Key Insights from IMTS: Recommendations for Data Fabric Vendors to Win Over Industrial Enterprises

By Leo Gergs | 4Q 2024 | IN-7548

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The International Manufacturing Trade Show (IMTS) is one of the world's most important events for the manufacturing industry to come together and learn about new technology trends and products on the factory floor. After a somewhat scaled down show in 2022 (due to the COVID-19 pandemic), this year's edition saw the show return to full scale, bringing together about 100,000 visitors and more than 1,700 exhibitors. In line with the growing adoption of automation, data storage, management, and integration were one of the key topics at IMTS 2024, with different vendors showcasing their solutions and following different approaches.

In the context of the ongoing industrial transformation, digitization of production processes, and the emergence of Artificial Intelligence (AI) use cases, enterprises across all different verticals show an increased interest in optimizing the utilization of their data. In an industrial context, this translates to the need to utilize data across different domains for predictive analytics and forward-looking decision-making.

Looking closer at how data management and data integration were discussed at IMTS 2024, it is apparent that data fabric is, by far, not the only approach discussed within modern enterprises today. However, data fabric is the most advanced and holistic approach to enterprise data integration. It creates a unified data management platform, integrating data from different systems and heterogenous sources, as well as data formats for further processing and Artificial Intelligence (AI) model training. The interest is high now to understand exactly how to leverage these capabilities to process and, ultimately, drive industrial enterprises' business outcomes. At IMTS 2024, both Amazon Web Services (AWS) and Microsoft presented their data fabric offerings (AWS Industrial Data Fabric and Microsoft Fabric). Google Cloud showcased the capabilities of its Manufacturing Data Engine, which is headed in the direction of a data fabric deployment.

Most data management and integration discussions at IMTS, however, were not advanced enough to center around platforms like data fabric (or similar data management and integration solutions). Instead, industrial enterprises discussed the need for data storage, analytics, and-to a considerably lesser extent-integration solutions only in as far as it concerns actual and concrete business-related and/or operational challenges. To that end, the discussion on the show floor-outside of the booths of AWS, Microsoft and Google Cloud-centred around best effort approaches to identify 1) the most pressing business challenges and 2) short-term automation solutions. These are primarily driven by Operational Technology (OT) companies, which are enjoying a considerable incumbent vendor advantage in the industrial space, underpinned by Data Operations (DataOps) and proprietary management solutions, such as Siemens' Mcenter.

Earlier in the year, ABI Research conducted its first wave of an extensive multi-client survey among more than 450 manufacturers in the United States, Germany, and Malaysia (see ABI Research's key takeaways) that identified the need to "remove data silos" as one of the four most important technology challenges hindering their digitization initiatives. Against this background, when it comes to day-to-day activities, manufacturers still largely rely on data storage and management solutions that uphold and reinforce these data silos, instead of overcoming them. In other words: their short-term actions undermine their long-term strategies.

The fact that manufacturers decide to go for short-term automation solutions that reinforce, instead of erase data silos is an important lesson for data fabric vendors. After all, no enterprise willingly and knowingly opts for short-term data management solutions that undermine their long-term strategic digitization initiatives and goals. Rather, data fabric vendors need to bear in mind the short-term requirements of industrial enterprises and account for the fact that a large proportion of them currently has no coherent data strategy in place and considers this as a main barrier to deploying data fabric.

• Integration & Interoperability: Industrial enterprises often use a multitude of different technologies, legacy data storage, and Internet of Things (IoT) devices. To account for this, data fabric vendors need to underline how their solutions can connect to legacy systems and communicate with other devices and industrial automation equipment, without extensive reconfiguration. To win the trust of manufacturers and other industrial enterprises, hyperscalers should communicate clearly how their (data) fabric offering interoperates with widely recognized industrial protocols, like OPC UA or MQTT and Application Programming Interfaces (APIs). To show this in practice, hyperscalers and cloud provider should look at designing reference architectures and blueprints to demonstrate successful integration with legacy industrial systems and IoT devices.
• Scalability & Flexibility: Vendors will also need to understand that when (manufacturing) enterprises deploy new technology, they often start small and grow the deployment with time. Consequently, data fabric vendors should emphasize their solution's ability to scale in line with enterprise requirements. Hyperscalers and cloud service providers should, therefore, look at providing their data fabric in a modular architecture that scales with increasing data volumes, user bases, and sources of data over time. In addition, they should emphasize hybrid cloud capabilities to allow for easy scalability of the data fabric, while also safeguarding enterprise data integrity. Designing a tiered offering for data fabric can help hyperscalers standardize this scalability in some form.
• Security & Compliance: Security is the most important concern for enterprises-particularly in an industrial context.Not only should vendors develop and implement stringent security measures, but they should also focus on educating enterprises on the security measures in place, and the integrity and protection of valuable enterprise data. Clear documentation of compliance procedures, as well the reference toward specific industry regulations (e.g., the National Institute of Standards and Technology (NIST), International Organization for Standardization (ISO) standards, and others) can be helpful assets in this context.
• Business Value & Use Cases: Finally, vendors need to understand that enterprises face budgetary controls that require these enterprises to carefully justify any investment in data fabric. Consequently, hyperscalers and cloud service providers should target messages that resonate with implementing enterprises. In the manufacturing context, they should clearly outline how their data fabric solution improves Overall Equipment Effectiveness (OEE), elevates the quality of output, and enhances Environmental, Social, and Governance (ESG) performance. Most investment decisions-particularly in manufacturing-are driven by Return on Investment (ROI) considerations, so data fabric providers should develop tools and calculators to help enterprises quantify the value.