Overview
Enterprise systems are supposed to enable decision-making, yet too often, they are the very thing that holds organizations back. Nowhere is this more evident than in companies running traditional SAP environments. While SAP has long been the backbone of enterprise operations, its rigid architecture, siloed data structures, and costly extraction processes make it fundamentally incompatible with the demands of modern analytics and AI. Each of these large-scale platforms come with a tax on the business: (1) labor and system costs to move data, and (2) latency costs due to lack of access to data. Each of these costs is tangible and significant.
The question is not whether organizations need to evolve beyond these constraints. The question is how quickly they can do so before competitors leave them behind.
In traditional SAP environments, data is trapped in legacy paradigms stored in proprietary formats, accessible only through cumbersome processes that introduce latency and inefficiency. What should be a seamless flow of insights that powers revenue and profit growth is instead a set of data wrangling processes that frustrate and stymie the business. The time has come for a new approach; a Databricks-centric solution that enables real-time access to SAP data, integrates seamlessly with cloud-native architectures, and leverages AI to drive true business transformation.
The SAP Databricks partnership changes the entire dialogue for enterprises seeking to modernize their data estate, improve business agility, and drive financial impact. Traditionally, SAP customers have struggled with high total cost of ownership (TCO) due to expensive licensing, rigid architectures, and slow, batch-based analytics that limit decision-making. The modern enterprise demands real-time insights, AI-driven automation, and seamless data integration across business units. By unifying SAP data with external sources in an open, scalable Lakehouse architecture, businesses can improve TCO, eliminate data silos, and unlock new revenue opportunities.
The challenge of proprietary data models
Companies locked into proprietary systems face a number of significant challenges that restrict innovation and growth. These systems are designed to be highly structured in order to be performant, which makes them inflexible and brittle. This condition makes it difficult to integrate with modern cloud-based analytics and AI platforms. The closed nature of proprietary architectures means businesses must rely on expensive, vendor-specific tools and expertise to extract and transform their data. Additionally, licensing costs and contractual restrictions limit scalability, forcing enterprises to work within rigid frameworks that prevent them from adapting and innovating to service the evolving business needs. These constraints lead to inefficiencies, slow decision-making, and increased operational costs. In a post-pandemic world, organizations must break free from these limitations and embrace open, scalable data architectures.
We recently worked with a global supply chain organization that faced significant roadblocks in accessing and integrating SAP data with their cloud analytics platform. Their finance and operations teams struggled with slow, complex reporting that took weeks to generate. By leveraging a modern data lakehouse approach, they decoupled SAP data from proprietary constraints, enabling real-time analysis across finance, procurement, and supply chain. This transformation resulted in a 20% improvement in revenue forecasting accuracy and a measurable increase in profitability by optimizing procurement decisions and reducing waste. The transformation allowed the company’s leadership to shift from static quarterly reporting to dynamic, AI-driven insights that informed daily operational decisions.
SAP’s fundamental flaw is its insistence on keeping data locked in proprietary structures. Organizations accustomed to working with open, scalable data architectures find themselves bogged down by layers of complexity, unable to access their own data efficiently. Forcing companies to build SAP connectors to extract data removes the real-time nature of modern decision-making and burdens IT organizations with unnecessary cost. Business intelligence teams must rely on static reports generated from outdated extracts, preventing the kind of agile, data-driven decision-making that today’s market demands. If companies want to harness the power of machine learning and AI, they must first address this underlying issue: rigid, legacy data models were not designed for the scale and speed of modern analytics.
The cost of maintaining legacy analytics in SAP
While we don’t recommend moving away from large-scale systems like SAP entirely, we highly encourage organizations to redesign their data estates to achieve a much higher ROI and lower TCO. Rather than continuing to throw money at legacy analytics solutions, organizations should embrace architectures that separate compute from storage, enabling on-demand scaling at a fraction of the cost. Open formats eliminate vendor lock-in, allowing companies to store SAP data in cost-efficient cloud storage while leveraging best-in-class compute engines for analysis. Organizations that modernize their SAP analytics infrastructure reduce cost and create a more agile, scalable foundation for innovation.
Blueprint worked with a multinational technology company that had spent years building and maintaining a complex ingestion framework designed to make SAP a more effective transactional tool in their supply chain. Their SAP infrastructure costs were consuming a disproportionate share of their IT budget, with escalating licensing and infrastructure costs. By transitioning to an open data architecture in the cloud, they reduced annual SAP infrastructure expenses by almost 40% while improving scalability and performance. The new system enabled cross-functional teams to access real-time research, supply chain, and sales data without incurring high SAP-related costs. SAP analytics has one of the highest intangible costs in the industry, including infrastructure maintenance, customization, and opportunity cost of inefficiency. As data volumes grow, these costs only increase, forcing organizations to invest more in a system that delivers diminishing returns.
Solving the problem with SAP Databricks… A unified, scalable data architecture
Organizations that successfully modernize their SAP data infrastructure move away from proprietary constraints and adopt architectures that support dynamic, cross-functional insights. A unified lakehouse model allows enterprises to integrate structured and unstructured data from across their ecosystem, eliminating silos and unlocking new opportunities for AI-driven automation. With SAP Databricks, customers can now expect out of their SAP environment:
Eliminate costly data silos
AI-driven financial intelligence
Leverage real-time, AI-powered analytics to optimize revenue forecasting, cash flow management, and operational efficiency—allowing finance teams to make data-backed decisions faster.
Lower TCO & maximize ROI
Financial agility & risk management
Additional Resources
Catch up on our TCO webinar series
Mastering TCO with Databricks
A Blueprint series feat. President and CEO Ryan Neal
Unlock the tools to master Total Cost of Ownership (TCO) with Databricks in this educational three-part webinar series. We’ll delve into TCO analysis intricacies, comparing Databricks with alternatives to reveal long-term economic benefits. Gain the necessary tools to accurately define Databricks’ TCO within your organization, empowering informed decisions for future data strategies.
