Big Data and its impact on the logistics sector

Big Data, also known as macrodata, refers to massive or large-scale data that, due to its volume, complexity or growth speed, makes it difficult to capture, manage or process. However, due to its high potential, more and more organizations are working with this type of information.

Currently the use of Big Data in companies is an unstoppable trend and the transport sector is no exception. Although it is true that it is still in a very incipient phase and many companies are still not applying it in their day to day.

In Stock Logistic we have talked about automation in the logistics sector, the phenomenon of capillarity or trends in land transport of containers… now, as a key to the future, the emergence of Big Data is a phenomenon that we also have in mind.

Why is Big Data so important?

Analysis helps companies and organizations leverage their data and use it to identify new opportunities. It provides a point of reference for improving the organization internally, for example, working in the area of cost reduction or the generation of new products or services.

The use of macro data in transport

Some of the advantages of applying Big Data in the everyday life of the sector are:

  • Help with route planning and traffic management. Analytics is the ideal way to plan routes – avoiding traffic congestion as much as possible – and with absolute control of routes or times.
  • Cost reduction and optimisation. The capture of data on the fleet allows not only to have more control over the information, but also to be more relevant. From there it is easier to make decisions.
  • Analysis of driving habits such as braking, speeding, driving time, etc. With this information you can take the necessary steps to improve the way you drive and save on fuel consumption or maintenance.
  • Reduced environmental impact. Nowadays, more and more companies are trying to control and develop strategies aimed at reducing their “environmental footprint“. These measures will be much more effective if they are developed from the data extracted by the day to day of the company.
  • Marketing and sales. Big data is increasingly used for advanced segmentation of consumers, automate the customization of products, adapt communications at the time of the sales cycle or capture new sales opportunities.

Big Data Risks

However, to date, some limits have already been detected in relation to the obtaining or application of these macro data:

  • Intrinsic difficulty due to the complexity of the enormous volume of data to be collected or processed.
  • Increased risk of fraud in an environment where cybersecurity is sometimes not applied.
  • Reduced privacy and legal issues. You can run the risk of certain data being leaked, leading to severe consequences such as brand discredit, and even legal consequences for the company.

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