The Foundation for Analytics & AI Starts with an Industrial Data Fabric
Manufacturers today are inundated with data—more than they ever imagined. This data holds immense potential to enhance production efficiency and overall business value. Yet, much of it remains trapped in silos, inaccessible to those who need it most to drive data-informed decisions. The source of this problem? Fragmented systems introduced during the wave of digital transformation. These systems generate diverse types of data with varied storage requirements, making it difficult for one data source to communicate effectively with another.
Historically, organizations have relied on middleware solutions and integrations to enable data sharing across different systems and databases. Enterprise Resource Planning (ERP) systems have also helped by centralizing data into a single repository. However, the modern landscape of data storage has evolved, with central repositories giving way to data warehouses and data lakes.
Cloud vendors now offer a more dynamic and flexible approach to data integration: the data fabric. This technology seamlessly connects and accesses various data types across locations and formats, addressing challenges like real-time data synchronization, data quality, and governance.
What is an Industrial Data Fabric?
An industrial data fabric acts as a virtualization layer within distributed data environments, enabling consistent access to data stored in the cloud, local sites, or hybrid setups. This allows organizations to aggregate data from multiple locations into a single point for easy access and analysis.
Some key benefits of a data fabric include:
General-purpose data fabrics offered by vendors like AWS, Microsoft Azure, and Snowflake are suitable for many sectors. However, manufacturing presents unique challenges, involving data from systems like ERP, SCADA, MES, and sensor-generated data from manufacturing processes. Creating an industrial data fabric requires specialized architecture and deployment strategies to handle these intricacies.
Laying the Foundation for Industrial AI & Analytics
Industrial organizations face diverse analytics use cases, from no-code solutions for quick insights to high-code applications requiring advanced data science techniques. When data access is simplified, everyone in operations gains the ability to make better, data-driven decisions.
Not every manufacturer has implemented a data fabric. In these cases, TrendMiner’s Enhanced Data Layer serves as a powerful alternative. It integrates both Operational Technology (OT) and Information Technology (IT) data sources into a unified access point, making it an optimal choice for process manufacturing companies that have yet to adopt a full data fabric.
Examples of OT data sources include:
Examples of IT data sources include:
TrendMiner’s Enhanced Data Layer indexes, structures, and contextualizes time-series, asset, and event data for rapid-fire visualization in a universal production client. This capability provides a 360° view of operational performance, enabling operational experts to detect anomalies, make recommendations, and even predict future outcomes.
By visualizing large datasets, users can perform root cause analyses and identify correlations between process data—like equipment status—and structured events. This contextualized event data enhances the database, providing more opportunities to glean valuable insights.
Moving Toward an Integrated Data Fabric
Organizations adopting a cloud-first strategy typically start by moving IT data to the cloud. But data migration doesn't happen overnight, and during the transition, valuable IT data still resides in local environments. The data fabric helps democratize data across the organization, ensuring it is accessible, whether it resides in the cloud or on-premises.
Once IT data is in the cloud and a data fabric is in place, TrendMiner’s Enhanced Data Layer works to make this data available for broader use. The integration contextualizes OT data, providing a complete overview of operations. As OT data migrates to the cloud, TrendMiner's enhanced data layer seamlessly supports this shift, working within the industrial data fabric to provide a unified view of operations.
The path to operational excellence lies in making all your data accessible in one place. TrendMiner’s Enhanced Data Layer allows you to connect time-series data, production events, and asset structures, empowering engineers to perform deeper searches, generate insights, and create AI and machine learning models. Your contributions to operational performance today lay the groundwork for future advancements as your organization progresses along its analytics maturity journey.