Unify Your Operational Data: How Manufacturers Are Building a Modern Data Foundation

Data and Analytics leaders at manufacturing enterprises today are pursuing three critical data initiatives: modernization of their data infrastructure, implementation of advanced analytics driven by a central, real-time source of truth, and automation of data engineering processes.

These initiatives have become increasingly urgent as manufacturing leaders are pushed to transform operations by leveraging their data. They must optimize supply chain efficiency by leveraging Just-In-Time Analytics and improve customer outcomes with reporting powered by live supply chain data. and enable predictive maintenance and smart manufacturing improvements by leveraging cloud, on-premises, and Internet of Things (IoT) data. Additionally, they need to maximize marketing spend effectiveness while resolving data discrepancies across teams to create a single source of truth for enterprise-wide analytics and production optimization.

What holds data and analytics teams back

The path to achieving these objectives is often blocked by three significant challenges: vendor lock-in, fragmented and decentralized data access, and complex homegrown integration architectures. Data teams face the challenge of transforming large volumes of operational data into actionable insights while navigating complex technology ecosystems that do not play well with each other. From legacy ERPs to modern cloud platforms and Manufacturing Execution Systems (MES), manufacturers need to modernize their infrastructure while maintaining business continuity.

Our work with data leaders in manufacturing has shown us that these teams operate in hybrid environments, with mission-critical data spread across systems:

• Legacy on-premises systems (DB2, Informix, Teradata)
• Modern cloud data stores (Snowflake, Google BigQuery, Databricks)
• Enterprise operational systems (SAP, Microsoft Dynamics, Workday)
• Manufacturing Execution Systems (MES)
• Product Lifecycle Management (PLM) systems
• Quality Management Systems (QMS)

The growing fragmentation of the enterprise’s data assets adds to the challenge of delivering operational efficiency through a modern, centralized, automated data infrastructure.

Vendor lock-In

Recent changes to SAP's data access policies, including restrictions on third-party access to the operational data provisioning (ODP) framework, have highlighted the risks of depending too heavily on any single vendor's ecosystem. This dependency can severely limit an organization's ability to innovate and adapt to changing business needs.

For one, it prevents data teams from adopting best-in-class technology and forces them into the toolset of a particular ecosystem – even if it isn’t a good fit for the organization’s needs. Secondly, it creates a situation where organizational data is held hostage if the company chooses to switch to a more modern, capable solution – compromising business continuity. Finally, it allows vendors to impose consumption-based pricing for data extraction – meaning that manufacturers must pay a data tax to fully leverage advanced analytics and AI.

Fragmented data access

Manufacturing organizations struggle to create a single source of truth for enterprise-wide analytics and production optimization. Traditional approaches often result in data silos, making it difficult to enable real-time analytics and Just-In-Time decision-making across the supply chain. Common problem areas include connectivity bridging the cloud/on-premises divide, between legacy and modern systems, and across ecosystems (like Microsoft, AWS, SAP, etc.).

The challenge isn't just about moving data—it's about maintaining business continuity while modernizing your technology stack. Data leaders need to avoid or release the proprietary hold on them with a few strategies:

Decouple data connectivity from operational systems, opening up opportunities to modernize your tech stack without disrupting operations. This frees your organization from vendor lock-in and having your data held hostage by any one ecosystem.

Bridge the gap between legacy and modern platforms to ensure business continuity across disparate ecosystems.

Standardize and automate data ingestion processes, to efficiently populate your data stores, quickly realize the value of your investment in data warehouses like Snowflake and remove reliance on cumbersome bespoke pipelines driven by custom-code and tech debt.

Enable real-time analytics across all data sources, so that all decision-makers and analytics developers across the organization are working from the same single source of truth.

Building a future-proof data foundation

The key to successful modernization lies in creating a standardized, automated approach to data integration that addresses three critical areas.

First, implementing a decoupled architecture creates the flexibility to support modernization efforts. This approach abstracts integration logic from both operational data systems, enabling organizations to implement microservices-based connectivity for real-time production monitoring. This allows organizations to freely adopt new technologies without disrupting existing workflows.

Standardizing data access is the next milestone in achieving a modern data architecture. By creating a single, governed access point for all data sources, including shop floor data, organizations eliminate cumbersome manual export processes. This standardization enables self-service analytics while maintaining control and ensuring a single source of truth. As a result, production performance dashboards can deliver at-a-glance insights for resource allocation and operational attention.

The final component involves automating data ingestion processes. By replacing custom ETL code with automated processes, organizations can implement efficiencies like change data capture (CDC) and schema change detection. This automation establishes a standardized approach to data ingestion and creates a single, repeatable pattern that works across all operational systems - from shop-floor sensors (IIoT) to enterprise applications both in the cloud and on-premise.

Real-world impact: modernizing manufacturing data infrastructure

The benefits of CData's connectivity approach are already transforming operations across the manufacturing sector. Global pharmaceutical leader Recordati successfully broke free from SAP vendor lock-in while maintaining strict European privacy compliance. Their implementation enabled seamless integration between SAP ERP, HANA, and Salesforce, creating a flexible foundation for future data initiatives.

Similarly transformative results were achieved by BI and data architecture leaders at life sciences manufacturer Repligen, who reduced their Snowflake implementation and data ingest timeline by an entire year. By automating 80% of their data pipelines, they realized seven-figure savings through improved forecasting and inventory optimization. Their success extended beyond operational efficiency, creating executive dashboards that drive strategic decision-making from the shop floor to the C-suite.

Manhattan Associates' experience further validates this approach. Faced with challenges replicating custom data and escalating costs, they replaced their existing data ingest strategy with automated CData Sync pipelines. The solution not only handled their complex data requirements but also provided a predictable cost structure (while processing over 500 million rows monthly). As their Senior Data Analytics Manager Anthony Neu noted, “I don't want to think about things I don’t have to think about. I want a thing that does what it does and does it beautifully. That’s CData.”

These success stories demonstrate how manufacturers can modernize their data infrastructure and maintain business continuity, achieving both operational efficiency and strategic insights.

The path forward

As manufacturing organizations continue their digital transformation journeys, maintaining control over their data strategy becomes more important than ever. Establishing a strong data connectivity foundation gives data teams at these organizations the control to adapt to changing needs while maintaining business continuity. It also allows them to standardize ingest and data access, which creates a single organizational source of truth that powers data-driven decisions across all levels, in initiatives like predictive maintenance, supply chain and production optimization, and sales forecasting.

Focusing on these principles allows organizations to build a data foundation that supports what they need now and set themselves up for successful future innovations—whether that's implementing AI for predictive maintenance, optimizing supply chains, or delivering better customer experiences.

The key to success in today's dynamic business environment isn't just having the data—it's having the right architecture that makes their data accessible, actionable, and adaptable to changing business needs. Manufacturers can position themselves for success in an increasingly data-driven world by taking control of their data strategy.

Build a modern data foundation with CData

Learn how CData can help you build a modern data foundation while maintaining control of your technology stack. Contact us for a no-obligation consultation.