5 reasons why Big Data is a challenge in machine building
There are several factors that still prevent many manufacturers from fully exploiting the crisis-proof revenue potential in after-sales service. These are the top 5 reasons why Big Data is still too rarely monetized:
1. LACK OF KNOW-HOW
Using and monetizing data in service - that doesn't sound difficult in principle. In practice, however, there is often a lack of the necessary expertise and, above all, the specialists who can analyze data and derive meaningful strategies from it.
2. RIGID SILO THINKING
Silo thinking still prevails in many companies. Departments work at cross purposes instead of with each other. Silos are not only evident in collaboration, but are also noticeable when looking at data structures: Today, information that is supposed to make the service business future-proof is often scattered across several departments and employees.
Different formats, local storage locations, redundancies - all this means that data must first be extracted from different systems and unified before outdated data silos can be turned into productive data gold mines.
3. DATA BLINDNESS
The fact that data is distributed decentrally throughout the company is not only due to silos. Often, there is simply no central platform where information can converge and be clearly analyzed.
If manufacturers can't even manage to bring transparency to their flood of big data internally, how are they supposed to use the information efficiently and build data-based business models for plant operators on it?
4. LACK OF RESOURCES
Implementing a system that provides the visibility needed for efficient data management often fails in practice due to resources. Companies either balk at the cost of implementing software or don't have the time or manpower to implement a system to monetize data.
5. INEFFICIENT TOOLS
Even when vendors manage to implement a tool that aggregates and prepares data, many of them do not use the tool efficiently. Too many features, too little flexibility, and overly complex processes are not what after-sales service needs for a revenue push.