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Let's build a tower

Let's get fluent in two languages

I've always been bothered by Pieter Bruegel the Elder’s painting of the Tower of Babel.

It’s not just that it’s difficult to tell how tall this tower is; it’s also hard to see how wide it is. And why is it leaning? And that cloud at the top, the one that’s passing in front of the tower. I mean, it’s a blue sky day, and you just wouldn't get clouds as low as that on a day like that, would you?

Of course, the cloud is there for a purpose. The top of the Tower of Babel is supposed to “reach unto heaven”. Here’s the link to the passage—from Genesis 11 in the Old Testament—that explains what the Tower of Babel was, and what it signified.

The story of the Tower of Babel is the story of how God feared the power and potential of a human race that could all speak the same language. “Nothing will be restrained from them, which they have imagined to do.” So He scattered us all far and wide, and forced us to invent new and many and different languages.

We use the language metaphor a lot when we talk about data. At the most basic level, we need to learn and master query languages in order to get at the data. Query languages (as well as functions in Excel) use syntax. The sentences in SQL start with verbs like SELECT and then move onto nouns like CRN, ADM_TYPE and SEX, and include prepositions like FROM and conjunctions like WHERE.

But the language metaphor extends beyond this narrow, technical sense. It’s way more general than that. For example, you often hear managers complaining that data analysts seem to be “speaking a different language” to them. And this isn't just an objection about our use of vocabulary or syntax; this is an observation about how one group of people see the world so differently from another that they might as well be speaking a different language.

But if we're going to apply this language metaphor to the world of data, we need to be clear about who’s speaking English and who’s not. Because I reckon the onus is on the folk speaking the foreign language to make themselves understood, and not the other way around. And for the avoidance of doubt, it’s the analysts who are speaking the foreign language here (how many times have you heard a manager say — facetiously — after a data presentation: “Thanks, but now can you just please explain it for us again? But in English this time?”)

If you want to make yourself understood by people who don't speak your language, and if it’s your responsibility to make yourself clear to them (as opposed to their responsibility to try to understand you), then what you don’t do is what the archetypal Brit abroad does and just say the same thing again but louder: “You didn't understand that graph I just emailed you? No problem, just let me email you two more graphs that you also won't understand. Oh, and here’s a third. You won't understand that one, either.”

No, if you want to make yourself understood, you have to find a way of rephrasing what you just said. Perhaps using simpler language. Perhaps speaking more slowly this time. Perhaps also using sign language. Perhaps you'll even make the effort to learn a few words of the other person’s language so that it’s clear you're at least making an effort to make yourself understood. Perhaps you and the manager will jointly work out how to come up with a way of getting the point across. Perhaps you’ll even crunch the numbers with the manager alongside and co-create the narrative.

If we adopt techniques like this, then it will feel at first as if we've become our own translators. But I don't think we should view this metaphor as a language barrier between us-the-analysts and them-the-managers. No, we should instead be thinking about the metaphor as follows. “Data-ese” is a foreign language that is neither spoken nor understood by managers. But we analysts, we lucky, talented analysts (!) — we speak both languages. We are the interpreters. We interpret data so that it’s understood by those who don't get it.

The thing I love about the Tower of Babel story is the idealism: if all human beings could speak the same language, then “nothing will be restrained from us, which we imagine to do.” And of course I have the same idealistic ambitions for data: if only the data language barrier could be broken down, then all sorts of limitless possibilities will get opened up to us.

Some of you will be familiar with Douglas Adams’s The Hitchhiker's Guide to the Galaxy. In which case you may recall this creature:

“The Babel fish is small, yellow, leech-like, and probably the oddest thing in the universe. It feeds on brain wave energy, absorbing all unconscious frequencies and then excreting telepathically a matrix formed from the conscious frequencies and nerve signals picked up from the speech centres of the brain, the practical upshot of which is that if you stick one in your ear, you can instantly understand anything said to you in any form of language: the speech you hear decodes the brain wave matrix.”

That definition itself is so beautifully opaque, so obfuscatory, so damn technical that it almost parodies itself.

But once you've read the definition two or three times you realise that it’s telling us analysts exactly what we need to do. We need to become small, we need to become yellow, and we need to become leech-like. And we need to be able to translate any data message into plain English so that it’s easily understood.

[9 January 2015]

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