These days it’s an undisputed fact that successful digital transformation requires a cultural shift across an organisation. A holistic approach spanning across process and people, infusing itself not only into any highly disrupted areas of the business, but in more stable, unchanged areas too. Changing the way people think is a key priority of any digital transformation success.
There will come a point, six months or maybe a year in, where the process is underway and people are keen to start thinking digitally. When it occurs, this should be seen as a success; as many as 84% of companies fail at digital transformation. When you reach this point you have an organisation of keen and enthusiastic people ready to champion the digital approach - which is excellent in itself.
You may have heard a quotation originating from a German General from the 1930s which is now much repeated in the business world. Paraphrasing, he proposed that people can be categorised into either ‘clever or stupid’ and ‘industrious or lazy’ – they’ll be one of the first and one of the second pairs; and the most dangerous people are stupid and industrious. While the world of business is maybe not as clear cut, it’s certainly true that without the correct knowledge and understanding, your team’s new found enthusiasm for all things digital may at best go to waste and at worst do more harm than good.
At the centre of a successful digital transformation strategy is the customer; but it is built on a foundation of data. After customer centricity, data is at the heart of good decision making. Whatever the goal of your digital transformation, it almost certainly has a basis in data, whether you’re creating efficiencies, optimising processes, growing revenues, or something else. It’s likely too that measurements of success will require some kind of data analysis, and so your people will be in contact with data, day-in, day-out. After all, information is data with context, and is required for everything.
Data literacy, the ability to read, understand, create, and communicate data as information, is therefore one of the most sought after skills in modern businesses. One of the things which separates a truly excellent digital transformation strategy from one which is merely adequate is a focus on using momentum to improve employee skills to equip them with the skills they need in the new digital world.
This might feel like another hurdle on your road to digital realisation, but it need not be. If you have successfully gained buy-in from your organisation, and started a cultural shift, your people will be primed for engagement with a data literacy programme. The first step is to identify the existing level of data literacy within your organisation. For example, business analysts are likely to have achieved a high level of data literacy already and to act as ambassadors in driving forward change.
Even in the absence of an organisation-wide education programme, there are certain key tenets that you should make sure are well understood. Being digitally literate empowers employees to feel at home in the digital world. Below I set out some of the important pointers to get up-to-speed in data literacy.
The First Step
Former BBC Head of Statistics Anthony Reuben has shared some powerful advice about how he approaches his current role as a member of their fact checking team, and it is just as relevant for business people as it is for journalists. The first thing to ask when presented with any piece of data is “Is this reasonably likely to be true?” A little bit of upfront reality checking could save a real headache down the line. Take for instance a report that the UK uses 42 billion plastic straws per year; aside from the obvious importance of the message, it is worth examining the number. The population of the UK is around 66 million people; if that report is accurate, every person in the UK is using over 630 plastic straws per annum; almost two a day. I don’t know about you, but I don’t even remember the last time I used a plastic straw.
Suss out your sources
It’s always worth questioning where data comes from, and what the source tells us? An obvious might be a tobacco company presenting findings suggesting smoking does not present the health risks normally claimed; thinking critically about the source can prevent simple errors. Just as a journalist would not trust “my mate at the gym told me” as a valid source, businesses should also be discerning consumers of data. To someone without a degree in chemistry, the website www.dhmo.org, might seem alarming, but a little scrutiny reveals what dihydrogen monoxide really is; water.
When someone tells you something is ‘average’, it might indicate the need to ask a few more questions. First of all, this could be referring to three different things; school mathematics classes teach the mean, the mode, and the median; and you’ll know from this that not all averages are created equal. One of my favourite examples of this is that people in the UK have on average less than two legs. This is because nobody has more than two legs, and some people have fewer. Whilst this claim is not mathematically incorrect, it’s not very helpful; because it uses the mean average (adding up all the results and dividing by the number of results). The mode (the most frequently occurring) and the median (the result in the middle of the highest and lowest) averages give the much more sensible answer that people average two complete legs.
Careful of Cost
Every winter, it’s pretty much guaranteed that somewhere in the UK an unexpected snowstorm will bring transport to a grinding halt, and the news will explode with cries of how much it costs the economy. Using another Anthony Reuben example, let’s consider some snow we had in 2009. One such headline claimed the disruption cost the UK economy £3bn – a figure provided by the Federation of Small Businesses. Unpacking this, the figure was split down to £1.2bn each for a snowy Monday and Tuesday, with the remainder incurred throughout the rest of the week.
The first thing you should be wondering is where this number came from. Helpfully the newspaper in question tells us: the FSB estimated that 20% of people would be off work due to snow on each of Monday and Tuesday, and therefore took 20% of what they have estimated a bank holiday to cost the economy (£6bn, apparently). Frankly, this raises more questions than it answers. Anyone who has ever worked in retail or catering will know that the whole economy does not come to a halt on a bank holiday. There are more esoteric considerations too; the occasional three-day weekend might actually make people more productive for the rest of the week. It simply doesn’t make sense to see a bank holiday as a complete loss of output for the economy.
You may well have spotted another, just as problematic assumption: that snow is bad for the economy at all. When something is described as a cost to the economy it is usually referring to a reduction in GDP; but this is a very complex measure. While some businesses such as manufacturing plants that depend on having people on site and getting deliveries of materials will experience a loss due to snow, a growing number of people can simply work from home. People probably have their heating on if it’s snowing and they are stuck in the house, as well as using other appliances they might not normally be using; and they might even be having some online retail therapy. Then there are cases such as a person whose haircut or dentist’s appointment gets cancelled due to snow; they still need a haircut or a check-up, so the economy isn’t losing out on that spending – it’s just being delayed. Then consider that sometimes bad things are good for the economy – if some people crash their cars due to bad weather, that’s actually good for GDP.
The same can be said of costing estimates across the board; they are notoriously difficult to compute and almost always contain some suspect estimations.
If a figure is quoted as a percentage and only as a percentage, it’s worth considering whether giving the underlying figures would tell a different story. For instance, if you look at the sales of vinyl records, they show a trend of decreasing for years, until recently when there has been somewhat of a resurgence in popularity. If you want to paint this in a certain light, you need only give the percentage increase in sales during this renaissance, which is around 1900% - but in their decline sales fell from around 90 million per annum to less than one million; about a 90% drop. After the new wave of vinyl buying, the annual sales are around four million. Actual sales figures tell a very different story to the percentages.
Big Numbers Overwhelm
It is incredibly difficult to really get to grips with big numbers. The annual output of the UK’s economy is a little over £2 trillion – two trillion! It’s hard for a person to really conceptualise how much money that is. This makes it quite easy to obfuscate the reality behind the numbers when they get larger than a person can easily get to grips with. The most helpful way to approach really big numbers is to contextualise them, which is why you see a lot of things being compared to the size of Wales – it means more to most people than saying ‘21,000 square kilometres’. Dividing something by some other relevant number may also help, which is why government spending is often expressed as being ‘per capita’ or similar. Remembering a few key ‘big numbers’ can also be useful, such as the population of the UK, or the distance from Earth to the Moon (239,000 miles, in case you’re wondering!).
Correlation Not Causation
Getting caught out by the assumption that correlation implies causation can be very costly. When one sees two things increasing in line with each other, one often instinctively goes on to assume one of them rising causes the other to rise. Unfortunately, these assumptions are rarely borne out in fact. If nothing else, this article should demonstrate how figures are often not what they seem on the surface, and often have a much more complex story to tell when you dig a little deeper; this is the case here too. To start with, sometimes a correlation is just a coincidence.
Secondly, sometimes there are confounding factors – if you have an ailing pot plant and play it some jazz music every day whilst at the same time adding some plant food to its water, it would be spurious to claim that jazz music makes plants recover their health.
All or Nothing
Cherry picking data can be at best misleading and at worst extremely dangerous. . If someone picks and chooses from a collection of data to present only that which supports their stance, only part of the story is being told. This is particularly noticeable in politics, where it seems that any side can provide data that upholds their point while contradicting their opponents’. It is always worth questioning what you’re not being told.
When evaluating whether one thing causes the other, it’s a good idea to start by checking whether the numbers you’re looking at are unusually specific; if they are, they might have been chosen to support a specific point-of-view rather than giving a full overview of some information. If you wanted to examine whether one thing causes another, you’re likely to have a time period in mind, i.e. 10 or 15 years, before analysing the data. If the numbers are particularly specific, for example something being more likely to happen to a certain group of people over a period of 13 years, it is worth examining what happens to the data if you examine it over a less specific period. It is likely that the data with the most prominent correlation has been deliberately chosen, and the conclusion starts to break down if you change the range. A good example of cherry picking is the UK debt – you could say that public sector debt has nearly tripled since 2002 (note the oddly specific date); if you expand the range you’ll see that debt as a percentage of GDP is less than half what it was in the 1950s.
Cautions and Conclusions
Before you go and evangelise these key data literacy pointers, it’s worth raising one last cautionary point. There are some words and phrases that should always raise questions about data. ‘Up to’ is a common one; essentially meaningless if you don’t care about the maximum. If you were offered a job with a salary ‘up to £40,000 per annum’ you would immediately be on guard – that caveat would be upheld even if you were offered a salary of £0.
Question too anything claiming to be a record number, especially when it comes to things like spending. The world is a growing place, and such is the way of the western economy that population growth, economic growth, and inflation will always mean government budgets are likely to be at record levels.
Comparisons over a long period of time are very tricky beasts too. Metrics, methods of measurement, rounding conventions; these things change over time, and those changes can have a big impact on how the numbers look. When comparing data spanning a 500-year period, it is hard to be certain of the veracity of 500-year-old figures.
Whatever approach you take, the key is to provide your staff with the skills required to make the most of the exciting new frontiers in a digitally transformed organisation. These basic principles of digital literacy should be a good starting point, and the internet is full of interesting numbers you can use to illustrate the points made above; so take these things into account, sprinkle a pinch of salt over the data you’re presented with, and make the most of your digital journey! If you’re still not sure where to start, we’re always here to help.