As enterprises deepen their digital transformation initiatives, the need for a unified architecture to manage and access data across disparate systems is becoming critical. A data fabric—a cohesive data management framework that integrates data from various sources and locations—is emerging as a cornerstone for future-ready enterprises. Yet, implementing a data fabric comes with its own set of challenges.
For example, there is an increasingly diverse use of connectivity solutions in industrial settings, creating fabric deployment complexity. ABI Research forecasts that industrial firms will generate 4.4 Zettabytes (ZB) of data by 2030. A majority of these data—about 56%, or 2.5 ZB—will still be transmitted via fixed-line connectivity. Fixed-line connectivity is chosen for its data integrity, reliability, and availability. However, as connectivity becomes more fragmented with the rise of Wi-Fi, private cellular, Bluetooth®, Low-Power Wide Area (LPWA), and Internet of Things (IoT) technologies, vendors face increasing interoperability demands. This creates complexity for data integration, cloud services, and data fabric providers tasked with unifying these inputs into a streamlined environment.
Our analyst team forecasts that enterprises will spend US$232 billion on data fabric solutions annually by 2030, up from US$107 billion in 2025.
1. Technology and Infrastructure Challenges
Many enterprises operate on a combination of legacy systems and cloud-native platforms. The result is a fragmented technology stack that generates data in different formats, using various protocols and connectivity standards. For data fabric solutions to function effectively in these environments, vendors must:
- Maintain a library of pre-built connectors for popular systems.
- Offer comprehensive Application Programming Interface (API) documentation and developer support for custom integrations.
- Utilize distributed processing engines to support scalable data integration and real-time analytics.
- Provide specialized migration services to gradually replace or integrate legacy systems.
Addressing these challenges will ensure seamless data interoperability, a critical component for any effective data integration strategy.
2. Data Governance and Compliance Challenges
Sensitive enterprise data—whether from manufacturing operations, healthcare records, military, or government systems—requires stringent oversight. Enterprises expect full data lineage visibility and protection from potential breaches or regulatory violations. Therefore, data fabric platforms must:
- Integrate automated data lineage tracking, audit logs, and role-based access controls.
- Offer stringent encryption protocols for secure data storage and transmission.
- Ensure frequent compliance updates to align with evolving global standards such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
Without robust data governance frameworks, enterprises are unlikely to trust or invest in data fabric technology.
3. Operational and Management Challenges
Traditional approaches to data management—such as Extract, Transform, Load (ETL) pipelines and point-to-point system integrations—are inefficient and costly. Data fabrics promise streamlined operations through automation, real-time performance monitoring, and scalable architecture. To fulfill this promise, vendors should:
- Offer plug-and-play modules that support incremental adoption.
- Provide managed services to reduce internal Information Technology (IT) workload.
- Implement built-in alerts for performance issues and data quality degradation.
These operational enhancements reduce overhead while supporting dynamic use cases across manufacturing, logistics, and supply chain management.
4. Organizational Challenges and the Human Factor
Technology alone doesn’t drive digital transformation—people do. One of the most persistent barriers to successful data fabric implementation is the lack of internal expertise and cultural readiness for data-driven operations. To overcome this, ABI Research makes the following suggestions:
- Create detailed onboarding plans, provide virtual training modules, and offer certification courses tailored to different enterprise roles.
- Promote change management initiatives in collaboration with Human Resources (HR) and leadership teams.
- Offer low-code/no-code interfaces to democratize access to data tools across departments.
These strategies enable enterprise-wide alignment and encourage broader data literacy—a necessity for broader use of a fabric solution.
5. Commercial Challenges
Enterprise requirements vary widely by vertical, making it difficult for data fabric vendors to offer both customization and scalability. To address this tension, vendors should:
- Develop modular offerings that allow clients to tailor solutions to their needs.
- Introduce tiered, usage-based pricing models that reflect actual business value.
- Supply Return on Investment (ROI) calculators and industry-specific case studies to demonstrate tangible benefits.
This approach to commercialization not only improves buyer confidence in a data fabric purchase, but also supports long-term scalability and profitability for vendors.
Conclusion
Successfully deploying a data fabric requires navigating complex landscapes, spanning the technological, regulatory, operational, organizational, and commercial lenses. Yet, with the right strategy and capabilities, data fabric providers can empower enterprises to unlock the full value of their data. Enterprises are still in the early stages of their digital transformation journey. They currently only use about 5% of all the data they generate, presenting a lucrative opportunity for technology providers to fill a gap that must be filled for digitalization.
Whether it's integrating with legacy systems, ensuring compliance, or building a data-literate workforce, overcoming the hurdles outlined is essential to harness the promise of data fabrics in manufacturing and beyond.
For further study on data fabric, refer to the following ABI Research content:
- What Is an Enterprise Data Fabric, and Why Is a Hybrid Cloud Approach Vital?
- Data Fabric Use Cases, Benefits, and Examples for Enterprises
- Overcoming Challenges in Bringing Data Fabrics to Industrial Enterprises [AN-6152]
- Key Drivers for Data Fabric Deployments for Industrial Use Cases [AN-6149]
- Technology Requirements for Data Fabric Deployments in Enterprise Verticals [AN-6150]