It’s ok. I didn’t get dumped. I didn’t lose my job. And I didn’t get in a fight with my parents.
This isn’t a rant. This is a post about data. Big data is all the craze lately, and while you need a Computer Science degree to properly understand what it is, people are doing some pretty cool shit with it (like preventing suicide).
In fact, data is becoming so valuable that amazingly creepy data-collection methods are being developed and we are increasingly able to ‘pay’ with our data.
However, there is a common trend in advertising, not to mention the whole of society, that over-emphasises the quantitative and dismisses the qualitative.
If you need further evidence of this, think back to the last time you discussed the success of a film. Despite any opinion of quality, the final say is often had when profits are discussed. “It must be a good movie, it made 1,000 million dollars.” Perhaps this obsession with quantity occurs because it is simply easier. Either there is more or there is less, and that settles it. There’s no need for convoluted arguments with words and ideas.
In an advertising environment, this often translates to dismissing your gut feeling and going with research. It is very hard to explain your gut feeling. It is very easy to say, “8 out 10 people preferred the colour blue”.
This kind of quantitative dependence, in my opinion, leads to stale work. Life is not quantitative, you do not find a video 6/10 funny. When you use a quantitative set of tools to create something qualitative, you end up creating stale work.
People lie, numbers lie also – the tricky bastards.
Consider this: The average person has less than one arm and most people have a penis. An oversimplification, but there are some things that quantities are simply bad at illustrating. Of course, we could use a more complex set of numbers to look at the world and perhaps see a clearer picture, but these would bring their own set of faults.
Many a mathematician has claimed that, “Numbers constitute the only universal language”. Yet it is a language in which everyone is only partly fluent.
Take this experiment for example: How long would it take to count to a million at a rate of two per second?
Take a few seconds and really think about it.
I guessed it would take about three days. But in fact, at a rate of two per second, it would take around five and a half days.
With this in mind: How long it would take to count to a billion at a rate of two per second?
Think about it again.
If a million takes five days, then a billion would take…
One and a half years. I’m guessing you thought it would be less. But at a rate of two per second, it would take one and a half years of continuous counting to reach one billion. Why am I talking about this? To show you that numbers are not the universal language. That despite our dependence on quantities, we are really, really, really bad at understanding them. This isn’t just for big numbers, we have a predisposition to pick the number 7 when asked to choose a random number, and no one really knows why. So while numbers can be useful, our relationship and understanding of them is treacherous.
People are lying scoundrels
In Daniel Miller’s fantastic book, Consumption and Its Consequences, he discusses the curious manner in which people say they prefer the small independent grocer, and then shop at the large supermarket. Miller argues that this is because there is a disparity between what society, culture and politics expects of people, and what people actually do.
It’s not that people are lying, I think that most people truly do want to support small business more. But when it comes to the end of a long day and they can make one stop at the supermarket and go home to their family that little bit sooner, they choose their family over society’s expectations.
The ultimate example of this phenomenon is the media. It is often claimed that the media is overly negative, and individuals claim that they want to read more positive news. As a society, we expect that people shouldn’t enjoy reading nasty, negative things. But in fact, bad news sells. If people did actually enjoy reading about nice things, then newspapers would write about nice things. But we are stuck between what we actually read, and what society expects that we should read.
This discrepancy between the expected and the actual exists everywhere. But it rears its ugly head most when we collect quantitative data from a qualitative source. People often just plainly don’t care about an ad, or a package colour, or a brand name. When you place somebody in a context where they are expected to have an answer, such as an interview situation, they will give you the answer you expect, instead of their actual answer (Which is often “I don’t care”, “I don’t know”, or “What?).
In the words of Henry Ford:
If I had asked people what they wanted, they would have said ‘faster horses’
So next time you’re in a fight where it’s quantitative v qualitative, try and use this argument. It won’t be easy, but in the end your work will thank you.