Business Intelligence

The technologies for collecting data along the entire value-added chain are there and they work: Sensors and actuators in machines and plants, edge technologies for fast local processing, networks for transmitting data to centralized computer centers, data storage facilities, databases and algorithms. They all have the necessary maturity. Isolated and integrated solutions from various vendors, interoperable systems, open standards and all interfaces link system worlds together.

Despite this, many companies complain that data for evaluations are contradictory, incomplete or obsolete. This often leads to management decisions based on insufficient information because the spectrum of available data has not been exhausted. In addition, the characteristics of the data used are sometimes inadequate. The bottom line: the potentially immense benefits of Big Data analyses for manufacturing companies are only available if the quality is adequate.

Challenges in data-driven business models The fact that the technologies needed for well-founded intelligent data analyses are mature and available does not mean that they are also ready for use by all companies. As a result, it may seem obvious that this is exactly where investment needs to get off the mark. However, this is not the rule. According to experts, it is primarily the lack of technical understanding that hinders many companies on the road to data-driven business models of operational business intelligence. A targeted assessment or E-learning platforms for digital readiness in industry can help.

Alongside the lack of technical understanding, the complex system landscape required for valid data analyses often slows the productive use of data. IT employees are often faced with the task of linking fully developed, heterogeneous systems that were not conceived to be open or interoperable.

The good news for companies on the road to a sound data strategy is that you already have most of the data you need. Master data and metadata, transaction data, increasingly even data from machines and plants in networked production – all of this is the raw material that can be upgraded into data-driven business models in an operational business intelligence sense.

To truly benefit from data, consistent digitization of business processes over the entire value-added chain is required. Data can only provide meaningful information if they completely cover and represent a company’s business activities. This task is handled by Business Process Management (BPM). BPM is concerned with the integration of processes and applications to make the data available. It brings an additional important benefit from the company’s point of view. Digitized processes offer the option of automation, for example, in the form of Robotic Process Automation (RPA) in which robots carry out activities that were previously performed manually. Automation tasks, in turn, are the basis for efficiency increases at the company because they replace expensive, mistake-prone manual processes with the work of IT-assisted systems.

While companies have to think about the technologies, databases, cloud storage, network technologies and algorithms, it doesn’t need to be a headache. They are available and can be adapted to individualized needs at little cost.

Here, too, the requirements are shifting in the direction of what many experts call the “democratization of IT.” The technologies are so accessible that they can be adapted in engineering departments. Development and extensive integration work by IT departments is becoming less necessary.