Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?

In these short lines he touched on an intriguing differentiation between wisdom, knowledge and information. Add data at the beginning and people have proposed various models such as a hierarchy, chain, or pyramid to help understand their relationships.

It's perhaps like a skier who's experienced the data points of snow and conditions in the mountains most days of their life. They begin to observe predictable patterns and then gradually distill these into knowledge of the relationships between the weather and avalanche risk. And over a lifetime of building and applying such knowledge they may develop instincts and behaviours for predicting risk and living with the mountains that may even be called wisdom.

In an unlikely scenario, I once wrote a class paper, that somehow people still find to read, on The Data, Information, Knowledge, Wisdom Chain: The Metaphorical link (pdf). It touches on some of the subtle differences of how we think about each of these, such as how I might complain of information overload, but never of knowledge overload.

Data Information Knowledge Wisdom

Sir Arthur Eddington, an English astrophysicist, told a short story involving a scientist studying fish by pulling them up with nets. After checking all the fish hauled up, the scientist concludes that there is a minimum size of fish in the sea. But the fish seen were determined by the size of the holes in the net, the smaller ones having slipped through, unmeasurable. The instrument you use affects what you see. Or as Richard Hamming puts it: "You get what you measure."

This analogy provides a nice concrete example of a phenomena that affects us routinely in more subtle ways. What and how we choose to measure affects the conclusions we draw. So, a website may easily measure sales and bounce rate for its pages, while things like trust, authority or satisfaction, which may be more significant longer-term metrics, go unmeasured.

"There is always a tendency to grab the hard, firm measurement, though it may be quite irrelevant as compared to the soft one which in the long run may be much more relevant to your goals. Accuracy of measurement tends to get confused with relevance of measurement, much more than most people believe. That a measurement is accurate, reproducible, and easy to make does not mean it should be done, instead a much poorer one which is more closely related to your goals may be much more preferable. For example, in school it is easy to measure training and hard to measure education, and hence you tend to see on final exams an emphasis on the training part and a great neglect of the education part."

What gets measured gets better (an earlier take on this)

Quote from Chapter 29, You get what you measure, of Richard Hamming's, The Art of Doing Science and Engineering: Learning to Learn.

If you happen to be in the business of doing research or science Richard Hamming's Bell Lab's talk, You and Your Research (pdf), is an excellent read.

You get what you measure

There are lots of questions you can ask of data. I like this model as it gives a framework for the different types of questions you might ask. It was introduced to me as the data science pyramid with descriptive analytics — measuring what’s happening — being the foundation on which others are built. You don’t have to go in order, but it’s a nice approach to consider how the questions you’re asking of your data might evolve.

To take our school Summer fête as an example, for a long time takings were all in cash and put into one bucket at the end for counting. You end up with one number for how much you raised. But you might move to collecting more descriptive data on the takings for each stall — whether the bar earns more than the tombola, for example. You then might be able to ask diagnostic questions like figuring out why one fête earned more than another with a different mix of stalls. If you know how many people came you might be able to make some predictions on the takings of a future fête based on number of people and stall types. And if you get fancy you might start to use your data to figure out how you could raise more money for the school in future.

There are many ways to slice and dice data and the questions you ask, but I like the clarifying simplicity of this one.

As far as I know, the framework is the Analytics Ascendancy model, or Analytics Value Escalator, or other such business sounding name from Gartner.

Analytics maturity

Pitched as the one number you need to grow your business the Net Promoter Score (NPS) gives you a way to measure across your customer-base how many people love what you’re doing enough to tell their friends vs how many people are ambivalent or negative about your business. The resulting number, your NPS, from +100 to -100 has been shown to give a reliable indicator of companies’ future success.

To calculate your NPS ask your customers how likely they would be to recommend your business to a friend — or variations thereof — and give them a scale of 0 (not likely) to 10 (very likely) to answer on. Those who answer 9 or 10 are your biggest fans or Promoters. Those who answer 7 or 8 are passively satisfied — they like your business but are unlikely to shout about it. And anyone 6 or below are your detractors. If they talk about your business it could well be negative. Your NPS is your % of promoters minus the % of your detractors. For example, if you got 100 responses, 35 9s or 10s, 37 7s and 8s, and 28 0–6s then your NPS would be 35 minus 28 = 7.

Keep track over time and you’ll get a good sense of how your business is doing. Follow up with qualitative feedback, for instance “why did you give this rating?”, and get a recipe for improving your business. Simples.

Learn more in the original Harvard Business Review article, “The One Number You Need to Grow” by Frederick F. Reichheld.

Net Promoter Score

The default table crowds out your data with non-data-ink and locks your data into cells. When data is locked into cells their relationships with other data is harder to see and the reader has to work harder to learn from your table.

When making a table of data consider if, by adjusting spacing and alignment, you can make the columns or rows clear enough as they are without the prison bars. And look for opportunities to remove the visual noise to let your data and the relationships between them shine out.

The data prison is a concept from Edward Tufte. Check out the beautiful Envisioning Information, for more.

The data prison

I grew up with the idea that there are developed countries and developing countries. There was the ‘rich’ world and the 'poor’ world. I didn’t actively consider it, it’s just always been there. And it’s wrong.

Hans Rosling, in his excellent book Factfulness, shows that wealthier countries tend to have smaller families and a low child mortality rate, whereas poorer countries were more likely to have larger families and a high child mortality rate. Plotting the spread of countries in 1965 gives a reasonable approximation of a cluster of 'developed’ countries with small families where most children live, and 'developing’ countries with larger families where more children die. But that’s in 1965.

The world has changed a lot since then and now that model of dividing up the world into two buckets just doesn’t hold true with the data any more. Most people in the world are somewhere in the middle.

And more useful than dismantling my worldview is providing a new model to replace it with. A model with 4 income levels instead of 2 and the number of billions of people who currently live in each level. It’s a model with most people somewhere in the middle.

For more, check out the Factfulness book, or Gapminder. And you could do much worse than watch Hans’ entertaining TED talk: the best stats you’ve ever seen.

A world of 4 income levels

The half-life of caffeine.

There’s a famous photo series by Eadweard Muybridge that shows, unequivocally, that while galloping all four of a horse’s legs leave the ground. I’d always heard that’s what they trying to find out at the time, but from what I’ve read it was more a question of at what points do a horse’s legs leave the ground. The photos show the somewhat surprising state of leaving the ground while the horse’s legs are tucked, not when they are outstretched. It’s a lovely example of what Edward Tufte calls small multiples.

Do horses legs leave the ground at a gallop?

Data graphics can lie in many ways, but the most common, I expect, is the error of using area or volume objects to represent one-dimensional data. (More Edward Tufte. Also see Maximise data ink)

Watch for the lie factor

From the video Wealth Inequality in America

Wealth inequality in America

Principles from Edward Tufte. Remove chart junk. Erase redundant and non-data-ink. Above all else show the data.

Of course the present infographic trend laughs in the face of these principles in the interests of trying to encourage people to actually look at the data in the first place. But, assuming you actually want people to seriously infer some meaning from the data, these are not bad principles to follow when constructing your data graphics. Some basic Excel templates could do with a clean up for example.

Maximise data-ink

Tricky topics - validity trickier than reliability. At it’s core I believe reliability is about getting the same results given the same conditions and validity is about measuring what you intend to measure. Harder than it sounds.

Understanding reliability and validity

In both a “great! we’re improving kind of way,” and also a “teaching to the test” kind of way. From Richard Hamming’s, The Art of Doing Science and Engineering.

Sketchplanations tagged with data