The next bus will come when it comes

Data is not a service. Service is not data. Knowledge is power, but it’s not always very much power.

My default mode of transport in London is the bus. Having information about when the next bus is coming has been exciting and empowering, even if the shine has worn off a little bit.

But I’m interested in the data because I want to catch a bus, not because I have a deep inner yearning for it in its own right. So here are two small stories about how that can be harder than it should be.

I am at a bus stop. It’s not looking good: there seem to be two buses in a clump eleven minutes away. As it turns out a bus actually comes in seven minutes – but almost certainly not the one which was eleven minutes away. And over those seven minutes, the data tells me a remarkably incoherent story.

Time First bus due Second bus due Third bus due
1707 11 11 16
1709 9 14 19
1710 5 5 8
1711 4 4 12
1712 3 11 16
1713 2 10 15
1713 2 5 9
1714 1 4 13

Beyond observing the fault in the space-time continuum at 1710, it’s quite hard to make sense of what is actually going on. But in a way it doesn’t matter. What matters is that I can’t trust the data: Bus checker screen shotit appears to be telling me something, but I can’t be sure that it is telling me anything.

Another time, the other side of London. I am on a bus. A hundred yards ahead, I can see another bus on the same route. The driver announces that the bus I am on is to stop short of the terminus and we must all get off at the next stop. As we get off, the bus in front has just pulled away – and it turns out that the next bus is 19 minutes behind. There is, as far as I know, nothing wrong with that data, but the wrong decision has been made based on it. Turning round the first bus rather than the second, or even holding the first bus for the thirty seconds needed to allow the second bus to catch up  would have allowed a smooth transfer with nobody waiting. As it was, the wait was long, tedious, and completely unnecessary.

This isn’t really about buses. As so often on this blog, it’s about better connecting the front end with the back end  and focusing on the service not the process. There is a lot of excitement about open data, and so there should be. The openness of TfL’s data allowed me to watch those two stories unfold on two excellent third party apps, and it’s really useful to be able to do so. But easing the transmission of dodgy data, as in the first story, isn’t really what’s wanted. And in the second story, the problem was not the data but that TfL didn’t seem to be paying any attention to it.

In the end, service design is unavoidably about designing the service.

Comments

  1. Loved this post, which really brought home the narrowness of much of the discussion about open data at the moment. As long as it remains centred on the principle of openness and the technology, rather than the utility, it will stay of interest to a principally technical audience. If it isn’t embraced by service designers and policymakers, then it’s destined to appeal solely to geeks. I’m always reminded of this passage from Bill Bryson’s “A walk in the woods” which seems apposite:

    Eight or nine other people were scattered about the summit, but there was one youngish, rather podgy man on his own in a very new and expensive-looking windcheater. He had some kind of hand-held electronic device with which he was taking mysterious readings of the sky or landscape. He noticed me watching and said, in a tone that suggested he was hoping someone would take an interests, “It’s an Enviro Monitor.”

    “Oh, yes?” I responded politely.

    “Measures eighty values – temperature, UV index, dew point, you name it.” He tilted the screen so I could see it. “That’s heat stress.” It was some meaningless number that ended in two decimal places. “It does solar radiation,” he went on, “barometric pressure, wind chill, rainfall, humidity – ambient and active – even estimated burn time adjusted for skin type.”

    “Does it bake cookies?” I asked. He didn’t like this. “There are times when it could save your life, believe me,” he said, a little stoutly. I tried to imagine a situation in which I might find myself dangerously imperilled by a rising dew point, and could not. But I didn’t want to upset the man, so I said “What’s that?” and pointed at a little blinking figure in the upper-left corner of the screen. “Ah, I’m not sure what this is. But this” – he stabbed the console of buttons – “now this is solar radiation.” It was another meaningless figure, to three decimal places. “It’s very low today,” he said and angled the machine to take another reading. “Yeah, very low today.”

    Somehow I knew this already. In fact, although I couldn’t attest any of it to three decimal places, I had a pretty good notion of the weather conditions generally, of account of I was out in them. The interesting thing about the man was that he had no pack, and so no waterproofs, and was wearing shorts and trainers. If the weather did swiftly deteriorate, and in New England it most assuredly can, he would probably die, but at least he had a machine that would tell him when and let him know his final dew point.

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