Designing data spaces and IoT prototypes for complete networked systems

Burgeoning data volumes are transforming companies’ IT systems

New technologies and innovative methods arising in fields such as the Internet of Things, like widespread use of sensors and actuators, are providing ever greater opportunities to create and collect data. This development has received a great deal of attention in the current debate on the issue of big data.

This new profusion of data has huge implications for the IT systems and server architectures in and between companies and related actors – it is driving a trend that industry and politics have no choice but to engage with if they wish to remain able to make timely and correct decisions based on truly relevant data in the future: Big data shifts the importance of individual, generally self-contained administration or transmission systems such as enterprise resource planning (ERP) platforms, or traditional relational databases, to the data themselves. The success of a company no longer depends on functioning individual systems, but rather on data and their interpreted use in the complete networked system.

Growing interconnectedness demands new concepts for data spaces and use of technology

Before these data – which generally originate from different sources and systems, and exist in a wide range of formats – can be effectively used as information for analysis, however, business model and product development, or process optimization in a networked environment of this nature, they must be prepared, i.e., identified, recorded, stored, structured, ordered and integrated in such a way as to permit queries for a variety of different purposes. This requires suitable methods and technologies for the provision and integration of data.


Responding to growing complexity with portable data

Even in a scenario of such complexity, business processes rely on security and speed. These attributes can only be achieved with consistent data, ideally available in real time. This in turn requires data to be dissociated from their particular system of origin, and merged once again – in other words, the data must be freed from their application and system constraints. Detached from their original application and technology, the data become portable, allowing further processing in a wide variety of contexts without loss of detail. As a result, processes and entities such as order picking, containers, forklift trucks or incoming goods are reduced to the information required by a given process step or decision, becoming points on an imaginary map. Accordingly, companies no longer have to concern themselves with details, but can concentrate on what truly matters – the information itself.

The solution: interconnected data spaces and rapid IoT prototyping

This approach in turn has consequences for data space design and choice of technology, and constitutes a major challenge for companies in view of the high cost and resource expenditure involved: overhauling database systems and server architectures, choosing and integrating the required sensors and associated software and interface design all require substantial investments of money and time.

To address this problem, we develop new methods and processes that allow data to be modeled and prepared in such a way as to make them easy to use in new contexts, by freeing them from their original environment without loss of detail. This enables companies to quickly access the right data and use them in their processes, for instance by feeding them back directly into an ERP system.

How we free data from their application and system constraints

  • We devise interconnected data spaces by representing data as knowledge graphs, providing companies with uniform, flexible access to all relevant data.
  • We use rapid IoT prototyping to select the right technologies and sensors for a given process and test them in terms of their accuracy and granularity in data collection and processing in the shortest possible time. This allows companies to make a direct and cost-effective assessment at the prototype stage of how hardware and software can be integrated into their processes.

We leverage the Web of Things to accelerate and simplify implementation, using web technologies to control IT systems and devices with appropriate sensors and actuators. Moreover, we follow a graphical approach to data to represent relationships and entities.

In this way, our research enables us to create the necessary data pool for subsequent utilization as quickly and resource-efficiently as possible – from process optimization and data analytics to business model development.

Practical insights – Focus project


Focus Project

Industry 4.0 in practice

Technologies and Solutions for Digitalized Value Creation