Data Ethics For Business

We exist in an increasingly data driven world. More and more, we are encouraged or directed to ‘listen to the data’ above all else. After all, the data doesn’t lie. Does it?

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Data Ethics in business is the name of the practice used to ensure that the data being used to make high-value commercial decisions is of the highest quality possible. However, there is a catch. Human beings are the catch. We have  gut-instinct, prejudices, experience, belief systems, conditioning, ego, expectation, deceit, vested interests etc. These behavioural biases all stand to cloud the data story, and usually do.

A high-value commercial decision does not necessarily have immediate financial consequences. Although, in commercial terms, a sub-optimal outcome is invariably linked with financial loss. In the first instance, the immediate effects of a high-value decision can be on organisational morale or have reputational consequences.

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When a high-value decision is to be made there are invariably advocates and detractors. Both camps like to believe that they are acting in the service of a cause greater than themselves. Occasionally, some of the actors cloud the story because their self-interest is what really matters to them, and they try hard to mask that with the veneer of the greater good. Hence the term ‘Data Story’, because behind the bare numbers and pretty graphics  there is an entire story.

The concept of conducting a pre-mortem examination of the entire data story to model what can go wrong is becoming more important for senior decision makers. It is getting increasingly difficult to use the traditional internally appointed devil’s advocate as, due to the inherent complexity of understanding a data story, this function needs to be performed by subject matter experts. Although the responsibility for decision-making always falls on the Senior Management, they want to do it with a full breakdown of the many facets of the data story.

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In order to achieve this, individuals with a unique blend of talents, experience and inquisitiveness must be used. People with absolute objectivity and discretion, who don’t rely on inductive reasoning. Ones who are robust enough to operate independently, diplomatically and discreetly and have executive backing to interrogate all the data sources, ask the difficult questions and highlight any gaps, inconsistencies, irregularities. From this they can provide a report for the Executive Sponsor(s) with questions to ask and inquiries to make so a well-informed decision can be made.

After all, when there is  lots at stake, no one wants to be remembered as the person that screwed-up and tried to blame the data?

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Why is data dangerous?

In the words of @RorySutherland: “The data made me do it” is the 21st Century equivalent of “I was only obeying orders”. The growing power and influence of Data Science touches everyone’s lives. Sutherland also remarks: “Markets are complex and there can be more than one right answer. People in business prefer the pretence of ‘definitive’ because if you can show you’ve done the ‘only right thing’ you have covered yourself in event of failure”. These are all attempts at Plausible Deniability, and they are weak.

For the record, plain old data is not dangerous, you are unlikely to be hit by an errant Spearmans Rho, or a rogue Control variable that detached itself from an analysis. Data is just a record of the measurable values of something that has happened in the past. Digital exhaust, if you will. Like speed in a car, it is the inappropriate use of it that causes issues.

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Doing the right thing often sees people becoming  enslaved to Type 1 and Type 2 data, because they are the easy parts. You can hire experts, who can count well, use the software and understand how to tease out knowledge from the data points. What the majority can’t do, or may even do intentionally, is to manipulate the presentation, context and language used when presenting their findings. This is the Type 3 data I talk about, that isn’t traditional data as we know it.

Type 3 data is the really dangerous stuff. The reason for this is our complete fallibility as human beings. This is nothing to be ashamed of, it is how we are made and conditioned. It is in fact, entirely, boringly, and ordinarily normal. I was recently told by a lawyer – I say this because she is pretty well-educated – that all statistics are a lie. She then cited the famous Mark Twain (nicked from Disraeli) saying of, “There are lies, damn lies and statistics”, as if this were all the proof she required. Interestingly, when I challenged her on this and made a case for accurate uses of statistics she refused to even acknowledge this. She was wedded to her belief and I must be wrong. Case closed.

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I think immersion in courtroom rhetoric may have been getting the better of her. However, this goes to show the just how dangerous we humans can be. Imagine being a client with a lawyer whose dogmatism may cause them to overlook or be able to question relevant statistical evidence? All stemming from a strongly held view that all statistics are lies. Professor Bobby Duffy recently wrote an excellent book called Perils of Perception and on p.100 he shows just how problematic this view can be.

My point is: If a person who is well-educated, and practising in a profession like law, can hold such a position, then it is not beyond any of us to do so, quite unwittingly. Until one is more familiar with the behavioural biases that we are all susceptible to, the way Type 1 and Type 2 data can be mis-represented (Type 3 data) and how that uses our in-built foibles to generate a reaction.

This is where someone who understands both of these areas, and can blend that knowledge into an expertise which is useful, can help you. When important decisions on strategy, direction and spending  are conditional on interpreting data from others, you want to get it right first time. If not, you’ll be forced into, “The data made me do it”, and that rarely ends well.

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