I've been keeping my eye open for a good example of a PowerPoint slide that needed some love, and in a meeting today, I finally found one. I definitely don't pretend to be an expert on how to make this chart in to the perfect slide, but here are some things I would do to help it on its way.

Here is an anonomized, but otherwise un-altered version of the original slide:
The first thing I want to do when I see something like this is to remove the chart junk. I want to remove pixels that don't convey any information. The 3D and the lines around the bars don't help me to understand the data, and in fact they provide my eye with more work to do, and so detract from the data. The boxes around the chart area and the legend are unhelpful defaults in Windows. I don't need to know where the plot area or the key area ends, so they can go, also. The axis themselves can also go. They don't help and so they shouldn't stay.

The grid lines I'll leave for now, but I'll grey them, so they aren't overpowering. When you look at the slide, the data should leap out and anything like gridlines and axis and labels that help me to understand it should blend in to the background, and support the data from there.

Finally I immediately want to label the y-axis with percent and move the key to sit alongside the data. This way I don't need the extra pixels of little blue boxes to show me what they relate to, and this way they are where my eye finds them easiest to find - next to the data.

We then get something like the chart below. Basically, this is a much cleaner and easier to read version of the original chart. Notice the use of grey for the control group and the bright colour for the group we should be interested in, the group that we contacted. Also notice that I changed the wording on the labels to say exactly what they are, without any jargon (such as the phrase 'control group').
Next problem: the axis goes from 54% to 68%. This highlights the difference between the data elements, since the data is bunched up. But isn't the data being bunched up kinda the point? If on an honest scale (in this case 0% to 100%), the difference is slight, isn't that the point? Headline message: there isn't a glaring difference in the data!!!

Here's where a proper scale gets us:
Now this is still the basic chart, with some tidying and presentational suggestions. I'd go one further. I'd present the data in a different chart format:
This adds a number of steps:
  • The stacked bar draws attention to the fact that there is something 'else'. There is more data that is missing from the original chart. In response to a question, it turned out that the question that generated the chart had a couple of other answers: Voted for Jones, voted for someone else, 'I'm not telling you' and others. I suggest drawing them in grey since they're not the focus, but they're important and depending on the size and nature of them, might actually be more important than the data in the chart: If 'I'm not telling you' varies significantly by age and is big enough, it might completely explain the variance that the chart purports to show!

  • I added in black arrows to show the effect that is the point of the chart: the difference in Smith votes in people we contacted versus people we didn't contact.

  • I added a plain English explanation to the top of the chart, so that the slide means something to someone not listening or someone sent the PowerPoint by email. Why force them to listen and attend the presentation if they want to understand it?

  • Finally I added in places for two more pieces of data that are essential if you are to understand the meaning of the chart: the margin of error and the sample size. The speaker mentioned that the sample of size was small in the 18 to 39 years category and so that shouldn't be trusted. If so I'd either not show it on the chart at all, or mark the sample size clearly to illustrate that it shouldn't be trusted.
In this example I don't mean to insult the presenter. This was one slide in a long presentation of some truly excellent work. The fact that this analysis had been done and written up and presented should be an inspiration to everyone in the room. I simply mean to provide an illustration of what to look out for in charts to ensure we read the right things from them and to provide some tips on how I think the display of charts might be improved.
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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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