What is Data Analytics?
Data analytics is a powerful tool for manufacturers, which involves applying mathematical techniques to extract valuable insights from data. This can help to improve systems, understand trends, and increase efficiency across various industries.
The benefits of a relational database versus a historian database
Manufacturers are increasingly collecting data and it has become correspondingly more important to collect, save and distribute all stored data for further use. Access to this information lays the foundation for faster decisions, increased productivity and reduced costs.
It's All Connected: Care, Collaboration, and the Cloud
When it comes to MedTech, data is the gamechanger. Whether it is patient outcomes or manufacturing excellence, data holds the key to a new era in manufacturing - and with it, the Cloud. It is the future of MedTech.
5 Ways You Can Improve Your Manufacturing Operations with a Data-First Strategy
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By going digital and adopting a data-first strategy, manufacturing leaders can transform their operations and better prepare themselves for the future.
Modern Manufacturing Needs a Platform-agnostic Configuration Solution
With a platform-agnostic configuration solution, your data can float across systems and be used in any consuming application. And because you are not tied to any one enterprise system, you can create a single source of truth across the organization.
Synthetic learning - How synthetic data can help manufacturers train AI
With synthetic data providing such a useful alternative to generating real-world data, it might not seem surprising that a study by Gartner estimates that by 2024, 60 per cent of all data used in AI developments will be synthetic.
Digital Insights: Improving Collaboration Across Sites and Global Teams
Insights are more than data, it's about improving how your teams and production lines work together. Insights are crucial as they let cross-functional teams see the same data and understand the real-time state of the entire operation. Learn more in this Q&A.
Service buses and their impact on digitalization
One of the biggest challenges that enterprises face in their digitalization efforts is having too many complex data silos and applications that don't follow a common architecture.
Leverage the Edge - the Benefits of Processing Data at the Edge, Not the Cloud
The edge is an essential layer of the manufacturing technology stack. Machines on the factory floor collect vast amounts of raw data from various sources using numerous protocols, which all needs to be processed quickly to gain actionable insight.
The Current State of Supply Chain Data Quality
The implementation of strategic data quality capabilities can make or break a business. Many businesses suffer the consequences of risks and excess costs without ever understanding the root cause to be poor data quality or integration.
How Does the New Generation Memory - DDR5 - Change Things?
With more and more data comes the need for storage and fast access which means that technology like DDR5 has never been more important.
Accelerating Digital Transformation to Support Post-Pandemic Growth
We have reached a tipping point to reengineer our end-to-end supply chains. Resilience across the entire value chain is critical. You must have the systems in place and ensure there is no over-dependence on any one partner, country, or region.
5 Ways MDM Can Improve Manufacturing
The often-discussed goal is to have a "single version of the truth" across the organization. MDM is not unique to the manufacturing sector, but it's rapidly gaining ground in that industry.
The Dynamic Duo of the IoT - Combining Cloud and Edge Processing Gives the Best of Both Worlds
A challenge lies in that fact that every single smart device connected to the IoT generates huge amounts of data. All of this information must be processed and analyzed to successfully take advantage of the opportunities presented by Industry 4.0.
How Does IoT in Manufacturing Drive Employee Productivity
More devices mean more data and more information. But there is a catch! Data in itself is not helpful until used in the right context. In order to gain that context, you must ask the right questions from the data.
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