Digital Ecosystem Adoption Outpaces Real Data Collaboration
Industrial executives overwhelmingly agree that digital ecosystems are now central to competitive strategy. According to new research from AVEVA and IMD Business School, 74% of leaders rank digital ecosystems as a top strategic priority. Yet only 27% report that they share data substantially or extensively with ecosystem partners, exposing a striking disconnect between ambition and execution in industrial data sharing. The study, based on more than 275 interviews across 12 sectors, highlights how organisations want to use connected platforms to innovate faster, manage volatile supply chains and accelerate decarbonisation. However, this vision of end‑to‑end, real‑time collaboration remains elusive in practice. Instead, many manufacturers and infrastructure operators still operate with fragmented data flows that limit the value of ecosystem participation and stall progress towards truly connected industries.
Industrial Intelligence and the Promise of the OT Data Fabric
The AVEVA–IMD report frames “industrial intelligence” as the capability to integrate operational technology (OT), information technology (IT) and artificial intelligence (AI) to drive decisions across entire value chains. In theory, this points toward an OT data fabric: a unifying layer that connects machines, plants, partners and platforms into a coherent digital ecosystem. With such an architecture, industrial data sharing could become seamless, enabling real‑time optimisation, safer operations and more resilient supply networks. Leaders are investing in distributed, edge‑to‑cloud architectures to move in this direction, hoping to replace rigid point‑to‑point links with flexible data services. Yet the research shows that, despite this architectural shift, most companies have not translated these investments into extensive partner‑level collaboration. The OT data fabric remains more of a strategic aspiration than a fully deployed backbone for ecosystem‑wide intelligence.
Manufacturing Data Silos: The Stubborn Barrier to Ecosystem Value
The gap between digital ecosystem adoption and actual collaboration is largely explained by persistent manufacturing data silos. Legacy systems, accumulated over decades, lock critical process and asset information into proprietary formats and isolated databases. Integration complexity makes it difficult to bring OT data into shared platforms without disrupting operations or creating new security and governance risks. The AVEVA–IMD study highlights weak governance as another structural barrier: without clear rules on ownership, usage rights and accountability, partners hesitate to open up sensitive operational data. As a result, even organisations that deploy edge‑to‑cloud solutions often recreate silos in the cloud rather than eliminating them. The technical groundwork may be in place, but inconsistent data models, fragmented responsibilities and compliance concerns keep many industrial firms from progressing to truly extensive industrial data sharing across their ecosystems.
From Operational Connectivity to Strategic OT Data Sharing
Executives interviewed in the report emphasise that ecosystems are already delivering operational value, but the next step is turning connectivity into strategic advantage. AVEVA’s CEO Caspar Herzberg argues that governance, integration and learning now matter more than algorithms, underscoring that technology alone will not dissolve manufacturing data silos. IMD’s Professor Michael Wade notes that industrial sectors have long collaborated out of operational necessity; what is new is the potential to transform those relationships into real‑time, intelligence‑driven systems. To close the 74% versus 27% gap, organisations must define clearer roles, shared standards and robust data‑sharing frameworks that partners can trust. This means treating the OT data fabric as a multi‑company capability, not just an internal IT project. Firms that succeed will be better positioned to orchestrate ecosystems, respond to disruption and capture new sources of value from connected operations.