How Do I Know…

…if I am getting the entire Data Story?

…if it was analysed properly?

…if I can trust the conclusions and recommendations?

Every executive that is reliant on decision-making data presented to them by other people shares these doubts. If you don’t know how to ask the correct questions, parse the information in the replies correctly and follow-up with the right requests for more information you will forever be at the mercy of others. My experience is that people with responsibility do not enjoy that situation.

Without an impartial assessment of the Data Story they will not be able to satisfy themselves that the Data Story they are being told is the right one. Every big decision needs to be made with a greater element of faith than was intended.

untrustworthy.png

There are two basic elements to achieving an accurate Data Story. The first is the human, and the second is the technical.

  1. Human

Everything may be tickety-boo, the best, most loyal people, are giving you a perfect Data Story. If you know this to be true then stop reading now. Life is great. On the other hand, if you ever wonder then keep reading.

(Type 1, Type 2, and Type 3 data - a recap here -  for clarity , I am writing about Type 2 and Type 3 data. Remember, Type 1 is the Mars Lander sort of stuff!)
  • “These results are from AI. It can do things we can’t.”

Whether the results are attributed to AI, which has spotted a very subtle pattern in a vast mass of data, or a straight survey designed, run and analysed by , means nothing in and of itself.

Even if an AI tool uses the best and the brightest to program the algorithms it ‘thinks and learns’ with, the fact remains that people – with all their attendant beliefs, prejudices, biases, agendas etc – set the rules, at least to start. If the machine has indeed learned by trial and error, it was still programmed by people. Therein lies the weakness.

human AI blend

This weakness comes from the initial decision makers, precisely because they aren’t you or your Board. The Board is likely to have a much wider range of experience and carry more responsibility than the Data Science/IT/Marketing departments.

How often have you spent time with these people? Are they even in the same office as you? How old are they? What are their social and political biases? And so on. Unless you know this then how can you begin to understand anything about the initial algorithms that started the AI going. When were they written, what was the market like then, by whom, in which country?

With all data collection and manipulation it is crucial to have a fuller story. It is the  background and understanding of those setting the questions, writing the algorithms, tweaking the machine learning, analysing the data, their managers, the instructions they have been given, the emphasis that this Data Story has received in the rest of the organisation before you see it. It is also insight into the marketplace provided by the sort of Thick Data that Tricia Wang and other ethnographers have popularised.

My message to you is that data is so much more than numbers. Just numbers can be misrepresent the story so greatly. We are social animals and as long as there are people involved in the production, analysis and presentation of data it doesn’t matter a jot how incredibly intelligent and fast the tools are. We are the weakness.

complicated employees

If you still struggle believing this concept then think about electronic espionage. It is rarely a failure in something mechanical that causes catastrophic breaches of security, it is the relative ease with which people can be compromised and share information. The people are the weak link.  In the very first days of hacking a chap called Kevin Mitnik in the US spoke of Social Engineering as the means to an end. We are all inherently flawed, these flaws are shaped and amplified by our social and work environments, so why couldn’t that affect the Data Story you get?

    2. Technical

  • “The data we have used is robust.”

I’ve heard that line trotted out many times. Gosh, where to start? It may be. Nonetheless, a lot can and does happen to the data before you see the pretty graph. Here are just a few things to consider before just agreeing with that assertion:

What was/were the hypothesis/hypotheses being tested?

Why?

When was it collected?

By whom (in-house or bought in from a third-party)?

Qualitative, quantitative, or a blend?

What was the method of collection (face to face interviews, Internet, watching and ticking boxes, survey, correlational, experimental, ethnographic, narrative,phenomenological, case study – you get the idea, there are more…)?

How was the study designed?

Who designed it?

How large was the sample(s)?

How was the data edited before analysis (by who, when, with what tools, any change logs etc, what questions were excluded and why)?

How was the data analysed (univariate, multivariate, logarithmic, what were the dummy variables and why, etc.)?

How is being presented to me, and why this way (scales, chart types, colouring, size, accompanying text etc)?

Research design

And so on. This is just a taste of the complexity behind the pretty pictures shown to you as part of the Data Story. From these manicured reports you are expected to make serious decisions that can have serious consequences.

You must ask yourself if you are happy knowing that the Data Story you get may be intentionally curated or unintentionally mangled. I started this site and the consultancy because I am an independent sceptic. In this age of data-driven decision-making you mustn’t forget. Incorrect data can’t take responsibility for mistakes, but you will be held to account. This is not scaremongering, it is simply fact.

If you need a discreet, reliable and sceptical  third-party to ask these questions then drop me an email.  I compile the answers or understand and highlight the gaps. You make the decisions, albeit far better informed and with the ability to show that you didn’t take the proffered Data Story at face-value, but asked an expert to help you understand it.

 

 

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