What data can’t tell you

You join me at the coffee shop of my office building as a familiar scene plays itself out across the road: a member of the public approaches a facade of the Barbican Conservatory in hopes of gaining admittance. They pull and push on a multitude of doors, check their phone maps, rub their eyes to make sure they are not seeing a mirage. They all leave in the end, with an unmistakable look of confusion and annoyance, whether altogether or to find an alternative entrance. I’ve been sat here for 20 minutes and during that time 5 people tried and failed to get into a conservatory.

You see, Barbican Conservatory is a masterclass in stupendously misleading design. What the visitors  are actually looking at is in effect a fire exit, and loading entrance to the conservatory. Two sets of revolving doors along the most prominent outward-facing side of the building, on either side of a massive Barbican logo? Fire exit mate. Nothing in our day-to-day experience has prepared us for this unintuitive piece of design. This has been an issue for years and yet Barbican has never bothered to use £5 quid of my £15 exhibit ticket fee to put up a poster that says “Entrance is THAT way”. At the time when government is set on slashing cultural heritage budgets, 5 people over 20 minutes on a Wednesday afternoon is 40,000 annoyed visitors a year.

There’s a delightful 1916 book called Obvious Adams that tells a story of a New England marketer who would go around improving businesses by solving problems deemed to be below the dignity of the executive management, much like ‘Customers can’t find the entrance’. It remains one of the best marketing books ever published because it speaks to the nature of problems that we do not consider. If things were bad then, they are worse now. (8 people)

Being a data person, I’d speculate that at least part of the problem lies my way. In 2022, most organisations won’t dare shifting their arse in the chair unless they have some data to support it. I am a big fan of Nariaki Kano, management professor at the University of Tokyo, who came up with a theory that products and services have salient threshold attributes – things you have to have, or no one will use it. For instance, rarely do you find yourself saying ‘The flight was good, my plane had both wings.’ Yet, if it didn’t have one of the wings you wouldn’t board the flight had I paid you to do it.

In the case of the Barbican, ‘Visitors can find the entrance’ is one such threshold attribute. Yet there is no customer segment in the database for ‘Visitors who couldn’t find the entrance’, just like there is no field in a database for ‘Our doors are misleading’ and ‘Our signage is rubbish’. As far as Barbican’s data analyst is concerned, there is no quantifiable evidence to suggest even the existence of a problem, let alone a solution.

Having spent the past 10 years of my life advocating for the benefits of data, you could imagine how emergence of pseudo-scientific data culture rather undermines my project. I don’t actually believe in data, not in a conventional sense. We are all part utilitarian and part romantic. While data can help us to quantify the former, it is completely clueless about the latter. In a business setting, data is a fantastic reference point, but it is not an actual roadmap. Data-driven doesn’t mean you get to nap in the back seat.

I rather suspect that there are a few multi-billion tech businesses harvesting riches of data that in actuality solve a psychological rather than a technological problem. I was in Bristol the other day and tried out their local version of a taxi phone app. It was functionally similar to an Uber app, with one significant exception – it didn’t have a map. Thus, I had to sit in the lobby of the hotel, thinking ‘When is it going to arrive?’, ‘Is it my cab? No, that’s someone else’s’, ‘Did they cancel?’ etc. Despite using an app, my experience of hiring a taxi was no different than it was 10, 20 or 50 years ago. Uber didn’t actually solve the phone-taxi problem (you could use a phone to hire a taxi in late 1800s), it solved the certainty problem. Humans have a great need for certainty and advent of a map transformed the psychological experience of hiring a taxi. Instead of sitting on tenterhooks you could now say ‘Oh look he’s stuck in traffic, I reckon I could down a gin and tonic before he gets here.’

Increasing emphasis on quantification of objective reality at the cost of subjective experience means that we are drastically limiting our field of solutions, and the kinds of problems we may like to solve. All of us know something that doesn’t fit in a database - what’s it like to be a human. Data is important, but that knowledge makes all the difference.

Anyway, make that 12 people.