At a glance
Without the combination of big data technologies and a modern ERP system, large amounts of data remain unused. Big data tools are strong at pattern recognition in huge, unstructured data streams, but do not provide actionable detailed information for users. An ERP system, on the other hand, centralizes business processes, evaluates structured data and places it in a specific context, but is not designed for the entire volume of raw data. Only the targeted cooperation of both technologies makes it possible to manage data floods, generate transparent key figures and proactively control work processes.
According to a study by Bitkom Research, 60 percent of German companies have so far made little or no use of the potential of their data (as of June 2024). A big mistake, because only those who know how to analyze and utilize collected data will remain competitive.
Most companies are aware that efficient data processing will soon be essential for their survival. However, many do not know which applications they can use to really make profitable use of the huge volume of their unstructured data. An ERP system is certainly not designed to handle large volumes of raw data. And even with a powerful big data tool alone, there are no great successes to be celebrated.
In this article, we reveal how you can still achieve optimum utilization of your data with big data and ERP solutions.
What can big data tools do – and what can’t they do?
Big data is huge amounts of semi-structured or unstructured raw data that is generated in great variety and at a rapid pace. Simply collecting and storing these huge data sets does not add value for companies. On the contrary: it slows down processes, takes up valuable time and costs money.
Special big data applications are required so that you can generate value from the collected data volumes. In contrast to conventional data processing software, these applications can capture, process and analyze complex data in high-speed environments.
However, big data applications do not analyze the resulting data stream down to the smallest detail. Instead, they look for patterns and conspicuous events in order to provide decision-relevant information. The quality of the individual data packages is not the decisive factor here, but the big picture.
Dedicated big data tools are therefore ideal for recognizing regularities or repetitions in large data streams. However, they cannot link the data to practical use cases and cannot provide users with detailed information. Interpretation must take place elsewhere – in the ERP system.
Strengths and weaknesses of big data tools at a glance:
- Analysis of very large, unstructured data volumes
- Search for patterns and anomalies
- No detailed evaluation of the data
- No value-adding interpretation for the user
What can ERP systems do – and what can’t they do?
The ERP system has formed the central data backbone of a company for decades. Information from different business areas converges in the software and is available there in a central database. With an ERP system, companies can therefore not only control all business processes. It also enables them to process data from the entire company in a central view.
An intelligent ERP system enables the qualification of data and draws timely conclusions from large data sets. This is achieved through automation, real-time analyses and machine learning, among other things. Based on detailed data analyses, the software can be used to create informative business reports for team leaders, managers and decision-makers.
However, using ERP software alone as a big data tool is not an option. ERP systems are designed to manage only a limited amount of high-quality data. Monitoring and analyzing extremely extensive, complex and volatile data streams in real time is not one of their traditional tasks.
In principle, many ERP systems are also capable of processing and analyzing large volumes of unstructured data. After all, larger companies in particular produce considerable amounts of data in their day-to-day business, which in turn flow together in the ERP software. However, for the following two reasons, an ERP system cannot handle big data on its own:
Capacity limit
Big data must not be allowed to enter the ERP system unfiltered. The software is not capable of generating sufficient capacity for such large volumes of data. Sooner rather than later, the point would come at which the performance of the software suffers and the system becomes increasingly unstable due to slowdowns. Big data projects therefore require significantly more powerful tools that are equipped with the appropriate computing power.
Other objective
An ERP system takes a different approach to a big data application. While big data is primarily about pattern recognition, an ERP solution transforms company data for users into meaningful key figures. To do this, ERP uses algorithms that analyze data qualitatively. Using the same algorithms in the context of big data would require an extreme amount of computing power.
Strengths and weaknesses of ERP systems at a glance:
- Central storage and processing of company data
- Qualification and interpretation of data
- Not enough capacity for extremely large data volumes
- No mass analysis of unstructured, unfiltered data possible
Big data and ERP: it’s the combination that makes the difference
Both big data tools and ERP systems have strengths and weaknesses in big data scenarios. In order to get the most out of your data utilization, a combination of several software systems is therefore generally used. These systems only exchange process-relevant information and perform different tasks.
While a big data tool takes care of monitoring and analyzing the data stream, an ERP system puts information into a practical context. Data exchange is limited, but focused. Even if an ERP system cannot analyze large collections of data on its own, it is still extremely helpful for big data evaluations:
- The ERP system takes over the interpretation of data and thus provides essential added value. By directing the extracted analysis data in the right direction, you can relate your findings directly to practice and implement them accordingly.
- It can place existing master data in context with the analysis data and then draw conclusions that trigger automated reactions.
- It can detect problems early and reliably. For example, if the monitoring data of your machines deviates from the norm, your ERP system can notify you.
And what does this look like in practice?
Take the Internet of Things (IoT): with every machine, every component and every sensor, more and more data is added to automated production organizations – until finally no one can cope with the flood of information and the huge amount of data defies analysis.
The ERP solution of the future has a task that an IoT architecture alone cannot accomplish: It will evolve from a control module to a data hub that enables decision-makers to control and analyze the production area . In this context, it is important that the ERP software does not receive raw data, but only information that requires a reaction or interpretation.
While big data is primarily about pattern recognition, an ERP solution transforms company data into meaningful key figures for users.
A concrete example from production illustrates the interaction:
Let’s imagine that a company has a large machine park. Together, the machines are equipped with thousands of sensors that constantly transmit environmental and operating data.
Pure analysis tools are able to evaluate the resulting data stream and scan it for patterns that indicate impending failures. But even in the event of a hit, the software only knows the ID of the affected machine and the fault pattern. However, this is of little help to the employees responsible. You need additional information such as
- Designation and type of machine
- Location
- Maintenance schedule
- Available service technicians
- Affected production orders
In other words, as long as events in the data stream are not linked to master or order data, you cannot determine an appropriate response.
This is where the ERP solution comes into play:
It does not receive all the measurement data from the temperature sensor of machine A. It only receives an alert if the temperature in combination with other sensor data deviates significantly from the pattern shown by identical machines under normal conditions. In this case, it is the task of the ERP system to provide the responsible employees with all further information, for example:
- What type of machine is it?
- Where is machine A located?
- Which service technicians are responsible for machine A?
- What steps are necessary to prevent further problems?
Only when this detailed information is available can those responsible initiate further measures.
Conclusion: ERP systems will continue to create added value in the future
ERP solutions can no longer play to all their strengths in big data projects. However, they are increasingly taking on a different role in companies – away from the central data hub and towards the information cockpit. They reduce the enormous complexity of fully automated production environments to an understandable level, enrich big data analyses with the necessary contextual information and focus your employees’ attention on the essentials.
In the age of digitalization, a modern ERP system is the key to greater efficiency and profitability. It helps you to make the right decisions, sustainably optimize your value creation and
