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Building a Foundation for AI & Analytics

With an Industrial Data Fabric

Unlock data silos to accelerate operational insights and decision-making.

Manufacturers today are sitting on vast amounts of data—more than they ever imagined. This data holds immense potential for optimizing production and driving business value, but much of it remains trapped in silos, inaccessible to those who need it most for data-driven decision-making. The root of this issue lies in the fragmented systems introduced during digital transformations, where different applications generate varying types of data with diverse storage needs. As a result, data from one source often struggles to communicate with data from another.

The Challenge of Unlocking Data Silos

To bridge these gaps, companies have historically turned to integration solutions and middleware that allow data to flow between different systems and databases. Enterprise Resource Planning (ERP) systems have also played a crucial role by consolidating data into a central repository. However, as data storage needs have evolved, central repositories such as data warehouses and data lakes have become the go-to solutions.

Recognizing the limitations of traditional methods, cloud vendors now offer a more dynamic approach to data integration: the data fabric. This technology seamlessly connects and accesses diverse data types across various 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 in a distributed data environment. It provides consistent access to data stored in the cloud, on local sites, or in hybrid environments. It aggregates data from multiple locations into a central point so that it is readily available for analysis and decision-making.

TrendMiner infogrpahic

Some of the benefits of a data fabric include:

  • Integrated Data Management: Unifies data integration, preparation, and analysis across multiple sources and destinations.
  • Automated Governance and Compliance: Includes built-in tools for data governance, compliance reporting, and quality control.
  • Real-Time Data Access: Speeds up data retrieval for faster visualizations and more informed decision-making.
  • Self-Service Data Discovery: Empowers users without specialized technical skills to discover, access, and leverage data.
  • Scalability and Flexibility: Easily scales across different environments and adapts to evolving business needs.

While many vendors—such as AWS, Microsoft Azure, and Snowflake—offer data fabric solutions suitable for general-purpose data, industrial data presents unique challenges. It encompasses information from systems like ERP, SCADA, MES, as well as sensor-generated data from manufacturing processes and industrial AI. Deploying a data fabric in such environments requires specialized architecture and deployment strategies, as noted by the ARC Advisory Group.

Laying the Foundation for Industrial AI & Analytics

Industrial organizations have a wide range of analytics use cases. When it is easier to access data, more people become empowered to make more informed decisions.

Not all manufacturers have a data fabric in place, yet operational experts still face pressure to meet business objectives. This is where TrendMiner’s Enhanced Data Layer can help. It integrates with a variety of Operational Technology (OT) and Information Technology (IT) data sources, which provides a single access point for comprehensive analysis. TrendMiner’s Enhanced Data Layer is an optimal choice for process manufacturing companies that have yet to implement a full data fabric.

Examples of OT data sources include:

  • AVEVA PI
  • AVEVA Wonderware
  • Honeywell
  • Yokogawa
  • Aspentech
  • Canary
  • GE Proficy
  • AWS Sitewise

Examples of IT data sources include:

  • SAP Digital Manufacturing
  • IBM Maximo
  • Batch Record Systems
  • OEE applications
  • Eschbach Shiftconnector
  • Microsoft PowerBI
  • Laboratory Info Systems

The Benefits of Contextualization

The Enhanced Data Layer indexes, structures, and contextualizes time-series, asset, and event data for rapid visualization in a universal production client. This 360° view of operational performance empowers engineers to detect anomalies, make informed recommendations, and predict future outcomes.

TrendMiner’s new Event Analytics module

By visualizing operational events, users can quickly review large datasets, uncover trends, and perform root cause analyses. The contextualization of process data—such as critical equipment status—into structured events further strengthens the database, so there are even greater opportunities for gaining insights and learning.

Moving Toward an Industrial Data Fabric

Typically, IT data is the first to be moved to when an organization adopts a cloud-first strategy. However, this does not immediately provide access to all the IT data. Not all the data is moved overnight. A data fabric helps democratize data across the organization as data is moved to the cloud.

Enhanced data layer works with different data fabric needsThe benefits of TrendMiner’s Enhanced Data Layer
Enhanced data layer works with different data fabric needs
Enhanced data layer works with different data fabric needs

Once an organization moves IT data to the cloud and creates a data fabric, TrendMiner’s Enhanced Data Layer makes it available for broader use. This integration contextualizes OT data and offers a more complete overview of operations. When OT data eventually moves to the cloud, the Enhanced Data Layer continues to work seamlessly within the industrial data fabric.

Achieving a 360° View of Production

The door to operational excellence opens when data is accessible all in one place. TrendMiner’s Enhanced Data Layer allows you to see the relationship between time-series data, production events, and asset structures. With more data at your fingertips, you can conduct deeper searches, explore insights, and contribute to the development of AI and machine learning models.

By creating a trusted source of data, you not only improve current operational performance but also lay the groundwork for future advancements as your company progresses in analytics maturity.

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