http://www.bonkersworld.net/images/2011.06.27_organizational_charts.png

So much for trying hard to bring data to life and make it easier to use.

Waiting for a plane to take off on a cold, rainy heathrow runway just

now, a thought occurred to me about one reason I love data and helping

people use data.

Some data challenges (part 1)

Originally uploaded by David Boyle

I've been thinking a lot recently about all of the challenges I've seen day to day in making data useful. There are many more than I can capture here, but I thought I'd highlight some of the key ones I've most enjoyed wrestling with.

I'm sat next to our new son Emmett in the special care baby unit. While he sleeps I'm Reading his file. It's an impressive volume.

It's something I'd be really excited to see in any of the organisations I've worked with over the last few years. For a number of reasons:

1.

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It's amazing how difficult it is to make sense of large numbers. I think they really do need to be compared to something to make sense. I loved an approach that the New York Times took a couple of years back.

The following is another great example from an email forward that I just got.

Amongst many fascinating stories in Malcolm Gladwell's Outliers, chapter eight has one that brings back a strange memory for me. And one that should remind us to always question whether the data we are shown really supports the conclusions we draw from it.

... or so it might seem if you take polling results seriously.

In a recent poll, 9% of New Yorkers said they were planning to head to DC for the event. There are about 16 million adults in the New York area, suggesting 1.4 million people planned to make the trip.

There are some occasions where you need to keep your data gathering quiet!

From the excellent XKCD. 

I also love this one.

Ok, so there are actually a few cool features in there. Its still annoying to get used to, though!

One feature I've found pretty handy recently is the conditional formatting enhancements. They really let you visualise data quickly and easilly. Great stuff.

There was a nice little chart in the Wall Street Journal yesterday showing clearly how central banks are racing interest rates towards zero.

I just got around to reading Science News' article on how Forence Nightingale was a pioneer in using novel and innovative charts to present data. Apparently she went to great lengths to do so in order to convince Queen Victoria of the need for social change.

I remember reading about Benford's law years ago with fascination and thought I'd share it. Such a fun use of maths in the real world. Here's one application:

Dr. Theodore P.

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Not the best case study in the world, but a fun one:

I was bored at work seven years ago today. I can tell that because I was just looking through old emails and seven years ago today I was emailing Seb with an 'audit of recent text messages'.

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Since someone leaked the British National Party's membership list to the internet, I thought I'd take a look.

Here are some common first names:

And some common last names:

No surprises there then!

I couldn't find a Singh or Patel. Odd.

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I'm currently working on advertising strategy and someone just forwarded me a document arguing that when a major brand 'goes dark' on advertising for a long period, it significantly damages the brand.

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I saw another article today on how supermarkets waste too much fresh food. I thought I'd post a quick chart showing an easy way to reduce waste: buy more stuff!

Slow selling products lead to waste. They always will.

I was playing around with a really cool and easy to use analysis tool for Excel, Analyse It. And I thought I'd show how easy it is by running an old data set through it. So here is some quick analysis of an old political campaign I worked on. Seriously, this took about 30 minutes total.

I'm loving my iPhone. Today I'm loving it mainly because it adds another dimension to the data that I can gather: where things take place. For example, I use InstaMapper to track where I am in the world.

Even MC Hammer thinks its cool.

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This blog is about how you can analyse information in the world to bring clarity and make better decisions by doing so. This post is about some thoughts on how difficult that sometimes can be! We all like to be nice and to reward people who are nice to us.

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Another quick post on the wedding.

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I was excited to get a new notebook yesterday. The brand new Moleskine Volant Pocket in fact. Easy to slip in your pocket, but really well built and with fantastic paper quality. I in fact spent much of the evening in the pub writing down thoughts and ideas to celebrate finding it.

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Air Miles seem like a great deal. But I'm not sure they really are. The problem is that you have to do a bit of research and some maths to really work out whether they are a good deal or not.

(follow on to my earlier post on root causes)

Ok. So you have a thing. A thing that is good or bad or indifferent, but that you want to keep an eye on. You want to keep it good, or explain why it is bad, for example. But its complicated -there are a lot of other things that make it the way it is.

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As the New York Times says so clearly: "The human mind isn’t very well equipped to make sense of big numbers. We don’t deal with a trillion of anything in our daily lives, and so when we come across such a big number, it is hard to distinguish it from any other big number.

When you are not happy about something there are probably a dozen or more potential causes. Its usually easy to jump to an idea which of these to blame.

Treemaps are an excellent tool to allow you to play with and really understand a data set. Take, for example, the use for which they were invented: visualising hard drive usage:

The screenshot is from SpaceMonger.

I popped over to Iowa just before the causus in January to play with some data for one of the presidential candidates. One thing that has always irritated me is how difficult for most people to get access to a decent map of data.

Word from information aesthetics on a great tool from some nerds that does something I'm amazed Gmail hasn't done for you: provide visualisation of your emails.

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