In his bestseller Measuring the World, Daniel Kehlmann describes how Alexander von Humboldt and Carl Friedrich Gauss tried to make the world writable in the 19th century using large amounts of data and information. In fact, they would both have hoped that this “examination of the world” would eventually be completed because everything had been mapped, recorded, cataloged and described.

So far this hope has not been fulfilled. The James Webb telescope allows us to see galaxies 13 billion light-years away, and particle physics uses huge detectors to capture tiny ephemeral particle trails. This creates huge amounts of data. So we are still measuring the world.

This also applies to companies: more and more measuring points – in production, logistics or in everyday office life – provide more and more data. They should not only be kept and archived, because they describe very dynamic processes. Ideally, the data is constantly available, can be evaluated and used to make decisions when questions arise – be it by humans or by supporting artificial intelligence.

Learn more about Huawei’s data-centric storage infrastructure here.

However, many companies still think like Humboldt and Gauss: they collect and measure in a static way. Agility is required these days. IT has responded to this. The development is moving from silos, in which one infrastructure is provided per application stack, to hybrid multi-cloud infrastructure. The conventional service-oriented architecture becomes a microservice-oriented architecture and individual, monolithic applications become container-based structures with Kubernetes as the management level.

These changes are a major challenge for software developers, infrastructure architects and administrators. Previously, they chose an application, determined where and in what format the data was stored for it and provided the necessary infrastructure. In the future, the full benefit can only be gained from the data if it is quickly and easily available for various applications.

Agility at the application level therefore also requires agility in the infrastructure. Responding quickly to new requirements requires you to have the data needed to respond. Data silos don’t help here. It takes sooner a data-centric, trusted and powerful foundation for all data. Because the applications of the future – including distributed databases, big data, AI, and high-performance data analytics (HPDA) – need not only a flexible data infrastructure, but also faster data access – right down to real-time analysis.