Info Clarity

Dance music in two dimensions

noreply@blogger.com (David B) — Tue, 29 May 2012 07:32:00 +0000

Pete Tong's International Music Summit last week gave me the perfect excuse to use one of my favourite visualisations. Here we use EMI's consumer insight to look at how passionate 17 countries are about dance music.

To get a good picture of what's happening you need to think about not just what percent of people are passionate, but also what that means in terms of the number of people who are passionate. So I like to chart them both and then join them with lines.

This allows you to pretty quickly draw out some fascinating observations. For example: just look at how low the passion is for dance music in the US and yet it's such a big country that it has by far the most people passionate about dance music.

Whereas the UK scores pretty high on both metrics. I called it the 'biggest developed market'.

My full report is here and EMI have made all of this data available by country by demographic group by genre. Take the data and have fun with it! Please share what you come up with!

It's all about the people

noreply@blogger.com (David B) — Wed, 09 May 2012 10:10:00 +0000

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.

Knowing when to use data and what to use it for

noreply@blogger.com (David B) — Fri, 30 Mar 2012 08:44:00 +0000

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!

A conversation with the Big Data Insight Group

noreply@blogger.com (David B) — Wed, 28 Mar 2012 03:55:00 +0000

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

What's a data scientist?

noreply@blogger.com (David B) — Sun, 04 Mar 2012 13:35:00 +0000

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 :)

The life of an analyst: Fun with charts

noreply@blogger.com (David B) — Fri, 02 Mar 2012 08:06:00 +0000

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:

New sparklines in Google Spreadsheets are AWESOME

noreply@blogger.com (David B) — Tue, 07 Feb 2012 20:49:00 +0000

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!

Data as the new 'black gold'

noreply@blogger.com (David B) — Fri, 03 Feb 2012 07:49:00 +0000

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 :)

Sometimes words can replace data

noreply@blogger.com (David B) — Mon, 26 Dec 2011 15:07:00 +0000

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/

Little sparklines in hospital notes

noreply@blogger.com (David B) — Mon, 07 Nov 2011 06:55:00 +0000

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!

Fixing ranking charts?

noreply@blogger.com (David B) — Fri, 09 Sep 2011 06:18:00 +0000

I've been playing with ranking things in charts recently, rather than charting their actual values. There are some clear benefits to ranking things:
- sharp focus on the order
- clear sense of best / worst
- simple to grasp for non-technical users
- we avoid having to squint to see small differences on a chart

... But there are some big problems, also:
- no sense of real-world distance between points
- no sense of whether anything on the chart actually scored 'well'

So for example the music charts are simple to grasp. And the concepts of 'number one' and 'top ten' are often discussed, we have no sense of whether number ten sold anything like as many as number ten. And whether number one sold a lot or a little.

I tried to take the best of both worlds with this visualization of the popularity of dance music in a few countries. It seems to do the trick for me. Let's see if it works for anyone else :)

Org charts that really show what's going on

noreply@blogger.com (David B) — Fri, 09 Sep 2011 06:08:00 +0000

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

My hard to read report is good for you

noreply@blogger.com (David B) — Tue, 26 Apr 2011 15:21:00 +0000

So much for trying hard to bring data to life and make it easier to use. Some research generated a raft of articles recently that essentially argue for making things less accessible and harder to read!

"If a piece of information is very easy to process (e.g., in large Arial font), we may be over-confident in how well we've retained that information—and more likely to skim it—, while information that's presented in a more challenging fashion (e.g., small Comic Sans MS font) forces us to read more carefully and think more deeply about the material."
http://m.lifehacker.com//5795705/improve-your-learning-and-remember-things-better-by-switching-font-styles

Now I feel terrible for wasting so much time trying to make data clear and simple to use. I should have stuck to complex charts and equations all along :)

Standing on the shoulders of giants

noreply@blogger.com (David B) — Thu, 31 Mar 2011 07:16:00 +0000

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. It's about the process of learning from others and
helping others learn from you.

My degree was in pure maths, which helped structure this process.
Since it is without any distractions from real world assumptions and
complications, I'm not sure there is a more structured way to build
knowledge than pure maths. There are a couple of key ways pure maths
works that are pretty helpful in the real world, also.

The first is the use of other people's work. In maths, people spend
their whole lives proving that something is true. They get the result
named after them, but what they also get is the knowledge that their
work will help others to go further. Because in maths, once something
is proved you can take that piece of knowledge and make use of it. You
can take one result that one person has proved and use it to help you
prove something else. There's no need to re-prove it. No need to even
really understand how the first person proved it. You can just take it
and make use of it. No progress would be made if you had to revisit
things that had already been proved.

The second is that people write things down and share them. Results
and logic are written down using a common language, so that people can
look them up and understand them. The process would fall to pieces if
people didn't write things down, didn't explain how they reached their
conclusions, didn't share their work and didn't use a common language.

They're pretty simple things, right? No magic. No wizardry (although
if you read pure maths it does look kinda like wizardry :). But yet in
business we often don't work like this.

In business we often fail to learn from others. We start from scratch
all the time. We forget to ask ourselves 'what do we already know
about this problem that we can build on?'. And when we work something
out, crunch some data or draw some conclusions, we fail to write it
down and share it properly. Documents and presentations capture only
part of the story, the rest of which floats in the head of the author
or in the room during the presentation and then is gone.

So here is to learning from others, building our knowledge and not
re-creating the wheel. And here is to writing things down, using a
common language and sharing our work. Here is to standing on the
shoulders of giants and to helping others to stand on our shoulders.
Sometimes its the simple things that help us to make a big difference.

Some data challenges (part 1)

noreply@blogger.com (David B) — Sat, 04 Dec 2010 15:32:00 +0000

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'll introduce them here, and then I'll do a post on each challenge in the coming weeks.

1. What data should we actually make use of? I'll argue that focus is vital, and less is more.

2. How to compare? When you have multiple sources of data, how do you bring them together to add value, without confusing?

3. How do you combine data with people's skills and judgement? This is rarely talked about, as we often pretend that data drives decisions. In reality, it's almost always head + heart, so let's embrace that and work out how to optimize it!

4. How do we check skills & judgement with data, and yet support skills & judgement, so we don't blindly follow the data? Sometimes an analyst's job is to play down the data to help people to be confident enough to inject their skill & judgment.

5. Clear objectives. Data has so many uses. My live is decision-making, but there are so many other uses (good, bad and indifferent). How do we get clear about what we're actually trying to use data for?

It's gonna be fun to think about these in the coming weeks. I hope it's interesting to you! As always, I'd love thoughts, ideas and stories in the comments.

Using data day-to-day

noreply@blogger.com (David B) — Sun, 17 Jan 2010 13:03:00 +0000

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. It puts data at the heart of decision-making. Understanding, explanation and planning is all based on data. This isn't a goal or something that is talked about. It's the only way it's done. And if the facts change, people change their minds.

2. Data is gathered by and used by the same people who do the day-to-day. Unsophisticated but appropriate tools are used to capture data (see the chart below: hand-written on paper. No systems integration, no IT projects). The people gathering and presenting data are not analysts! They don't outsource it to 'data experts'.

3. Data has a 'so what' attached. See the jaundice chart below. When Emmett went above the dotted line, he got a sun bed. If he had gone above the dark line, he would have got a more serious intervention. Two observations below the dotted line and he comes of the sun-bed. Impressive, eh? Data, rules, diagnosis, intervention. All EASY to use and easy to explain.

4. Process compliance. Data and analysis isn't aspirational or done to impress the bosses. It's a process that is actually followed. Processes are the way people think and act.

5. Plans produced as a result. That's right. Despite being under-staffed and over-worked, the staff make time to plan. Objectives are identified. Interventions are linked to the objectives. And this is all written down and used to guide action. I can't count the number of organisations I've worked with that skip this step. It's thought of as a specialist skill that requires planners to rent a suite in a hotel for a week with flip-charts and brainstorming. But Emmett's ten page plan produced within hours of his birth shows that planning really can be a natural part of doing your job, and needn't be a big deal.

How was this achieved? I think there are a few lessons that I'm taking away with me.

1. Training. Proper training, with objectives, training materials and assessment. They make time for it out of their day job. They value it. And it's done properly, with proper training, trainers and resources.

2. Process compliance. People must follow processes. They must be assessed against this.

3. The right tools. We must give people the right tools to gather and make use of data, without complex systems and without needing to outsource it to analysts.

(Sent from iPhone by Emmett's cot in Queen Elisabeth Hospital. And before anyone asks, he's going to be just fine. Hopefully we'll have him home today. Yay!)

What does one trillion dollars LOOK like?

noreply@blogger.com (David B) — Wed, 25 Mar 2009 12:57:00 +0000

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. ***** All this talk about "stimulus packages" and "bailouts"... A billion dollars... A hundred billion dollars... Eight hundred billion dollars... One TRILLION dollars... What does that look like? I mean, these various numbers are tossed around like so many doggie treats, so I thought I'd take Google Sketchup out for a test drive and try to get a sense of what exactly a trillion dollars looks like. We'll start with a $100 dollar bill. Currently the largest U.S. denomination in general circulation. Most everyone has seen them, slighty fewer have owned them. Guaranteed to make friends wherever they go.

A packet of one hundred $100 bills is less than 1/2" thick and contains $10,000. Fits in your pocket easily and is more than enough for week or two of shamefully decadent fun.

Believe it or not, this next little pile is $1 million dollars (100 packets of $10,000). You could stuff that into a grocery bag and walk around with it.

While a measly $1 million looked a little unimpressive, $100 million is a little more respectable. It fits neatly on a standard pallet...

And $1 BILLION dollars... now we're really getting somewhere...

Next we'll look at ONE TRILLION dollars. This is that number we've been hearing about so much. What is a trillion dollars? Well, it's a million million. It's a thousand billion. It's a one followed by 12 zeros. You ready for this? It's pretty surprising. Go ahead... Scroll down... Ladies and gentlemen... I give you $1 trillion dollars...

(Look at the little guy in the corner at the left. And notice those pallets are double stacked.) So the next time you hear someone toss around the phrase "trillion dollars"... that's what they're talking about.

I only read the Beano

noreply@blogger.com (David B) — Sun, 25 Jan 2009 20:23:00 +0000

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.

Consider the following table of scores achieved by kids in Baltimore public schools across 1st-5th grade. (The test referred to is the California Achievement Test, but that's not important to the example.)

What conclusions might we draw from this? I think we might reasonably start to think that Baltimore public schools were failing low income pupils. They start off with only a slight disadvantage from their moneyed peers (32 poits), but end school significantly under-performing them (73 points).

You get a very different story if you look just at what happens during the time pupils were in school. Karl Alexander tested pupils at the start and at the end of every school year, enabling him to measure how many points they gained while actually in school. Here are the results:

Now it seems that, if anything, schools are of more benefit to poorer kids. Across the five grades, during the school years, they gaines 189 points, while the wealthy kids gained only 184 points. The difference between the first table and the second lies in how many points the pupils gained or lost during the long summer holidays:

And if we know this, we know that the story isn't about education at all. Its about what happens in the school holidays. Poor kids on average neither gain nor loose points over holidays. But richer kids consistently gain. So by the end of the school period, they outperform their peers.

So the analytical lesson here is to always be careful that the data you are using really supports the conclusions you are drawing from it. Ask yourself: If you cut the data differently, might it tell a completely different story? If so, give it a shot.

And what of the Beano you might ask? I have an odd memory in primary school of my class gathering around the teacher after the long summer break one year. He asked us to each pick one book we'd read over the summer and tell the class about it. One by one the pupils in my class told their classmates about one of the books they had enjoyed. When the teacher pointed to me, I had to ask whether a comic counted, since that was all I'd read over the summer.

1.4 million New Yorkers in DC for Inauguration!

noreply@blogger.com (David B) — Tue, 20 Jan 2009 08:07:00 +0000

... 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. The lesson here is that there are some things you shouldn't use polling for!

From pollster.com: One problem with a question like this one may be that it lends itself to social desirability bias. As we know, citizens tend to over-report the extent to which they will (or did) vote in elections. In a similar way, some respondents may be proclaiming that they will attend the inauguration when they don't have any real intention of going. They may do so because they hear of so many others who are attending and they feel as though it is something they should be doing as well.

This reminds me of another article pointing out something else you shouldn't use polls for: asking people whether they were at historic events.

From Political Animal: I remember reading, years ago when I lived in Miami, that a significant percentage of the population of South Florida believes they were in attendance for the famous Dolphins-Charges playoff game in 1982. That's impossible, of course, since the capacity of the Orange Bowl was only about 75,000, and the population of Miami-Dade is in the millions, but locals remembered the game so fondly, they'd fooled themselves into thinking they actually saw the game in person. It's similar to the phenomenon of the number of people claiming to have been on hand for Woodstock in 1969 -- more people believe it than could have possibly shown up.

You also shouldn't use polls to ask people whether they did things that turned out to be a very, very bad idea:

Again from Political Animal: [in a recent poll] only 33 percent of respondents admit to having voted for the guy twice, while 52 percent said they'd never voted for him at all. If that were actually true, of course, Bush would never have had the chance to run the country so firmly into the ground that people are now pretending they never liked him

... so what does that leave us that polling IS good for?

Sometimes its not cool to gather data

noreply@blogger.com (David B) — Thu, 08 Jan 2009 12:14:00 +0000

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

From the excellent XKCD.

I also love this one. I wish I'd have thought of it!!

Excel 2007 is good for something, after all

noreply@blogger.com (David B) — Mon, 08 Dec 2008 14:52:00 +0000

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 are a wide range of new options that are just a button-press away:

Here's an example of what I've been using it for:

Much better than looking at a table of numbers, I'm sure you'll agree!

Interest rates racing down

noreply@blogger.com (David B) — Fri, 05 Dec 2008 17:05:00 +0000

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

(From WSJ via Paul Kedrosky)

Florence Nightingale's eye for info clarity

noreply@blogger.com (David B) — Fri, 05 Dec 2008 07:49:00 +0000

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. She was worried that without the clear presentation of data in charts, Queen Victoria's eyes would glaze over as she scanned statistics and tables of data.

Her most famous chart is an enhancement to what we would call a pie chart. It shows the number of deaths each month by their cause.

Each month is a twelfth of a circle. Months with more deaths are longer, meaning the area of each month shows the number of deaths. You can see that during the first part of the war, the blue wedges (disease) aremuch bigger than the red ones (wounds) or black ones (other causes). After March 1855, when the Sanitary Commission arrived, the blue wedges start becoming dramatically smaller.

From Science News:

The conventional way of presenting this information would have been a bar graph, which William Playfair had created a few decades earlier. Nightingale may have preferred the coxcomb graphic to the bar graph because it places the same month in different years in the same position on the circle, allowing for easy comparison across seasons. It also makes for an arresting image. She said her coxcomb graph was designed “to affect thro’ the Eyes what we fail to convey to the public through their word-proof ears.”

Some argue that a bar graph would have made her point more dramatically, though. One of the peculiarities of Nightingale’s circular presentation is that the deaths are proportional to the area, not the radius. Since the area of a circle is pr2, the area is proportional to the square of the radius rather than to the radius itself. This difference tends to de-emphasize the contrast between the small areas and the large ones. (In an early version of this diagram, Nightingale didn’t catch this distinction and drew the graphic incorrectly, with the radii proportional to the deaths. She quickly corrected her mistake.)

It seems to have worked. The 830 page report she wrote lead to massive changes in hospitals and by the end of the century, Army mortality was lower than civilian mortality.

Spotting fraud in numbers

noreply@blogger.com (David B) — Sun, 30 Nov 2008 19:57:00 +0000

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. Hill asks his mathematics students at the Georgia Institute of Technology to go home and either flip a coin 200 times and record the results, or merely pretend to flip a coin and fake 200 results. The following day he runs his eye over the homework data, and to the students' amazement, he easily fingers nearly all those who faked their tosses.

Smart, eh? Its all because people don't know enough about how numbers really work and so can't fake data convincingly. The first thing that people get wrong when faking data is assuming that each number 0-10 has an equal chance of being used. They do not. In the real world, numbers are much more likely to start with a '1' than any other digit.

From "The First-Digit Phenomenon" by T. P. Hill, American Scientist, July-August 1998

Benford's law can be used to predict the frequency of numbers. As you can see fro mthe above, it matches closely to real-world data sets. It predicts that '1' is the most likely first digit, then '2' less so and so on and so on.

When you see the analysis of fraudulent data sets, it really comes to life:

From "The First-Digit Phenomenon" by T. P. Hill, American Scientist, July-August 1998

From the same article:

Benford's law can be used to test for fraudulent or random-guess data in income tax returns and other financial reports. Here the first significant digits of true tax data taken by Mark Nigrini from the lines of 169,662 IRS model files follow Benford's law closely. Fraudulent data taken from a 1995 King’s County, New York, District Attorney's Office study of cash disbursement and payroll in business do not follow Benford's law. Likewise, data taken from the author's study of 743 freshmen's responses to a request to write down a six-digit number at random do not follow the law. Although these are very specific examples, in general, fraudulent or concocted data appear to have far fewer numbers starting with 1 and many more starting with 6 than do true data.

Back in 2005 I worked with a team of organisers who would report the number of doors that each of their teams had knocked on each day. I thought it would be fun to see how that data compared to Benford's law. Overall, you see that it doesn't look like people were being honest:

Buit its not all bad news. It looks like some were more honest than others:

Why keep data?

noreply@blogger.com (David B) — Sat, 22 Nov 2008 15:02:00 +0000

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'. Here is who I was text messaging seven years ago:

What fun to have that data and be able to look back - not at who I remember being in touch with - but who I was ACTUALLY in touch with. There are some names on there I had completely forgotten about! One good reason to gather data and keep it laying around, I guess. I'll probably dig it out again in another seven years and then I'll really appreciate it!

(If I hadn't swapped phones so often I'd have kept all text messages since then and I could do some trend analysis. But technology has sadly not made this possible, but I'm starting again now with my iPhone and this helpful site)

(As shown in a previous post, there is a neat tool to do this kind of analysis on your Gmail, but I'm not enough of a nerd to be able to get it to work. Rubbish.)

Info Clarity

Dance music in two dimensions

It's all about the people

Knowing when to use data and what to use it for

A conversation with the Big Data Insight Group

What's a data scientist?

The life of an analyst: Fun with charts

New sparklines in Google Spreadsheets are AWESOME

Data as the new 'black gold'

Sometimes words can replace data

Little sparklines in hospital notes

Fixing ranking charts?

Org charts that really show what&apos;s going on

My hard to read report is good for you

Standing on the shoulders of giants

Some data challenges (part 1)

Using data day-to-day

What does one trillion dollars LOOK like?

I only read the Beano

1.4 million New Yorkers in DC for Inauguration!

Sometimes its not cool to gather data

Excel 2007 is good for something, after all

Interest rates racing down

Florence Nightingale's eye for info clarity

Spotting fraud in numbers

Why keep data?

Org charts that really show what's going on