The reasons are manifold, ranging from the profound to the mundane. Companies may not be aware of the interactions, appreciate how they could use the data or even know that the information exists. Plus, a lot of the data is completely unstructured. The main challenge is to integrate the new data into the corporate database and link it so that it speaks the same language. To complicate matters, companies obviously pay close attention to any competitors who may also be in line to use the data.
This is where Deutsche Telekom’s Data Intelligence Hub (DIH) comes in. It serves as an interface and marketplace – one that is sorely needed, especially for data management. DIH offers central management and a comprehensive market overview of all the data that is freely available or for sale. DIH also allows experts to connect with companies that have few internal resources or little expertise in artificial intelligence – an essential ingredient in useful data fusion and analysis – so that, together, they can leverage all the data in the marketplace to optimize their processes and close gaps in their value chains. Combining data “particles” across functions, no matter how small or widely dispersed they may be, drives genuine data fusion and ultimately helps avoid production delays, unnecessary costs from waiting and excess inventory.
Telekom’s DIH serves as an impartial data custodian who observes strict security standards, protecting all the collected data and only sharing it between partners when permitted by the corresponding authorizations. Throughout this process, DIH provides the oversight, transparency and management of a decentralized and encrypted exchange without requiring Deutsche Telekom to store the data itself in the transfer process.
In logistics, for example, every freight or transport chain contains fragmentary data that can be used to forecast approximate delivery dates. However, customers – whether individuals ordering from Amazon or businesses expecting a rail consignment – expect punctuality, precision and reliability. With package deliveries, it’s clear where and when the package was loaded on the truck or train and what its current approximate location is. But some data is missing from the process chain. For example, there’s no intelligence on whether, when, why and where a consignment may be delayed. Having this information would allow transportation providers to intervene in real time, reduce waiting times and warehousing costs and ensure reliable planning for manufacturers.