Data Science to Analyze Relationships between Companies
Big data has currently become one of the newest allies to infer relationships between companies, competitors and suppliers, since the analysis of large volumes of data can reveal missing or relational aspects between different sectors within organizations, where they can complement each other.
Tuesday, July 6, 2021
The set of networks in which companies participate are considered as a resource for them. Each company participates in different networks and their position in them is what differentiates them. A company's relationships behave as a resource that is at the basis of the heterogeneity that allows them to support a sustainable competitive advantage.
When we talk about Big Data we refer to datasets or combinations of data whose volume, complexity and speed of growth make it difficult to collect, manage, process or analyze them using conventional technologies and tools, such as relational databases, statistics or visualization packages, within the time required to make them useful.
With today's most advanced Big Data tools it is possible to understand different types of business behaviors of companies in all types of industries.
How can we take advantage of large volumes of information to analyze our competitors and their business relationships with suppliers or customers?
One way to identify various types of business relationships is, for example, to define the coordinates of various distribution centers, then locate them on a map and cross-reference them with mobility data on the people who move between these centers on a daily basis. By crossing and analyzing these layers of information via big data management techniques, relationships between competitors, suppliers, customers and others can be inferred.
Anonymized location data from different mobile devices can define trends over a specific period of time. The use of big data in records that cell phones generate at every moment, allows increasingly accurate estimates of the levels of traffic received by a logistics complex and then analyze their relationships with commercial establishments in the area and other distribution centers.
This is just a fragment of a business solution, which can be more detailed and in-depth, since it is possible to combine the anonymous records generated by the various mobile devices with other types of complementary information.
Through solutions based on advanced mobility data analytics and predictive models, it is possible to identify different types of patterns, needs, strategies or even future consumer trends.
There are hundreds of solutions and analyses that can be performed thanks to mobility data, such as forecasting models, tracking and predictive market models, business intelligence, real estate project evaluations, solutions based on geomarketing, probabilistic models, among others.
Through geospatial data analysis techniques, CentralAmericaData carried out an analysis of five Walmart distribution centers in Florida, United States, with the aim of identifying patterns in the supply chains of these five centers and their relationships with commercial establishments and other logistics complexes in the State.
Through this analysis, whose objective is to show how geospatial data science techniques can be applied to solve problems in the logistics sector, the existing relationships between Walmart distribution centers and their supply sites were identified and characterized, so that different large commercial chains can evaluate and at the same time improve processes in their respective supply chains.
What makes Big Data so useful for many business sectors is the fact that it provides answers to many questions unknown even to the stakeholders themselves. In other words, it provides a more accurate point of reference.
Big data is a term that refers to such large, fast and complex amounts of information that it is difficult or nearly impossible to process them with traditional methods.
By analyzing the large volumes of anonymous data generated by mobile devices, it is possible to establish whether a distribution center has a commercial relationship with other logistics complexes, and even with establishments that serve the end consumer.
Using the most advanced Big Data tools, it is possible to understand the behavior of the supply chains of companies in the retail sector, since by monitoring delivery parts and counting mutual visits between suppliers and vendors, it is possible to identify and establish which are the most important relationships between distribution centers and points of sale to the end consumer, such as stores.
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