The trend of Big Data Analytics refers to the analysis of large quantities of data to reveal patterns of the past, highlight real-time changes in the status quo, and create predictions and forecasts for the future. This trend involves various processing techniques of structured data, which consists of specific numbers and values that are searchable and stored in a predefined format, as well as unstructured data, which may come in various native formats like video and audio files from sensors and social media posts.
The importance of data has generally been well understood for decades by those in the logistics industry. Without data and analytics, one cannot optimize or even have foresight to prepare for things to come. It is for these and other visibility reasons that logistics leaders have embraced sensors, dashboards, and other technologies to collect and display streams of information. As the use of data collecting devices increases, compounded with exponentially growing raw data found on social media and the internet overall, the rate of data coming in is far outstripping the rate of processing, with 463 quintillion (1018) bytes (or 463 billion GB) of data to be produced daily in 2025. To differentiate these massive accumulations of both structured and unstructured data from more traditional data that can be easily manipulated on a spreadsheet, experts have labelled the former as ‘big data.’
The processing and analysis of big data in real time using artificial intelligence (AI) algorithms and other technologies is itself an entire field of study, but we here at DHL see 4 main types of big data analytics that could be applied in use cases along and across entire supply chains: descriptive, diagnostic, predictive, and prescriptive analytics.
Descriptive analytics seeks to understand the existing situation and answer the question of what happened, while diagnostic analytics tries to investigate why something happened. Meanwhile, predictive analytics, as the name suggests, generates predictions and forecasts of what might happen in the future, and prescriptive analytics utilizes historical and situational data to recommend changes in what should be done.
The trend of Big Data Analytics has moderately high impact on logistics. While not directly transforming the look and feel of the supply chain physically, the greater visibility and optimized decision making that result from this trend can lead to strategic optimization along supply chain segments, substantially improving levels of service, from more efficient pallet storage in a facility to better customer case handling. In terms of realization, big data analytics is very much closer in the logistics industry than in other industries. Many, if not all, logistics leaders have harnessed big data in recent years to drive strategic decisions, and soon this trend will simply become the standard way of doing business and incorporated into logistics services.