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Maximizing Analytical Capabilities of Your SAP with Google Cloud Without Migrating Your Data using Planning in a Box

January 2, 2024 | Asheesh Gupta

Blog / Maximizing Analytical Capabilities of Your SAP with Google Cloud Without Migrating Your Data using Planning in a Box

In the digital age, data is the lifeblood of business decision-making. For companies reliant on SAP systems, expanding their analytical capabilities without jeopardizing the integrity of their data is a critical challenge. Google Cloud presents a compelling solution with its diverse ecosystem that enhances SAP with a wealth of data sources and analytical tools.

Google Cloud’s advanced analytics tools offer SAP users access to an array of data sources that were previously out of reach. Imagine tapping into market behavior through Google Trends or integrating comprehensive weather data to anticipate market changes. 

The integration doesn’t stop with data sources. Applications like Google Ads and platforms for Data Analytics, ML, and AI, including Generative AI, are readily integrable with SAP, offering a broadened scope for analytics and decision-making.

Recent Breakthroughs in BigQuery – Datasphere Interoperability

The recent announcement about Datasphere and BigQuery interoperability is a game-changer. This new functionality means SAP users can now access and analyze Google data directly from SAP Datasphere, opening up new avenues for accelerating business outcomes based on connected insights across SAP’s, Google’s, and other datasets.

As Ke Ma elaborates in his blog, you can either replicate your data, creating a copy known as “export” or “replication,” or you can opt for data federation, using the data directly from its original source. 

What is Data Federation?

Data federation works by allowing direct access and querying of data from its original location. In practical terms, this means instead of copying or transferring data across systems, you use it where it resides, ensuring real-time accuracy and reducing the risks associated with data movement.

For SAP users, data federation takes on added significance. SAP’s intricate ecosystem is deeply embedded in an organization’s operations, making the prospect of moving data potentially disruptive. By integrating data from SAP Datasphere with Google BigQuery services through data federation, businesses can enrich their insights without compromising the integrity of their core systems. 

Queries are federated via virtual tables in SAP Datasphere, meaning the data is accessed but never physically moved or replicated. This method ensures that the data remains in its source system, upholding its context and reliability, which is essential for accurate analysis and decision-making.

Setting the Stage for Advanced Analytics 

In her blog, Sangeetha Krishnamoorthy outlines a strategic approach for businesses to enhance data analysis and decision-making by integrating SAP systems, like SAP S/4HANA and SAP BW/4HANA, with Google Cloud’s BigQuery. This strategy involves using connectors to federate and replicate data to SAP Datasphere, where it becomes a pivotal resource. Once in Datasphere, businesses can enrich their SAP data by incorporating additional insights from Google Analytics, Google Ads, and other sources into BigQuery, creating a robust environment for advanced reporting through SAP Analytics Cloud.

This integration can be further enriched by the Google Cloud Cortex Framework (as we will explore next). This framework not only accelerates insight generation by offering packaged data analytics content for common business scenarios but also ensures seamless data federation from BigQuery into SAP Datasphere. Leveraging the Cortex Framework, organizations can navigate market shifts and exploit new opportunities, making informed decisions based on a comprehensive view of their SAP and non-SAP data.

Maintaining SAP Data ‘Context’ Outside SAP Environment with Google Cloud Cortex Framework

When it comes to blending SAP data with non-SAP data, there is one major roadblock. As Ke Ma points out, exporting data from the SAP landscape often means losing its inherent context. In external environments, recreating this context requires extensive manipulation — using joins, aggregations, calculations, and more — to replicate SAP’s application logic. This process isn’t just challenging; it’s also resource-intensive and costly. 

The Google Cloud Cortex Framework addresses these challenges head-on, offering a more streamlined and effective way to extract value from SAP data. It provides a comprehensive suite of tools and resources, including reference architecture patterns for seamless integration and a collection of deployable solution accelerators. These accelerators are designed for rapid data processing and modeling within BigQuery, laying the groundwork for advanced analytics and AI at scale. With the Cortex Framework, you can quickly transform your SAP data, blending it with external datasets and deriving actionable insights.

Pluto7: Your Partner in SAP Data Transformation

At Pluto7, we specialize in making this integration seamless and strategic. Our solutions are designed to bring the power of Google Cloud’s analytics, machine learning, and AI to your SAP data without uprooting your current systems.

In line with the Google Cloud Cortex Framework’s advanced capabilities, our offerings, including Planning in a Box, CXSense, Konnect Manufacturing, and Demand ML on SAP BTP, are engineered to address critical challenges such as excess inventory, inefficient demand sensing, and manufacturing waste.

Planning in a Box: Rapid Supply Chain Optimization with SAP and Google Cloud

Planning in a Box, a Cortex-enabled decision intelligence platform, transforms supply chain management by integrating with SAP and leveraging Google Cloud’s advanced analytics. It focuses primarily on inventory optimization, offering an innovative approach to managing and optimizing your supply chain. By harnessing the solution accelerators within the Google Cloud Cortex Framework, Planning in a Box provides rapid business value, allowing businesses to see tangible results in 4 weeks or less.

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Demand ML on SAP BTP for Demand Sensing Using Federated Learning 

Demand ML on SAP BTP addresses demand sensing by connecting Datasphere and BigQuery using Federated Learning. This approach allows for deep, insightful analysis without moving your data out of its secure SAP environment. Demand ML blends SAP data with 250+ external demand signals including Google Trends and Google Adtech Data, enabling businesses to accurately predict market needs and respond effectively.

Example: Enhancing Retail Store Productivity with Cortex Data Foundation

A leading retailer was struggling with maintaining optimal inventory levels due to unpredictable consumer demands and rapidly changing market trends. Their reliance on manual processes and isolated data systems resulted in frequent overstock and stockouts, impacting profitability and customer satisfaction. 

Using Planning in a Box, we deployed Cortex Data Foundation for SAP content rapidly into the client’s Google Cloud BigQuery environment.  Key implementations included:

  • Integration of SAP-aware data models providing deep insights into products, orders, inventory, and supplier lead times.
  • Incorporation of POS data and AI-driven shelf analysis tools for actionable intelligence.
  • Real-time demand sensing through the integration of external signals like weather and social trends.


  • Gained a 360-degree view of shelves, products, margins, and order frequencies. 
  • Integrated external signals like market trends, weather conditions, and real-time shelf consumption for short-term market forecasts.
  • Enabled an always-updated snapshot of inventory levels, reducing response times to changes.
  • Unlocked new insights into customer behavior and market trends, informing better decision-making.
  • Seamlessly connected SAP and non-SAP data for a holistic view of supply chain operations.

Our approach is not theoretical; it’s proven in the field. Retailers have used our solutions to transform store optimization, combining SAP data with insights from POS and AI to improve demand forecasting (as shown above). Similarly, by integrating data across SAP and non-SAP systems, businesses have gained unprecedented visibility into their inventory, enabling better forecasting and stock management.

Integrating SAP with Google Cloud’s advanced analytics doesn’t have to be a leap into the unknown. With Pluto7’s expertise and solutions, you can make this transition confidently and strategically. As you prepare for 2024, consider how this integration can not only protect but enhance your investment in SAP, turning your data into a source of powerful, actionable insights. Ready to start your journey? Reach out to us and explore how we can transform your business together.


Asheesh Gupta is the Head of Enterprise Architecture at Pluto7, bringing over 20 years of experience in technology consulting, system integration, and service delivery. He excels in architecting and delivering data-driven solutions on hyperscalar cloud platforms like GCP and SAP BTP. With a focus on supply chain optimization through digital transformation and AI, Asheesh’s work at Pluto7 continues to drive value and innovation in enterprise technology.

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