## Graphs > tables

One phenomenon that I find very strange in the NHS (and elsewhere, probably, I’ve never worked anywhere else) is the obsession people have with having tables of numbers instead of graphs. I have encountered this absolutely everywhere. People really want to know whether something is 21.4% of the whole or 19.2% of the whole and they can’t tell by looking at the beautiful graph that you’ve drawn.

I saw an analysis today which had nine separate tables of proportions. I’m going to go out on a limb and say no human being can understand a thing of such complexity. Nine tables, each with three categories, 27 proportions given. You could fit the whole thing on one graph and it would be readily apparent how they compare with each other.

But no, people want to know is it 13% or 15%, even though in almost all cases the amount of precision far exceeds the confidence levels of the sample.

Your report needs to say “category A is found twice as often as C, whereas A and B are similar”. Not “category A is found 17.6% of the time, whereas C is found 9.2% of the time- on the other hand category B is found 19.5% of the time”. Just writing it is exhausting me, never mind trying to understand it from cold in a meeting.

There are of course rare exceptions to this rule, sometimes you really need to know that something is 13.5% of the whole. But you should be asking yourself more questions- how reliable is the measure? What is the sampling error associated with this estimate? Otherwise your 13.5% is 14.6% is 12.3%. And who is usually saying this, if anyone-me!

## Stop punishing people with data

NHS data people know all about Goodhart’s law. First stated by Goodhart as the not very catchy

Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes

It was then popularised by Strathern as

When a measure becomes a target, it ceases to be a good measure

So far, so ordinary. What else do we all know? League tables are a bad idea too. Somewhat related to Goodhart’s law, the problem with league tables is that they often ignore nuance. "Bad" schools aren’t bad, they just have an intake that starts way behind. "Bad" surgeons aren’t bad, they just take on the more complex cases or work in a geographical location where there are more complex cases. There are ways of trying to balance out this bias, "value added" in the case of schools (being married to a teacher, I hear a lot about this one), and case mix adjustment in the case of surgeons. Although these methods go some way to ironing out problems there is always the chance that an unmeasured variable is affecting the results and distorting the picture.

In my own area of work, patient experience data, league tables are a terrible idea because the services are all so different and they serve so many different people. Older people are, on average, more satisfied with healthcare. People who are detained under the mental health act for what can be 10 years are, unsurprisingly, often less positive about their healthcare.

I would like to add another law of data to the canon.

The more data is used as a punishment the less engaged those it punishes will be with all data

I’ve read so many beautifully written and persuasive arguments about the power of data, all, naturally enough, written by True Believers. People who can see how data can be used to inform service delivery and planning. If I’m honest I think I live in an echo chamber, filled with analysts and technical types, all passionate about the insights data can generate. But sadly the reality outside my little bubble is that mostly people have data done "to" them. Every quarter a manager or public body drops out of the sky and asks them for all these numbers.

They’re punished if the numbers are late. They’re punished if the numbers are wrong

And they’re punished if the numbers don’t show them in a good light even if everybody knows there’s a perfectly good explanation as to why the data looks like that (thinking back to value added and case mix adjustment above).

They get it all done, feeling harried and that the way the data is collected and scrutinised is unfair and makes their team/ department look bad, they cross their fingers and pray that it doesn’t come back with red pen on it three days later, and then they forget about it for another quarter.

And the really sad thing is that these people have questions that could be answered with data. Everybody makes suppositions and hypotheses all the time, it’s impossible not to, and with help and understanding they could refine their ideas and be more effective at their job.

But data has crashed through their window like a rabid dog and made a mess of the place and it never occurs to them to take that same dog to the park to see if it can help them find the jacket they left there last week

They’re just glad to close the door and lock it and forget about the whole ordeal until the next quarter.

I think we do need to keep promoting the value of data and analytics, and I obviously spend a decent chunk of my time either selling people that dream or (more often) fighting Python dependencies on a server to make that dream a reality.

But I think it’s just as important to stop beating people with data, to try to work with them to understand why their numbers look the way they do, and to try to make all processes that involve data more to do with learning and less to do with punishing people with an unfair analysis of what really goes on in their department.

## Shoddy data

You know, naming no names because I’ll get in trouble but someone somewhere has paid for some data from a proprietary vendor and they’re shipping absolutely unusable garbage data.

They won’t fix it because “no-one else has complained”.

HAVE SOME PRIDE IN WHAT YOU’RE DOING. How about that? How about fixing it because it’s an embarrassment? I’m a random idiot in some random NHS Trust in the countryside and I couldn’t sleep if my database looked like what you’re sending.

I swear on my life the NHS could do 99% of this stuff better itself if we just dug in and had a go. Everyone’s in thrall to these people with expensive watches and glossy brochures but it’s all a confidence trick.