Mind The Gap: using data stories to address the void between what you think you know and what is really happening in your business

What is the Gap? 

mind the gap: In 2018, Swedish professor Hans Rosling, with his son and daughter-in-law, published a book called Factfulness. The full title is actually “Factfulness: Ten Reasons We’re Wrong About the World – and Why Things Are Better Than You Think

The core principle of the book was based on “Factfulness:The stress-reducing habit of only carrying opinions for which you have strong supporting facts” and it was a summation of work that Hans had started many years earlier. This work was crystallised in 2005 with him co-founding an organisation called Gapminder. One of the goals of Gapminder was to provide a factual view on global development. What actually happened was that the research it did brought to light how little was known about it. There was in fact a gap between what was believed to be known and what was factually correct.

All the data about global development was readily available from the United Nations, the World Health Organisation and the World Bank etc. But this was being ignored and instead an outdated view/data of the world was being used.

On the topics of
– Health
– Global population
– Income
– Education

People were getting things drastically wrong. This view of the world was decades old and had become so ingrained and readily accepted as the way things were. Compounded further by the continual propagation of these misconceptions by mainstream media, these opinions were eventually never challenged and just assumed to be correct.

Part of Gapminder’s challenge was to be able to present their findings in ways that would be easily and readily understood by anyone. So in order to achieve this, they developed a number of visualisation tools. These tools ended up being so innovative that Google bought one called Trendalyzer which was bubble chart technology. Google then built it into their own infrastructure.

Why does the gap exist?

The “gap” is obviously not a new phenomenon and it is present everywhere. A few of the reasons for it that are cited in Factfulness, are applicable in general. As humans, we have default standpoints that we take when it comes to certain things. While some of these are based on our social context and environment, much of them have evolved over thousands of years. Factfulness calls them instincts and four of those are listed below.

SizeLooking at figures in isolation and without perspective
GeneralisationDividing things into simplistic black & white views
DestinyBelieving that things don’t change
Single perspectiveThinking that things have a single root cause

How does the gap impact data analytics for business leaders?

We’ve discussed on this blog many times about the ever increasing volumes of data that organisations are now faced with. In a recent blog post on building a modern data architecture, we provided some of the numbers on the amount of data being created in the world each day and the amount of data that organisations are in possession of.

Context is key. Taken on it’s own, a result that represents an increase or decrease in sales value or volume can seem significant. But in order to clearly understand whether it is significant, the number needs to be put in proportion with other similar data. How does the value compare to products in the same category, sector or market? In order to do this with numbers rates are normally used because they end up being more meaningful.

Generalisations are a part of how things are often explained. But they can be misleading. In order to understand whether the output from running analytics is a generalisation or not, it’s important to split the data into smaller groups. This is even more necessary when the data in question consists of large groups. Then any differences or similarities within and between groups can be explored, which should then lead to understanding whether the groups are indeed relevant.
When generalisations are made, the language used to explain them can also be important e.g. “the majority of products in category A increased sales volume during the period”. In this scenario, is the majority 51%, 90% or something else?

Sometimes the rate of change of a value being measured can be slow. In particular situations it might make it seem that the value has remained constant. So in order to address this it’s important to keep track of small changes, as over a longer period of time the change may turn out to be significant.
It’s also important to keep refreshing knowledge of things that may have previously been constant. Just because they were constant during the previous period under review, doesn’t mean that they will remain constant. It’s possible that the combination of contributing factors have changed and so understanding what those are, will help to understand what has changed and why.

Single Perspective
It’s all too easy to look at problems or challenges in a single way. As the saying goes “If all you have is a hammer, everything looks like a nail” Therefore in order to validate any preconceptions or hypotheses, it helps to challenge them instead of just looking for ways to confirm them. Even the numbers themselves are not always enough to explain what is happening. Many businesses across the globe have been impacted by the current economic challenges due to the coronavirus. But purely looking at the numbers alone won’t explain any of the potentially drastic changes that have taken place between 2019 and 2020s numbers.

Bridging the gap

At Interrodata we’re all too aware of the gap that business leaders and their teams face in their day to day operations. That’s why our data science team has built analytics engines that don’t make assumptions about data. Our algorithms continually challenge findings in data in order to understand the context of change. We then use our story authoring tools to analyse the findings and build a story comprised of visualisations and a narrative to explain it all.

If you want us to help you build stories from your data then get in touch with us.

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