In today's interconnected world, businesses face multifaceted challenges that demand informed and agile decision-making. Geopolitical uncertainties, regional conflicts, unpredictable trade barriers and sudden tariff changes to not just disrupt global supply chains but completely reshape them at an unprecedented rate. Central to navigating these complexities and achieving the most optimal outcomes is data — the lifeblood of modern enterprises. Considering that 84% of global commerce is generated by SAP customers , it is only natural that the solution to current challenges lies in data generated, managed and governed by them.
According to a 2024 survey, 80% of global supply chains were disrupted over the previous 12 months with most organizations experiencing between one and ten disruptions. The impact of such disruptions can be reduced up to 30 percent if timely actions are taken.
Analyzing modern supply chains requires data from not just SAP sources, but from a diverse set of data sources such as supplier IT systems, shipping and transportation sources like vessel details, route information, and cargo types, followed by POS systems, IoT devices, and weather data. Effectively integrating and analyzing data from SAP alongside these external sources enables enterprises to mitigate risks associated with supply chain disruptions, inventory challenges, and equipment failures, leading to enhanced operational efficiency and profitability.
Evolution of SAP Analytics and Modern Challenges
For more than two decades, analytics in the SAP world has been driven by on-premise products that primarily relied on SAP ERP systems as the primary source of data. These products were designed for an SAP-first world where the primary goal was to optimize and provide insights into SAP ERP business processes. While SAP did adapt to the cloud by offering SaaS options, many of their customers continued to export data from SAP landscapes into other cloud data platforms that provided heavier data engineering capabilities along with functionality for artificial intelligence, machine learning and peta-byte scale analytics.
Pivot to SAP Business Data Cloud for Efficiency
The launch of SAP BDC opened several possibilities for companies balancing two separate analytics landscapes – one for SAP data and one for an all-encompassing Data Lakehouse that enables large-scale data processing, storage and AI/ML. The integration between SAP data models in SAP BDC using Databricks is enabled through Delta-Sharing, an open protocol for secure and efficient real-time data sharing without copying or physical replication. SAP BDC combines the best of both companies, giving organizations multiple options to enhance productivity and efficiency.
Leveraging the Potential of Next-Gen Data Integration
The SAP BDC integration opens several possibilities for businesses including:
- Unifying data silos by consolidating data across SAP and external sources for comprehensive insights.
- Leveraging real-time analytics by integrating operational and analytical workloads for timely decision-making.
- Enabling AI and ML capabilities using Databricks' advanced analytics for predictive intelligence and Gen AI use cases.
- Improving data governance and access control across enterprise data, ensuring compliance and security.
Most industries will benefit from the implementation of real-world scenarios which are easier to realize with accurate implementation of SAP BDC:
1. Supply Chain Resiliency in Consumer Goods
In the fast-moving consumer goods (FMCG) industry, supply chain disruptions can lead to significant financial losses. By integrating SAP transactional data with external sources like weather forecasts, port congestion data, and real-time transportation updates in Databricks Lakehouse, companies can predict and mitigate supply chain bottlenecks before they occur. This proactive approach enhances resilience, reduces stockouts, and optimizes logistics costs.
2. Demand Forecasting in Retail
Retailers struggle to predict demand fluctuations influenced by seasonality, consumer behavior, and macroeconomic trends. By merging POS data, historic sales and distribution data with social media sentiment analysis, market trends, and regional economic indicators in Databricks Lakehouse, businesses can refine demand forecasting models using AI/ML capabilities. This leads to optimized inventory levels, reduced waste, and improved customer satisfaction.
3. Predictive Maintenance in Manufacturing
Manufacturers often face unplanned downtime due to equipment failures, impacting productivity and profitability. By integrating SAP Plant Maintenance and SAP Quality Management data with IoT sensor data in Databricks Lakehouse, enterprises can develop predictive maintenance models that provide early warning of equipment failure, allowing for timely interventions that extend asset lifespans and reduce operational costs.
The Future of Intelligent Enterprises
Integrating SAP BDC and Databricks unlocks new dimensions for business intelligence. As industries evolve, enterprises that embrace unified, cloud-native, and AI-ready data architecture will be able to better manage disruptions and stay ahead of the curve. However, migration to such a landscape requires industry expertise, knowledge of SAP ERP and S/4HANA business processes, technical competency of legacy and cloud SAP analytics products and a deep understanding of AI and GenAI solutions.
