(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. So you have also put together a root cause tree. So you know all the things that are contributing to making it the way it is. What next?

BRAG it. That is Blue, Red, Amber, Green it. Add a splash of colour and, as clear as day, show what is great (blue), good (green), poor (amber) or down right crappy (red).

This has massive advantages over the other method that springs to mind: scoring. Tempting as it is - as an analyst - to add numbers and decimal places, sometimes they really aren't needed. Sometimes all you need is a really, really simple message. Sometimes BRAG will do. Why confuse things with the complexity of numbers? Why make someone read a number and ask them self whether than number is good or bad? Does adding numbers add value or just add complexity?

Here's my root cause tree for how happy I am, with BRAG added.


The lesson there for me is that I'm doing pretty well (green overall!) despite neglecting some things I really care about. Must get on that bike (red), do something to make a difference (red), save some cash (red) and spend a little more time with friends (amber)!

I also looked back a few years to a time when I wasn't happy at work to see if my 'happy at work' root cause tree worked. Sure enough. Despite having a green or two in there, there were a lot of ambers and reds. It pretty much sums up that feeling I had each morning on the way to work.

But when I'm happy at work, does it work? I tried BRAGging the same diagram as I would have done after a few months at my current job. It works! It pretty much explains why I was jumping out of bed each morning and racing to the office. My job had the whole package!


So for me, root cause seems to do a pretty good job. Now the challenge is to use root causes and BRAGs to make better decisions, rather than to use up time analysing past ones. After all, if we analysts can't use information to make better decisions, we are just a bunch of people who sit in dark rooms having fun with our spreadsheets.
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  1. I love this idea! I'll have to give this one a try in my journal.

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  2. 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. Both in terms of how we use data to help people make decisions and about how we need to understand the people we're trying to help, in order to give them what we need. Technology enables this, but without the right people and without understanding people, technology is as good as useless.


    I also talk about how important skills and judgement are. And that, although it's sometimes seen as the things that drives decisions, it's usually or perhaps always used alongside skills and judgement. 


    I think that admitting to the role of skills and judgement isn't being 'anti-data'. I think that being honest about this enables and empowers us to better use data in the right ways. And it certainly helps people to feel comfortable with data, also!

    With the right people in place and data playing the right role in an organisation, the opportunity for data to help an organisation is massive. The way that EMI Music has embraced data across the organisation alongside skills and judgement shows that this is the case.


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  3. 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. Turn your eye away from the reviews and go with your heart :)

    The same is true of many decisions data folks are asked to help with every day in organisations. We shouldn't be afraid to champion this strategy there, either!

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  4. 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.

    http://www.thebigdatainsightgroup.com/site/article/david-boyle-emi-zeebox-data-driven-includes-video
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  5. 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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  6. 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:

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  7. So much data, so easily displayed in such a small but easy to understand format. I need say no more. I'm in love with the new sparlklines just made available in Google Spreadsheets: http://support.google.com/docs/bin/answer.py?hl=en&answer=2371371


    It's this simple:

    Google Spreadsheets is rapidly becoming my go to choice for building business dashboards. Bye, bye cost. Bye, bye developers (would be VERY sad not to work with them, of course). Bye, bye Microsoft!

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  8. 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. The oil industry had to work this out before it could be mainstream. It had to refine oil to a form that works for consumers day-to-day and it had to make it available to consumers in ways that fitted in to their daily life. It's trivial to stop by a petrol station and pick up some oil in a format you can instantly make use of. Data doesn't yet work the same way: it's rare to find an organisation that appropriately refines it and then makes it available to it's people in a way they can access and make use of as part of their day-to-day work.


    Second, I think we need to demand higher 'miles per gallon' from our data. Often we gather fantastic raw data, capable of being a really powerful part of decision making ... but then business leaders don't ask interesting questions of it. They don't demand smart analysis and challenge the data to offer insight. It's like demanding that cars offer higher miles per gallon from the oil they are burning.


    Finally, I think we need to embrace hybrid technology. In cars that's about oil being only part of the story for how the car gets powered. In data it's about saying that data is only part of the story for how organisations get powered. We need to be honest and bold about the role of skills & judgement alongside data in powering organisations. Too many people believe / pretend that data alone can power organisations to greatness. Everything I've seen tells me that data is necessary but not sufficient: smart people to use the data alongside their expertise is ALWAYS required. The data world should be honest about this and build data and systems around that truth. I've always found that has a much greater impact :)
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  9. 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.

    But reading a cool data visualization book I came across this. Really it not based on 'data', but it's interesting his words and their location on the page conveys such a lot of information. Perhaps some good, well placed words can replace the need to chart actual data?

    http://creativeroots.org/2011/03/italy-infographic-map/
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  10. Simple, easy to read, but really powerful. Nice little sparklines spotted in the papers from the 20 week scan my wife just had. Cool little chart like this should be everywhere!

    And by the way, it's a boy!
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