A manufacturing company invested heavily in a data lake program, deploying a range of tools and cloud solutions to store and manage data from over 80 factories. After 4 years, the company saw little return on investment. Raw data was ingested into the cloud, but 90% of the time, data scientists and analysts spent their efforts just make sense of this unstructured data. Expensive AI initiatives remained siloed and unscalable—each machine required a custom-built model, and even similar machines from different vendors could not share AI models due to differences in IT/OT data structures.
The company needed a solution to transform its disorganised data lake into a valuable asset, capable of delivering actionable insights and scalable AI solutions across all factories.
IndustryApps introduced the IADS DataSpace, which transformed the company’s raw data swamp into standardised Digital Twin models. With IADS, all machine and factory data was contextualised and organised under a standardised schema with semantic definitions, enabling easy AI deployment and scalability.
This allowed AI models to be reused across similar machines, regardless of manufacturer or IT/OT system, significantly accelerating the company's digital transformation and AI journey.
IndustryApps' IADS DataSpace turned the company’s data swamp into a unified, valuable asset, enabling real-time insights and operational efficiencies.
Join the future of industrial digital transformation with IndustryApps. Experience seamless integration, unparalleled data control, and cutting-edge technology tailored to your needs.
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Industrial data management involves collecting, integrating, and analysing data from various sources within an industrial environment. It is crucial for enhancing operational efficiency, making informed decisions, and driving innovation through real-time insights and predictive analytics.
Industrial data management involves collecting, integrating, and analysing data from various sources within an industrial environment. It is crucial for enhancing operational efficiency, making informed decisions, and driving innovation through real-time insights and predictive analytics.
An AI co-pilot provides real-time insights and recommendations, helping operators optimize processes, reduce downtime, and improve productivity by leveraging advanced analytics and machine learning algorithms.
An AI co-pilot provides real-time insights and recommendations, helping operators optimize processes, reduce downtime, and improve productivity by leveraging advanced analytics and machine learning algorithms.
Cloud solutions are hosted online, offering flexibility and remote access. On-premise solutions are installed locally on your hardware, providing greater control and security.
Cloud solutions are hosted online, offering flexibility and remote access. On-premise solutions are installed locally on your hardware, providing greater control and security.
Our software includes robust security measures such as data encryption, regular updates, and compliance with industry standards to protect your data.
Our software includes robust security measures such as data encryption, regular updates, and compliance with industry standards to protect your data.
Yes, our solutions are designed for seamless integration with ERP, MES, IIOT, and other IT systems, ensuring smooth operation across all platforms.
Yes, our solutions are designed for seamless integration with ERP, MES, IIOT, and other IT systems, ensuring smooth operation across all platforms.
Implementation times vary based on the project’s complexity, but our streamlined process ensures you’re up and running quickly, minimizing downtime and disruption.
Implementation times vary based on the project’s complexity, but our streamlined process ensures you’re up and running quickly, minimizing downtime and disruption.
AI-supported data mapping ensures consistency and accuracy across all data sources, enhancing data quality and facilitating advanced analytics and AI applications.
AI-supported data mapping ensures consistency and accuracy across all data sources, enhancing data quality and facilitating advanced analytics and AI applications.
