Unlocking Business Value with Hyperconnected Data Products

An organization’s business strategy is intrinsically linked to its data strategy in the current landscape, where customer demands, supply chains that require predictive insights, and the drive for intelligent automation all depend on the real-time availability of data. However, critical operational data remains sequestered within ERP systems, such as SAP, for many enterprises, creating a data latency gap that hinders execution and competitive agility.

According to a blog by SAP partner Onibex, a fundamental paradigm shift is required to move from a process-centric view of data to one centered on hyperconnected data products, in order to bridge this gap.

From Data Byproduct to Strategic Asset

Traditional data models view data as a byproduct of business processes such as order-to-cash. It is accessed through periodic batch extractions or complex, tightly-coupled API integrations, resulting in stale data and brittle systems that stifle innovation.

However, the shift to hyperconnected data reframes data as a strategic, reusable asset. Instead of focusing on single-use-case integrations, it creates governed, real-time data products.

Under this approach, a data product is a ready-to-consume, self-describing, and high-quality data package that represents a core business entity, such as a customer or product, or a logical entity, such as available-to-sell inventory. When made available through a centralized, event-driven streaming platform, these assets become hyperconnected, enabling a decoupled, publish/subscribe architecture that enhances scalability and business agility.

Core Applications

This architectural shift directly enables tangible business outcomes across three key pillars:

1. Real-time experiences: By streaming live data products, organizations can provide accurate, up-to-the-second information to customer-facing applications. A primary example is providing a precise, real-time view of inventory to e-commerce platforms, thereby preventing stockouts and enhancing customer satisfaction.
2. Predictive insights: Continuous streams of operational data from sources such as SAP and IoT sensors can be leveraged to power advanced analytics. In manufacturing, this enables predictive maintenance models that anticipate equipment failure, thereby minimizing costly downtime.
3. Intelligent process automation: Data products can orchestrate and automate complex workflows across disparate applications such as ERP, CRM, and MES. This reduces manual effort and operational costs in areas ranging from financial reconciliation to inventory management.

Ultimately, the transition to a data-product model is a gradual and agile one. It begins by identifying a high-impact use case, delivering an initial data product to prove its value, and scaling the architecture incrementally across the enterprise.

What This Means for SAPinsiders

Accelerate connectivity with pre-packaged solutions. The complexity of SAP’s data structures can be a major hurdle to modernization. Platforms like Onibex OneConnect simplify this process by providing pre-packaged, real-time connectors for SAP ECC and SAP S/4HANA that integrate with modern streaming platforms, such as Kafka. This approach translates complex SAP terms into business-friendly ones, bypassing the need for extensive custom ABAP development. As a result, the organization can begin streaming data products in a fraction of the time required by traditional methods.

Shift from being a gatekeeper of data to being its enabler. To build an actual digital enterprise, the role of the SAP professional must evolve. This is because the goal is no longer just to maintain the ERP’s stability, but to democratize its data. This means focusing on creating and governing reusable, real-time data products that the entire business can consume. By adopting this mindset, an SAP professional becomes a crucial enabler of the real-time analytics, AI, and automation initiatives that define a modern digital business.

Embrace real-time, event-driven architectures. The data integration market has moved decisively away from nightly batch ETL. Today’s dominant trends include real-time data streaming through technologies like Change Data Capture, the adoption of Data Mesh principles where business domains own their data products, and the rise of composable, low-code integration platforms. For SAPinsiders, this signals a critical shift in skills from legacy development tools, such as IDoc or BAPI, toward expertise in modern, event-driven architectures to meet enterprise-wide data demands.