How SAP BDC and BigQuery Help Turn SAP Data Into Business Insight

SAP customers are under pressure to modernize both their ERP environments and the way they use enterprise data. Those efforts can move on separate timelines: data modernization does not have to wait for a core ERP migration, and existing data assets can already support analytics, automation, and AI.

For organizations that are migrating, however, the program creates a broader opportunity. Moving data out of legacy systems is no longer enough. SAP customers can use the transition to improve how that data is managed and reused, so teams can act on it with more confidence and speed.

SAP Business Data Cloud and Google Cloud’s BigQuery give customers a way to connect SAP business context with broader enterprise and external data, whether or not a migration is underway. With SAP BDC Connect for BigQuery, SAP data products can be shared with BigQuery without copying and combined with sources such as third-party logistics feeds, Google Maps geospatial data, Google Trends, and public datasets. That creates a data foundation where SAP and non-SAP data can be governed, understood, shared, and used by AI in ways that reflect how the business actually operates.

The Shift Toward Data Value

Fragmented data creates friction the business feels directly. Finance teams wait for reconciled numbers, and supply chain teams work from incomplete inventory signals. AI raises the stakes: a dashboard can tolerate some manual interpretation, but an agent or predictive model needs governed, current, context-rich data to make useful recommendations.

Traditional migration programs often measure success by technical completion: the data moved, the system cut over, and the new platform went live. Those milestones are necessary, but they do not show whether the business gained a better data foundation.

A stronger goal for SAP data modernization is creating data value. That means treating important enterprise data as a product: a governed asset the business can use, trust, and reuse across functions.

A data product needs ownership, meaning, quality controls, governance, and a clear understanding of how it supports decisions or workflows. In SAP environments, this is especially important because business context is embedded in processes, hierarchies, master data, transactions, and industry-specific logic.

SAP Business Data Cloud is designed around that idea. SAP data products are built to preserve semantic meaning and business context so analytics and AI can work from a more accurate foundation. BigQuery extends that foundation to the wider enterprise, giving teams one place to analyze SAP data products alongside non-SAP and external data.

Where the Model Changes

SAP and Google Cloud made SAP Business Data Cloud Connect for Google BigQuery generally available in July 2026. The connector provides bidirectional, zero-copy sharing: customers can access trusted SAP business data directly from BigQuery and enrich SAP BDC with Google datasets without creating duplicate copies. It supports SAP BDC instances hosted on Google Cloud and AWS, with Azure support on the roadmap.

The approach helps customers keep data governed and current. Each copy or transformation makes it harder to preserve governance, freshness, and business meaning. Zero-copy sharing still leaves some integration work, yet it changes the starting point from moving everything first to making trusted data usable where it creates value.

It also works with data customers already have. Early adopter ElringKlinger is using the connector for supply chain analytics and conversational AI on top of existing SAP BW/4HANA assets.

This moves SAP modernization beyond isolated reporting projects. When SAP data is combined with non-SAP data, external signals, and AI platforms, the business can ask broader questions. Which customers are at risk if supply constraints continue? Which products are exposed to demand shifts? Which financial forecasts change if a logistics event affects fulfillment? Which operational exceptions need action now?

Answering those questions depends on data that keeps its business context as it is shared across systems. Companies that treat data modernization as its own priority, and use any migration underway to advance it, will be better positioned to turn enterprise data into faster, better-informed decisions.

What This Means for SAPinsiders

• Measure modernization by how data is used. Track whether SAP and non-SAP data becomes easier to govern, combine, and reuse, alongside technical milestones such as cutover. Teams that set these measures early can show business results from a migration or data program and catch gaps before they carry into a new environment.
• Treat critical data as governed products. Assign owners, definitions, and quality standards to the datasets behind key decisions, starting with areas such as finance or supply chain. That gives business, IT, and data teams a shared foundation they can reuse across analytics, automation, and AI.
• Connect SAP context with external signals. Start with a use case where SAP data alone leaves gaps, such as combining order and inventory data with logistics or location data in BigQuery. An early, contained win builds the case for broader use and shows where zero-copy sharing reduces pipeline work.