For an efficient Supply Chain network, it’s crucial for an organization to amalgamate all the critical data sources to maintain control over its supply chain and manufacturing operations. An isolated first-party data housed in various SAP and non-SAP systems without any integration with second-party data and without any correlation with macro and micro-economic market factors will invariably result in sub-optimal supply chain operations. This fragmentation often culminates in high supply chain and manufacturing costs, imbalances in the demand-supply pivot, and reduced resilience to supply chain disruptions.
Over the years, numerous organizations have generated a vast amount of high-quality data within their SAP and various other siloed systems. However, this data is primarily utilized for reporting purposes, neglecting its true value.
This data holds incredible value for enterprises beyond just reporting. It aids in making real-time decisions to forecast demand, manage inventory, fulfill customer orders promptly, and run production operations optimally.
By blending first-party data (from SAP and other ERP systems) with second-party data (supplier and partner data), and third-party data (industry data, competitor data, paid/unpaid data from industry, market, government, analysts, etc.), enterprises can derive a plethora of actionable data insights. These insights enable businesses to make intelligent, data-driven decisions.
However, many organizations, despite having the best intentions, fail to optimize their operations and, subsequently, their profitability. This is primarily due to a lack of a robust data foundation capable of integrating data from all potential sources that could impact the business.
A solid data foundation is paramount for enabling full AI/ML functionalities, such as sophisticated predictive analytics algorithms or generative deep learning capabilities. These functionalities unlock insights and abilities that traditional technologies cannot achieve.
The recent announcement by SAP and Google Cloud at Sapphire represents a significant stride in this direction. Enterprises can now effortlessly bring in their SAP ECC/ SAP S/4HANA and other SAP LoB data through BTP native integration capabilities.
This negates the need to spend months figuring out the integration architecture and integration products. SAP and Google Cloud have announced a partnership where the integration between
SAP Datasphere and Google BiQuery will facilitate Digital Supply Chain transformation for clients.
High-Level Architecture leveraging SAP Datasphere and Google Cloud Services
The combined Datasphere and Google BigQuery architecture will allow enterprises to natively bring all their data from the SAP ecosystem, like ECC, S/4HANA, IBP, Ariba, SuccessFactors, and other SAP Line of Business products. It will also incorporate practically all their second and third-party data through Google BigQuery. This approach ensures enterprises focus on identifying critical data sources that impact their business operations rather than worrying about how to ingest and model them.
This architecture benefits from several industry-leading, cutting-edge engineering capabilities introduced by SAP and Google Cloud, such as:
- SAP Datasphere’s data federation capabilities offer a streamlined integration with Google BigQuery. This integration enables shared learning across various data nodes, all while upholding rigorous standards of data privacy and security. This approach bypasses traditional data copying or moving processes, thereby considerably reducing data latency and preserving the semantics and business context of the data. The implications are impressive: improved performance, lower need for additional computational resources, faster processing, and cost-effectiveness.
- SAP Datasphere and Google BigQuery can host petabytes of data and provide the ability to process this data in seconds.
- Google Cloud’s state-of-the-art Vertex AI platform hosts sophisticated and robust predictive and generative AI algorithms engineered by Google. These algorithms can be further customized and fine-tuned as per the organization’s needs.
- Native integrations built by SAP and Google to bring in data from practically any data source in real-time or in batches.
- FedML Python Library facilitates instant access to real-time data via SAP Datasphere’s unified data models for model training and ML flow in VertexAI and also to bring the results back to SAP applications.
- Over 250 public datasets in Google BigQuery, Google Trends, or Google Ad Tech data can be used to improve demand forecasting, merchandise planning, and other supply chain processes.
- Capabilities to effortlessly blend multiple data sources either in SAP Datasphere, Google BigQuery, or both.
- Strong and secure ML Ops capabilities to ensure that models are consistently up-to-date, trained, and learning from the latest data with seamless dataflow.
- The ability to visualize and plan ML-derived forecasts in the user interface of enterprise choice not only provides businesses with instantly actionable intelligence but also does so within the comfortable and familiar SAP ecosystem that many organizations already operate within.
- Flexible platform capabilities powered by the Glassbox methodology, where enterprises have complete visibility and accessibility to the ML/AI code. This is a stark shift from various SAS-based architectures with no or limited access to AI algorithms and code.
- Strong platform capabilities such as Data Governance, Data Quality, Master Data Management, and Data Compliance.
With the combined SAP BTP/Datasphere and Google Cloud architecture, a crucial business need is addressed: establishing a scalable, robust process for integrating external insights into SAP systems. It propels enterprises closer to the future of business intelligence, where data from multiple sources can be seamlessly integrated, and advanced Gen AI, LLM, and AI/ML models can provide real-time insights.