Welcome to our December Business Briefing.
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Here are some numbers I hope you find interesting (with thanks to Andrew Dilnot's podcasts).
In 1841, 42% of us were children, now it’s only 21%. Children are a much smaller component of our collective life – in part why it was more communal then and more private now.
From the early 1700s, the age of marriage fell from 26 to 23, fuelling an unprecedented population boom – doubling in 50 years and then doubling again.
At the beginning of the 20th century, half of us lived in households of 6 people or more. Now 1 household in 3 is someone living alone.
There is far less inequality in incomes now than in the 1800s. An educated man in charge of managing a large estate (like running a sizeable business today) had a salary of £12,000 in today’s money. Less than the minimum wage. A housemaid on the same estate earned the equivalent of £1,400 a year.
The average person has lost 5% of their wealth the since start of the 2008 recession. But if you went back to the early 1970s, you’d have only half the money you have now, including taking into account inflation. Travel on further to the late 1930s, and you’d only have 25% of your current wealth and only 10% by the time you reach 1830. We all got rich.
As late as 1968, of people over 16, 38% had no natural teeth, compared with 6% now. We consume fewer calories per person now than we did in the 1980s and in the 1950s during rationing.
Imagine a world without antibiotics and the risk of death that meant. All of our grandparents and many of our parents lived in such a world.
In 1908, 230,000 children died from measles, whooping cough, tuberculosis, diarrhea and dysentery. Half of all deaths were children. Now it is just 1%.
Do these numbers give you food for thought? Challenge your assumptions? Or your beliefs? [They probably don’t make you feel happier or more content (they should, shouldn’t they?) but that’s another issue altogether which Andrew also discusses.]
Anyway, would you like to be able to use numbers to have more influence over the decisions other people take? How good are you at communicating ideas based on data and evidence?
If you’d like some inspiration, then have a look at Ross’s blog.
Here are three examples in case you haven’t already seen them.
Don’t you hate it when people don’t listen? You tell them something, something you think is important, and a few days later they’ve forgotten.
A CFO complained to me last week that no-one seemed to hear her warnings of not hitting forecasts. In fact, they acted surprised when the numbers were missed, as if it came completely out of the blue.
This is the challenge for the financial professional. You have the data. You’ve drawn some conclusions about what is happening. You even have clear ideas about the actions that need to be taken. But it’s hard to get people to listen.
Why?
There are five general barriers to be overcome:
So five barriers to overcome.
Where to start? Try some ‘people watching’. Take some time to understand the people you are trying to influence. What are their beliefs, how will current events be affecting their thinking, how easy do they seem to find it to work with numbers? Look around your colleagues/bosses/staff and think about how they see the world. You already know they are a diverse bunch, but spend a few days trying to spot what makes them tick when it comes to hearing your ideas.
Make the effort to understand a bit better who you are talking to and you’ll find they end up listening a bit better.
This page from a Tesco report prompts a few things for us to think about in relation to designing such graphics.
First, the use of ‘most’ and ‘least’ comparisons can be very powerful if used properly. A lot of analyses can default to talking about averages. We could imagine this graphic being done with a “typical basket”. But using the extremes of a scale brings out very stark contrasts and helps focus us on the factors that appear to correlate with best and worst. We can then go ahead and satisfy ourselves that the data actually proves that connection.
So, perhaps you could develop such a comparison between your ‘most and least’ things, for example your:
- most and least profitable projects
- most and least recommended products
- most and least failing processes.
The same could also be done for individuals or teams: salesforce, surgeons, researchers, whoever. No doubt you will need to work hard to make sure the data is robust and we’d recommend sharing the information with the aim of helping everyone improve, but it will certainly grab attention and get people talking.
Second, you need to make sure you have the right categories for your analysis. This graphic is designed around the Guideline Daily Amounts (GDA) for food nutrition and Government advice on healthy eating and drinking. But can you spot the big item that seems oddly missing? Try looking at the final sentence in the small note at the bottom left of the page. The data is included but maybe it should be split out given its importance to health and well-being?
Finally, if you were asked to redesign this graphic to make it quicker and easier to read and understand, what changes would you make? Have a think before looking at the end here for some suggestions from me.
You don’t have to read many operational or financial reports before you discover the frequent use of red and green circles to signal how things are going for a particular metric.
Like in this integrated dashboard.
Green = Good, Red = Bad.
Sometimes they are joined by amber or yellow, to make up the classic traffic light reporting system.
There’s a problem though.
In the UK, around 5% of the population is affected by colour blindness. That’s 2.7m people.
The condition is more prevalent in men. You can expect that about 1 in 12 of your male colleagues will experience it.
For those with deuteranomaly, the most common form of colour blindness, there is a reduced sensitivity to the colour green.
With the result that most reds and greens just look the same.
So how are things going? Good or Bad?
Try yellows and blues if you want to make sure that everyone can tell the difference.
Ross Pow
December 2013
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