AI and Data: why quality matters for successful application

During Data Expo 2025, which Magic Software Benelux & Nordics is attending, this theme will take center stage: how do we ensure that AI is applied reliably, scalably and future-proof? The answer lies in the basics: high-quality data.

The role of data quality in AI applications
AI technologies need large amounts of data to make accurate predictions. Think of applications such as predictive maintenance, error detection in production processes or optimization of energy consumption. But if the underlying data is incomplete, incorrect or inconsistent, it leads to wrong insights and suboptimal decisions.

A classic example from mechanical engineering: if temperature measurements of a machine are recorded inaccurately, AI cannot draw reliable conclusions about wear or failures. The same goes for maintenance reports that lack essential data, such as the date of maintenance, parts used or actions taken. After all, AI learns from what it "sees." And if it sees wrong, it learns wrong.

How should data be structured?
To use AI effectively, data must be complete, correct and uniform. Specifically, this means:

1. Sensors must provide accurate and real-time data, for example, in standardized units such as degrees Celsius.
2. Maintenance data must be consistent: the same machine should have the same ID in every system.
3. Duplicates should be avoided; AI needs unique data sets to recognize reliable patterns.
4. Fields and attributes must be clearly named: what "temp." means in one system may not be "T°C" in another.

A unified data structure, supported by clear definitions and standardized formats, is the foundation on which AI can build.

Three steps to a solid data base
At Data Expo 2025, we will share how organizations are moving toward a robust data strategy for AI in three steps:

1. Create the IT prerequisites: data integration
   A strong data foundation begins with integration. Modern integration platforms, often cloud-native and low-code, bring together data from disparate sources such as machines, ERP systems, IoT devices and cloud applications. Think connections to platforms such as Microsoft Dynamics, SAP, Salesforce or even on-site custom applications. Both on-premises and in the cloud.
   These platforms often also support ETL (Extract, Transform, Load) processes and API-first integration, essential to unlock real-time data from different systems and technologies.
   
   
2. Ensure data quality and validation
   Data should not only be present, it should be correct. When entering data, automatic validation rules are needed to minimize errors. Redundant or erroneous data must be quickly detected and corrected. Furthermore, it is important to:
   Standardize across data sources, continuously monitor data quality and perform regular data audits using established protocols.
   
   This ensures reliability and consistency, essential for any AI model.
   
   
3. Analyze and leverage the data
   Only when data is analyzed does its true value emerge. By using dashboards and analytical tools, organizations can make data-driven decisions. Consider:
   
   • Predictive maintenance (predictive maintenance)
   • Quality control and error detection
   • Optimization of production processes
   • Management of energy consumption
   • Supply chain and inventory management
   • Capacity planning and robotization
     

AI algorithms recognize patterns hidden from humans, provided the data is reliable.

Conclusion
AI in manufacturing is no longer a distant future; it is happening now. But the real value comes only when the underlying data is correct. Those who take their data quality seriously create an edge. Not just in efficiency, but also in innovation.

Magic Software Benelux & Nordics is happy to help organizations during Data Expo 2025 to structure, integrate and leverage data in a way that strengthens AI applications. Our insights are not focused on tools, but on solutions that are scalable and sustainable - ready for the future.

"AI only has impact when it is fed with the right data. Companies that invest in data quality not only build more reliable AI models, but also position themselves as leaders in their industry."
Stephan Romeder, VP Global Business Development - Magic Software Enterprises

Want to talk to us about data management, AI or integrations during Data Expo 2025? Visit us at booth #1, we are happy to share practical experiences and industry insights.