Most people are afraid of numbers. An excess of numbers is frightening, stressful, and usually, despite being intended to support decision-making as an aid - does exactly the opposite.
Most of us make decisions, at the end of the day, based on intuition, opinions, and discussions, but little based on data.
Is it correct to make data-based decisions?
Yes, but not exclusively. There will always be room for opinions, knowledge, experience, and... gut feelings. However, we shouldn't rely on these alone. Data helps us make more rational decisions, less biased by stigmas, and less based on public opinion and perceived attitudes.
But how do we deal with the natural fear of the excess numbers that overwhelm us?
This question interests us, whether we want to make decisions for ourselves, or whether we want to market and convince others, to make decisions or form opinions related to the data we have.
The two important rules I apply in this matter are related to the quantity of data and how it's presented:
Quantity. It's very tempting to present as much data as possible. Sometimes we think that the more data we have, the more thoroughly we can examine things from multiple angles and make better decisions. Unsurprisingly, the opposite is true. Too much data creates overload. Presenting data in a table may organize it, but it doesn't fundamentally change the picture.
We tend to collect every possible piece of data for ourselves, especially data that's easy to gather, but the wisdom lies in thinking. We should extract and present to ourselves (or others) only the most significant data for decision-making.
A tip I can offer in this context is to change the question we pose to ourselves when choosing which data to use.
Instead of asking: What relevant data is available to us?
We should ask ourselves: Where are we not satisfied enough with the decisions we're making? Where are we making mistakes or could potentially make mistakes?
In areas where decisions are already good, it's a waste to invest in collecting and presenting data. It's better to save resources and attention for areas where data can provide added value. This is, of course, under the assumption that such data exists and can be collected at a reasonable cost (if the cost of data collection is higher than the benefit, we'll get from using it, give up in advance).
The second rule relates to accessibility - the way data is presented. Since graphs were invented, we think we've found the royal road. Beautiful and tempting. I encounter excessive use of graphs, and gauges - all according to fashion, and here too, it often creates the impression that the more color, sophistication, and innovation - the better.
Well, that's not the case. Each type of graph has its advantages. There's a correct way to show trends, a correct way to present data groups, and so on. We won't delve into the professional theory here, but we can mention a few guiding principles: simplicity, consistency, and minimum interference. Adhering to simplicity means, for example, minimizing the use of three-dimensional graphs; consistency deals with using the same type of graph always for the same purpose; and minimum interference refers to the combination of icons, colors, and backgrounds accompanying the graph.
In recent years, a new sub-field has developed in this context - infographics. This field deals with the conceptualization of information pictorially or graphically. Infographics certainly help improve accessibility but require creativity and graphic/artistic ability and are therefore mainly relevant when facing external clients and in focused contexts.
I'm not saying it's always easy to apply these two rules - limiting the amount of data used and investing in how it's presented. But these are the two rules that help make data useful and decisions intelligent.
It's worth trying.
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