Big Data and the Pitfalls of Spurious Correlations

In Spain, it is estimated that around 68% of industrial companies are considered “digital novices” or “digital followers,” indicating that they have not yet fully adopted digitalization in their businesses and need to do so to improve their competitiveness. When analyzing large datasets, there is a warning about the danger of finding spurious correlations, where variables may appear to be related without any real sense or where a third variable might be influencing the results. Therefore, it is crucial to interpret data with caution, remembering that correlation does not imply causation, and to be aware of how graphs and visualizations are constructed to avoid incorrect conclusions.

How to Choose the Most Suitable Type of Graph

When visualizing data, it is important to consider the objective and tailor the graph to the desired storytelling. The audience for whom the results will be shown also influences the choice of graph type. The size and type of data, as well as the relationship between variables, also determine which type of graph is most suitable. It is advisable to experiment with different graphs and use multiple graphs to maintain clarity and show comparisons, trends, and relationships between variables.

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Big data y las trampas de las correlaciones espurias

En España, se estima que alrededor del 68% de las empresas industriales son consideradas “novatos digitales” o “seguidores digitales”, lo que indica que aún no han adoptado completamente la digitalización en sus negocios y necesitan hacerlo para mejorar su competitividad. Al analizar grandes conjuntos de datos, se advierte sobre el peligro de encontrar correlaciones espurias, donde variables pueden parecer relacionadas sin tener un sentido real o donde una tercera variable podría estar influyendo. Por lo tanto, es fundamental interpretar los datos con cautela, recordando que la correlación no implica causalidad, y ser conscientes de cómo se construyen los gráficos y visualizaciones para evitar conclusiones erróneas.