Acquisitions and Partnerships Among Data Management Vendors Create Opportunity for Manufacturers to Develop Advanced Data Analytics Capabilities

Manufacturers will generate 4.4 Zettabytes (ZB) of data annually by 2030, yet companies continue to struggle with effectively managing and utilizing operational data. Only 30% can perform advanced analytics such as prescriptive analysis. This imbalance underscores the top two operational challenges for manufacturers: improving quality and equipment performance, as the vast amounts of data being collected persistently lack actionable insight. Consequently, data management software providers are advancing partnerships to supply more robust data fabric solutions.

Over the last 12 months, data management platforms have improved the lack of actionability from manufacturing data by bolstering connections with partners across the industrial ecosystem: Siemens announced a strategic partnership with Snowflake, an Artificial Intelligence (AI) cloud integration platform; Emerson acquired AspenTech, including data management and analytics software AspenTech Inmation (see ABI Insight “A Robust Data Fabric Is Essential for Deploying Next-Gen Technologies like AI, and Emerson Is Delivering This Architecture to TotalEnergies Through AspenTech Inmation”) and Aspen Mtell; and HighByte, an industrial data operations software provider, deepened its multi-year partnership with Amazon Web Services (AWS) by natively integrating Amazon S3 Tables into HighByte Intelligence Hub to make it easier for companies to build an open data lake for managing and analyzing industrial data.

These strategic moves underline the race to shape the industrial data infrastructure scene and close the gap between problem identification and resolution. This gap exists because manufacturers lack the tools to centralize and navigate the data they collect, which is essential for understanding and improving operations. The partnerships among industrial data solution providers create a landscape that allows more customers to achieve the data infrastructure and access the tools needed for advanced analytics.  

Manufacturers that lack the capacity for advanced analytics must seek data management options to stay competitive in product quality and demand responsiveness. The benefits of integrated and contextualized data are clear: simplified sharing and access, deeper and quicker insights, improved operational efficiency, and faster time to impact. Given that advanced analytics capacity remains limited across the industry, those looking for differentiation must act decisively.

Those that have a data infrastructure that enables advanced analytics should take the next step: utilizing AI and automation tools. Advanced analytics are a practical use case for AI, and manufacturers must look to utilize similar AI capabilities that are demonstrated across platforms like Siemens Insights Hub, HighByte Intelligence Hub, and Aspen Mtell. These platforms provide different degrees of Generative and Agentic AI to enable real-time data analysis and support predictive and prescriptive analysis. For instance, Siemens’ Insights Hub offers a Generative AI (Gen AI) assistant, Production Copilot, that can access real-time data and direct users to the exact source of dysfunction occurring on the production line. HighByte provides Gen AI for tag mapping and data contextualization with a configurable AI agent that uses those contextualized data to automate specified tasks. Aspen Mtell uses AI-powered and automated agents to detect anomalies, predict asset failures, and recognize degradation signals, while enabling custom configuration for company-specific workflows. Generally, AI rapidly navigates data and reduces the need for technical expertise to extract insights, making it a key tool for scalability.