I recently commented on an item in the New York Times about Amazon’s pursuit of “rigorous data driven management”. Dina Vaccari, one of the employees cited in the original New York Times article, has taken the opportunity to tell her own story in this piece. I found it enlightening as to what goes on at Amazon. Of course, it is only another anecdote from a former employee, a data source of notoriously limited quality. However, as Arthur Koestler once observed:

Without the hard little bits of marble which are called ‘facts’ or ‘data’ one cannot compose a mosaic; what matters, however, are not so much the individual bits, but the successive patterns into which you arrange them, then break them up and rearrange them.

Vaccari’s role was to sell Amazon gift cards. The measure of her success was how many she sold. Vaccari had read Timothy Ferriss’ transgressive little book The 4-Hour Workweek. She decided to employ a subcontractor from Chennai, India to generate for her 100 leads daily for $10. The idea worked out well. Another illustration of the law of comparative advantage.

Vaccari them emailed the leads, not with the standard email that she had been instructed to use by Amazon, but with a formula of her own. Vacarri claims a 10 to 50% response rate. She then followed up using her traditional sales skills, exceeding her sales target and besting the rest of the sales team.

That drew attention from her supervisor. Not unnaturally he wanted to capture good practice. When he saw Vaccari’s non-standard email he was critical. We now know that process discipline is important at Amazon. Nothing wrong with that though if you really want to exercise your mind on the topic you would do well to watch the Hollywood movie Crimson Tide.

What is more interesting is that, when Vaccari answered the criticism by pointing to her response and sales figures, the supervisor retorted that this was “just luck”.

So there we have it. Somebody made a change and the organisation couldn’t agree whether or not it was an improvement. Vaccari said she saw a signal. Her supervisor said that it was just noise.

The supervisor’s response was particularly odd as he was shadowing Vacarri because of his favourable perception of her performance. It is as though his assessment as to whether Vacarri’s results were signal or noise depended on his approval or disapproval of how she had achieved them. It certainly seems that this is not normative behaviour at Amazon. Vaccari criticises her supervisor for failing to display Amazon Leadership Principles. The exchange illustrates what happens if an organisation generates data but is then unable to turn it into a reliable basis for action because there is no systematic and transparent method for creating a consensus around what is signal and what, noise. Vicarri’s exchange with her supervisor is reassuring in that both recognised that there is an important distinction. Vacarri knew that a signal should be a tocsin for action, in this case to embed a successful innovation through company wide standardisation. Her supervisor knew that to mistake noise for a signal would lead to a degraded process performance. Or at least he hid behind that to project his disapproval. Vacarri’s recall of the incident makes her “cringe”. Numbers aren’t just about numbers.

Trenchant data criticism, motivated by the rigorous segregation of signal and noise, is the catalyst of continual improvement in sales, product quality, economic efficiency and market agility.

The goal is not to be driven by data but to be led by the insights it yields.