Measurements can mislead us, especially when they are used as management targets. That's the thesis of Jerry Muller's The Tyranny of Metrics. He writes:

What can be measured is not always what is worth measuring; what gets measured may have no relationship to what we really want to know. The costs of measuring may be greater than the benefits. The things that get measured may draw effort away from the things we really care about. And measurement may provide us with distorted knowledge - knowledge that seems solid but is actually deceptive.

Muller provides lots of examples of this, mostly from the US. But you'll all have examples of your own. In universities the Research Assessment Exercise (now the REF) contributed to increased administration costs and perhaps to the replicability crisis by incentivizing the publication of mediocre research. In schools, targets can encourage teaching to the test, endless revision and a focus upon the marginal student to the neglect of both the strongest and weakest. Waiting-time targets might distort clinical priorities. Immigration targets deter foreign students and lead to the harassment of people who have lived here for decades. Sales targets encourage workers to mis-sell financial products, cook the books, or increase risk by encouraging (pdf) "liars' loans. And so on.

All this will be familiar, especially to those of you acquainted with the work of John Seddon - although Muller puts it all concisely and well: Campbell's law, which says that measures will be misused, was proposed way back in 1979. And it's going to remain a big issue thanks to the rise of big data and machine learning - which, I suspect, will be the over-hyped discovery of weak correlations that don't hold out-of-sample.

The Tyranny of Metrics is not, however, a diatribe against targets. Muller points to the experience of some US hospitals to show that metrics can work. They do so, he says, when they are "based on collaboration and peer review":

Measurements are more likely to be meaningful when they are developed from the bottom up, with input from teachers, nurses and the cop on the beat.

In other words, metrics can succeed when they are complements to knowledge: when they organize the tacit and dispersed professional judgements of people who know ground truth. Tim Harford makes a good point when he criticizes Muller for overlooking Paul Meehl's finding (pdf) that simple statistics often out-perform professional judgment, but I don't think Meehl was arguing that statistical models should be constructed and used in complete ignorance of professionals' know-how. The latter informs the former, whilst the former in turn checks the latter.

Indeed, Muller's evidence shows that metrics can fail when they are substitutes for knowledge - when top-down bosses try to supplant professional know-how.

This raises an under-appreciated point - that questions of what statistics tell us and whether target work are not merely technocratic ones. They are deeply political, because claims to power and wealth are founded upon claims of expertise. As Alasdair MacIntyre wrote:

Civil servants and managers alike justify themselves and their claims to authority, power and money by invoking their own competence as scientific managers...But is this true? Do we now possess that set of lawlike generalizations governing social behaviour of the possession of which Diderot and Condorcet dreamed? Are our bureaucratic rulers thereby justified or not? (After Virtue, p86-7).

The belief that people and organizations can be managed and controlled by simple measures imposed from above is one of the main foundations of bosses' claim to power. In showing us that this belief is at least sometimes wrong, Muller has written a much more political book than many readers might think. And in doing so, he has also helped to advance the case for greater worker democracy.