In today’s rapidly evolving business landscape, the correct implementation of Artificial Intelligence (AI) is no longer just a technological advantage but a strategic imperative. Organizations that harness the power of AI effectively not only enhance their operational efficiency but also secure a competitive edge, driving innovation and establishing themselves as industry leaders. AI’s potential is boundless, but its success hinges on the quality of data it processes. High-quality data is the cornerstone of AI, enabling businesses to unlock their full potential and create value that propels them ahead of the competition.
The Role of Data Quality in AI
High-quality data is essential for the successful use of AI technologies in mechanical and plant engineering. The more data an AI system has at its disposal, the better it can perform its tasks. By continuously recording all company data, artificial intelligence (AI) can be trained to predict certain conditions, such as required maintenance, based on the knowledge gained. However, to make accurate and reliable decisions, AI requires precise and complete data. If incomplete or incorrect data is used, the AI will produce correspondingly inaccurate results. Therefore, it is crucial to continuously maintain company data to create a solid foundation for AI usage.
How Should Data be Structured?
Data should be as complete, available, and error-free as possible. For example, the temperature measurement of a machine must be recorded accurately and reflect the actual temperature. A machine maintenance record should include all necessary information, such as the date, actions taken, and parts used. Data should be recorded consistently within and across different systems. For instance, a product should be recorded in all systems with the same article number and attribute values. Data duplicates should be avoided, as AI technologies require unique data, such as the unique identification number of a specific machine. Clear naming of data fields and units, such as a standardized temperature specification in degrees Celsius, is also particularly important.
Creating a Solid Data Basis for AI
Generating a solid data foundation for AI technologies is a challenge for many machine and plant manufacturers. Large amounts of data must be collected, integrated, and analyzed from various sources. How can companies find a suitable solution for this?
Three Steps to a Solid Database
With a suitable data management platform, companies can achieve optimal management, integration, and analysis of data under one roof.
Step 1: Create IT Prerequisites: Data Integration
Powerful data management supports the centralized consolidation of data from different sources, enabling the optimal use of AI technologies. Cloud-native (low-code) integration solutions consolidate both on-premise and cloud-based systems, including local systems in the server room, various production sites, distributed edge data centers, and cloud environments. Some of these integration tools also support ETL processes and the API-first approach, establishing connections to the APIs of multiple applications with different integration technologies. This allows machines, back-office systems, applications, databases, APIs, IoT devices, web services, and platforms such as Google, Salesforce, SAP, and Microsoft Dynamics to be seamlessly connected in the cloud, on-premise, or in hybrid environments, enabling a seamless flow of different types of data.
Step 2: Prepare Data and Ensure Quality
In addition to establishing and continuously running processes, maintaining high data quality is essential. Automatic checks for errors and incompleteness should be performed during data entry and input to improve data validation. Incorrect or redundant data should be corrected or removed immediately. The existing IT infrastructure should enable the standardization and consolidation of data from different sources to ensure long-term consistency and completeness. Regular monitoring of data quality and audits according to defined standards and protocols are also important.
Step 3: Data Analysis and Utilization
The use of advanced analysis tools and techniques enables deeper insights and data-driven AI decisions. Dashboards and reports help present analyzed data in an understandable way and offer recommendations for action in various areas, including predictive maintenance, quality control and error detection, optimized production processes, energy consumption management, supply chain and inventory management, automation and robotics, and capacity planning.
Conclusion
The effective utilization of data is key to the successful implementation of AI in mechanical and plant engineering. With optimized data management and high data quality, AI technologies can recognize patterns in the data and make predictions that help optimize production processes and increase efficiency. At Magic Software, our Data Management & Analytics Service is designed to empower businesses by providing robust data solutions that enhance AI capabilities, driving smarter decisions and fostering innovation. With our expertise, companies can not only meet the demands of today’s market but also lead the charge into the future of AI-driven growth.
Expert insights from Stephan Romeder, VP of Global Business Development at Magic Software Enterprises.
