Red Flags & Sacred Cows

Here follows a cautionary tale. I name the culprit, not because I have an axe to grind or it is particularly unique, but it suits the example being made.

To repeat other posts on here: when someone starts quoting facts and figures at you and citing studies, it is entirely reasonable – and very sensible – to ask some probing questions. The figures are usually being used to sell you something. Be that an idea, credibility, services that the provider of the figures can also come and fix, at a price, naturally, or just in support of their existing position on a topic.

This entire topic is made much more challenging when very emotive topics are being commented on. Race, Gender, Diversity and Inclusion are today’s Sacred Cows. These topics always seem to make many people uncomfortable, whilst trying to appear as if they are just fine with it. They often deal with this by ensuring that they say nothing, thereby keeping their head below the parapet. An unintended consequence is that lack of enquiry means that statements with regard to the Sacred Cow go unchallenged.

labels

Twenty years ago there were few, if any, consultancies that were offering to help companies address issues that can arise as a result of various forms of discrimination. Many seem to think that because they are positioning themselves as experts in the field it puts them beyond reasonable criticism and examination. Please can someone help me understand why that elevates them beyond reasonable scrutiny and criticism?

A big problem with Sacred Cow topics is that any criticism of anything to do with them – in this case, the use/misuse of data – is tantamount to trying to undermine their very raison d’etre. It isn’t at all, it is all about the data. Data doesn’t care about any of these issues. To conflate the two seems as if it is a tactic to draw one’s eye away from the data and try and shame you into ceasing with the questions.

Where you should have a problem is when data is used to misrepresent issues. Whether intentionally or unintentionally, the mishandling of data can make problems appear very different from what they actually are. A simple example is in the analysis of raw data. If certain variables are not measured during collection and then controlled for during the analysis, or sometimes data collected in a specific area produces results that are then remarked upon and treated as a general finding with to qualifications added to them.

Back to the Red Flags though. The fact that it is a sensitive topic should prevent you from asking about the provenance of the data. If someone clasps their hand to their mouth and asks how could you possibly question a respected pillar of the industry, sometimes an author etc, then remind them about speaking truth to power.

Recently, I saw a post on LinkedIn from one of the founders of Pearn Kandola LLP Which read:

“A third (32%) of people who have witnessed racism at work take no action, and a shocking two-fifths (39%) of those said that this was because they feared the consequences of doing so*. If our workplaces are to become genuine places of safety, it’s vital that the government acts quickly to curb the use of NDAs to hide instances of harassment, whether it be racist, sexist or otherwise. RacismAtWork UnconsciousBias

*According to our own research at Pearn Kandola LLP

All well and good on the face of it. Nothing wrong with citing your own research, providing you can back it up. I was interested to learn more, so I asked if the research was published, what the sample size was, where and when it was collected etc? There has been no reply. Judging by many of the comments this has been accepted without criticism or interrogation by many, a worrying indication of a lack of critical thinking. Another area of concern when data is being reported and should also raise a little red flag in your mind is the use of words like shocking. I can only imagine this is to try and increase click through. It detracts from data and sounds more like a Daily Express ‘weather armageddon’ type headline.

Sacred Cow

If the data is robust they ought to be delighted to publish it and open it up to examination. After all, if it is robust enough to underpin public claims that are made then there is no reason why it ought not to be open to examination by a third party.

To question data means that you are thinking. Whatever the topic, there should be no Sacred Cows, especially not the data.