Publicada el 4 de Junio de 2021

Life can be seen as the complexity that comes from the decisions we’re making, both ourselves and the people around us. From the most insignificant ones, like what I’m going to eat today, to bigger ones, such as deciding what I want to do professionally or who I will share my life with. All of these decisions are more or less based on the information available to us. When thinking about what I’m going to eat today, I will consider what I feel like and what I’ve eaten lately to have a balanced diet. I also consider whether I’m going to play sports later; if so, I’ll eat something light. Maybe I check the ingredients in the kitchen. We are constantly comparing data to make decisions without even being aware that we’re doing it.

The business world is no different. We all have certain goals we want to achieve, and to reach them, we organize the available resources and make decisions. Do we enter this new market? Who is the best person to lead the project? Do we go with this new tool?

However, we aren’t machines – we don’t make decisions automatically and objectively based on all the data we have. We always use our intuition – for some things more than others. We’ve developed this human capacity evolutionarily over our history as a species. It basically consists of heuristic rules that our brain uses to reach a quick conclusion, not requiring a lot of effort to collect large amounts of evidence and complex reasoning. In most situations, it works incredibly well, which is why this method has survived, but there are other times when it leads to serious systematic errors. Thanks to the work of Daniel Kahneman, a Nobel Prize winner in economics, and his colleague Tversky, we can better understand the biases that repeatedly occur without us realizing it. This sort of analysis of irrational behavior is relatively widespread in the investment world; however, it isn’t usually found as frequently in other decision-making areas as at a company.

What does all this have to do with data analytics?

Thanks to the digital revolution we’re currently immersed in, the availability of data and tools for analyzing it is such that we can support our decisions much more on objective facts than on our fragile intuition. In reality, though, we don’t do this very often. We trust our intuition when we could ask the data stored on our servers and make much more informed decisions with less risk and uncertainty and hardly any effort at all (which is what intuition would also essentially avoid).

Experience tells us that one of the key points in meetings with various stakeholders lies in the ability to differentiate opinions from facts and from comparative conclusions. It’s normal for different managers to have different subjective perspectives, so I always suggest starting by presenting the objective data. Once we all agree (you can’t disagree with facts), it’s easier to move forward and plan a common strategy. What usually happens is that each person has a partial vision of reality, so agreeing on objective information is a good starting point. Most complex decisions are complex because we don’t have the data, either because it doesn’t exist or because we don’t use it. With perfect information and a clear goal, there would be no difficulty in choosing the optimal solution. Every time we ignore data and hope that we make the right decision, we’re relying on luck.

A specific case: Light as you Need

Let’s use a real example to show how we’ve enhanced our services by using data to make better decisions. The world of outdoor lighting has been and is in constant flux, from the cave-dwellers who used fire and the modern era when gas lighting networks were invented to now, when recent years have brought new lighting technologies like LEDs and lighting systems with real-time controls.

The question we asked ourselves was: how can we continue to improve this service? We saw that one of the key operating factors in this industry is based more on experience and intuition than on data: light curves. We may not be aware of it, but most points of light nowadays are not always lit at the same level but vary according to the time. For instance, we expect fewer people to be around at 4 a.m., so there isn’t as much light as 10 p.m. However, this variation in brightness is normally programmed in a generic way, without taking the particularities of each area into account. Until now, this approach was reasonable because collecting information on citizen mobility was expensive and complex, perhaps even not feasible. Today, this landscape has vastly changed, and we can acquire this sort of data with high precision at a low cost. We therefore analyzed citizens’ mobility indices through anonymized information from their mobile devices’ connections with communication antennas. This will allow us to propose more realistic light curves adapted to real needs.

This new model’s development has been very enlightening for us, reaffirming our way of thinking. Comparing current light curves with actual citizen mobility, we see that there are notable differences. Even in areas where we would intuitively expect different behavior, data brings us back to reality and helps us make more informed decisions.

For residents, this means better service. For the city council, it’s better resource management, lower costs, and greater decision-making capacity. And for us, it’s a competitive advantage and services with greater added value.

Ultimately, the final decision-making process hasn’t changed. We start with certain objective facts, analyze the hypotheses we come up with, and draw conclusions. The biggest change in recent years lies in the enormous amount of data available to us, bringing us closer to a much more accurate picture of reality. Those who can take advantage of this information to make better decisions enjoy a market advantage that is increasingly opening a wider gap. However, it won’t take long to close because those lagging behind will either transform or disappear. There’s no chance of survival for those who can’t keep up.

Written by Miguel González Pinto the 4 de Junio de 2021 con las etiquetas: Big Data Business strategy Urban infrastructure

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