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Interpreting big data to develop successful strategies

Interpreting big data to develop successful strategies

Accurately analysing data in order to optimise processes, understand the customers’ requirements and identify successful strategies: this is the work of a data scientist, with the support of artificial intelligence.

The quantity of data contained within the digital universe is growing exponentially, but it is crucial to know how to process and manage them in order to achieve any benefit. These data allow to increase the knowledge of a given subject, in order to make predictions and optimise processes. Two resources are fundamental to the success of this: artificial intelligence and a data expert who knows how to use the right algorithms.

Big data as a puzzle

Gabriele Compostella is a data scientist at the Volkswagen Data:Lab in Munich. His work is based on creating new combinations of data which can be analysed scientifically. Putting together data so that they have a meaning is not a task which can be performed by a single human being: for this reason, Compostella works closely with AI experts. “A person cannot put together a puzzle with hundreds of thousands of pieces” he points out. “Self-learning systems, which we specifically develop for the purpose, take over that work for us”. The team feeds the algorithms with data, and the systems then analyse and combine them: at that point the data scientists can use them to draw conclusions, correcting any errors. That is how the process known as Supervised Machine Learning works.

Effectively structuring the information

The end goal of this work does not regard personal data, but rather the enormous amount of information that the Volkswagen Group’s complex corporate procedures produce on a daily basis. This includes, for example, logistics and flow of goods, key financial figures, requirements and usage down to the smallest levels. “Just like with a puzzle, you need a systematic approach to be able to recognize the big picture behind it all”, Compostella explains. The analysis of data with advanced statistical methods is therefore just the first step. But data can also be used to help answer questions correctly and in a fact-based way – this effectively means predicting the future. The technical term for that is predictive analysis; as a practical example, it could be applied to estimate how market demand will develop into an equipment line thus adapting the supply chain in advance where necessary to create benefits on multiple fronts.


The Volkswagen Group has always had this information and data, but it’s only been in the last few years that we’ve had the technological resources to manage the different big data sources” continues Compostella. The specialists at the Data:Lab are also experimenting with data analysis of traffic flows in a joint effort with cities, with the goal of optimising urban traffic.

Source: – Volkswagen AG