SAP BDC is the Result of Decades of Data Evolution

In February 2025, SAP announced its latest evolutionary step to empower real-time data analytics and insights, while simultaneously providing an ever-improving data foundation for modern Business AI.

It goes by the name for SAP Business Data Cloud or BDC, combining the best SAP data and analytic advances of the last decade+ with the best technology advances of key partners Databricks, Collibra, DataRobot, Confluent, and others. I believe 2025 represents the “cold fusion” moment for real-time business data fabric, a goal all of us in the datascape have been striving for over the last two decades since the big bang moment of the internet exploded in the 1990s.

Like all technology, zillions of steps built up over time until “wow, this is big” happens. SAP enjoys a unique perspective from earning the privilege of serving as the ERP (Enterprise Resource Planning) bedrock for a broad array for corporations and governments around the world, caretaking financial data for companies and their shareholders. To best understand why BDC is a BIG deal, let me offer a dozen quick questions and answers that will help illuminate this singular moment:

The Problem: Why has breaking down departmental data silos and creating an effective data fabric for analytics been a difficult challenge for large companies?

Complexity is hard to overcome. Breaking down departmental data silos and creating an effective data fabric for analytics is a complex undertaking for large companies due to a confluence of factors. Here’s a breakdown of the key challenges:

1. Legacy Systems and Technological Disparities:

• Heterogeneous Environments:

• • Large organizations often have a long history, resulting in a patchwork of legacy systems, databases, and applications.
  • These systems may use different data formats, protocols, and technologies, making integration challenging.

• Lack of Standardization:

• • Different departments may have adopted their own preferred tools and technologies, leading to a lack of standardization across the organization.

2. Organizational and Cultural Barriers:

• Departmental Silos:

• • Departments often operate independently, with their own data repositories and processes.
  • This can create a “data hoarding” mentality, where departments are reluctant to share their data.

• Lack of Data Governance:

• • Without clear data governance policies and procedures, it’s difficult to ensure data quality, consistency, and security across the organization.

• Resistance to Change:

• • Implementing a data fabric often requires significant changes to existing processes and workflows, which can be met with resistance from employees.

3. Data Complexity and Volume:

• Data Variety:

• • Organizations generate vast amounts of data from diverse sources, including structured, semi-structured, and unstructured data.
  • Integrating and processing this data requires sophisticated tools and techniques.

• Data Volume and Velocity:

• • The sheer volume and velocity of data can overwhelm traditional data management systems.
  • Real-time analytics requires high-performance data processing capabilities.

• Data Context:

• • Preserving the business context of data as it’s moved and integrated is very difficult, but also very important.

4. Security and Compliance:

• Data Security:

• • Consolidating data into a central data fabric increases the risk of data breaches.
  • Organizations must implement robust security measures to protect sensitive data.

• Regulatory Compliance:

• • Organizations must comply with various data privacy regulations, such as GDPR and CCPA. This requires careful management of data access and usage.

In essence:

• The challenge lies in not just the technical aspect of connecting disparate systems, but also in the organizational and cultural changes required to foster a data-driven culture.
• The need to maintain data governance and security while enabling data accessibility adds another layer of complexity.

When did Data Warehousing take off for large companies?

While the groundwork was laid earlier in the 1970s and 1980s, data warehousing took off in mainstream corporations in the 1990s. SAP specifically joined the effort around 1998 with the first release of Business Warehouse (BW), designed to improve business analytics and reporting.

Understanding BW in the Evolution: What made SAP Business Warehouse Unique?

In short, it was built for SAP data. SAP Business Warehouse (BW) has established a unique position in the data warehousing landscape, particularly within the SAP ecosystem. Here’s a breakdown of what sets it apart:

• Optimized for SAP Data:

• • SAP BW is deeply integrated with SAP’s business applications, allowing for seamless extraction and transformation of data from SAP ERP and other SAP systems.
  • This tight integration ensures data consistency and accuracy.

• Purpose-Built for Business Intelligence:

• • SAP BW is specifically designed for business intelligence (BI) and data warehousing.
  • It provides powerful tools for data modeling, reporting, and analysis, enabling organizations to gain insights from their data.

• Multidimensional Data Modeling:

• • SAP BW excels at multidimensional data modeling, which is crucial for analytical reporting.
  • This allows users to analyze data from various perspectives and identify trends and patterns.

• Historical Data Analysis:

• • SAP BW is designed to store and analyze historical data, enabling organizations to track performance over time and identify long-term trends.

• Extraction, Transformation, and Loading (ETL) Capabilities:

• • SAP BW provides robust ETL capabilities for extracting data from various sources, transforming it into a consistent format, and loading it into the data warehouse.

• BEx Queries:

• • SAP BW utilizes BEx queries which are tools that provide powerful ways for users to query and analyze the data that is within the BW system. This allows for very specific and detailed analysis of the data.

Key Considerations:

• It’s important to note that SAP’s data strategy is evolving, with SAP Datasphere and SAP Business Data Cloud playing increasingly prominent roles. However, SAP BW remains a significant component of many organizations’ data landscapes.
• SAP BW/4HANA is the current version of SAP BW, and is optimized to run on the SAP HANA database.

In essence, SAP BW’s uniqueness lies in its tight integration with SAP systems, its focus on BI, and its robust data modeling and analysis capabilities.

Important Acquisition in the Evolution: SAP buys Business Objects in 2008

After SAP purchased Business Objects, SAP held a unique position in the business intelligence (BI) landscape, offering a comprehensive suite of tools that catered to various analytical needs. Here’s a breakdown of its key differentiators:

• Comprehensive Suite of Tools: SAP BusinessObjects provided a wide array of tools for reporting, analysis, and visualization, including:
• Web Intelligence (Webi): For ad-hoc reporting and self-service analysis.
• Crystal Reports: For pixel-perfect, formatted reporting.
• Analysis for Office: For integrating SAP data with Microsoft Office applications.

• • These tools allowed organizations to address diverse reporting and analysis requirements within a single platform.

• “Universe” Semantic Layer:

• • A key differentiator was the “Universe” semantic layer.
  • This layer provided a business-friendly abstraction of underlying data sources, simplifying data access for end-users.
  • It allowed users to query data using business terms rather than complex technical queries. This greatly empowered business users.

• Emphasis on Enterprise Reporting:

• • SAP BusinessObjects excelled at enterprise reporting, particularly for organizations with complex reporting requirements.
  • Crystal Reports, in particular, was known for its ability to produce highly formatted and professional reports.

• Wide Adoption and Maturity:

• SAP BusinessObjects had a long history and a large user base, making it a mature and widely adopted BI platform. This meant a large pool of expertise and a wealth of resources were available.

 

It’s important to understand that the BI landscape has evolved, and SAP is now heavily focused on SAP Analytics Cloud (SAC) as its strategic cloud-based analytics solution. However, SAP BusinessObjects remains a relevant tool for many organizations, especially those with established on-premise deployments.

The Birth of SAP HANA: Stronger, faster, able to leap tall buildings in a single bound.

SAP realized that it’s application running on top of existing relational databases such as IBM DB2 and Oracle would never perform well enough in combined transactional and analytics use cases. The world was (and still is) demanding real-time and near-real-time computing systems that can support customers in a real-time world. SAP HANA was introduced in 2010, and has subsequently advanced in capability over time.

SAP HANA distinguishes itself from traditional database management systems through several key features:

• In-Memory Computing:

• • Unlike traditional databases that primarily store data on disks, SAP HANA stores data in its main memory (RAM).
  • This significantly accelerates data access and processing speeds, enabling real-time analytics.

• Column-Oriented Data Storage:

• • SAP HANA uses a column-oriented storage structure, which is highly efficient for analytical processing.
  • This allows for faster retrieval of specific data columns, improving query performance.

• Hybrid Transactional/Analytical Processing (HTAP):

• • SAP HANA can perform both online transactional processing (OLTP) and online analytical processing (OLAP) on the same platform.
  • This eliminates the need for separate systems for transactional and analytical workloads, simplifying data management and providing real-time insights.
  • In practice, many companies continue to separate the workloads for a variety of reasons, but leverage the speed offered by SAP HANA.

• Multi-Model Data Processing:

• • SAP HANA supports various data types and models, including relational, graph, spatial, and document data.
  • This versatility enables organizations to analyze diverse data sources within a single platform.

• Advanced Analytics and Machine Learning:

• • SAP HANA includes built-in advanced analytics capabilities, such as predictive analytics, text analysis, and spatial processing.
  • It also integrates with machine learning libraries, enabling organizations to build and deploy intelligent applications.

• Data Virtualization:

• • SAP HANA allows data virtualization, so that data from other sources can be accessed without having to physically move that data into the HANA database.
  • This helps to reduce data duplication.

In essence, SAP HANA’s unique architecture and features enable organizations to process and analyze large volumes of data in real time, driving faster decision-making and innovation.

Embracing the Cloud: HANA Cloud.

As cloud computing evolved, so did SAP. The HANA platform is leveraged as the engine for SAP HANA Cloud, a database as a service offering that simplifies acquisition, scaling, and maintenance for customers. Importantly, HANA Cloud keeps a steady pace of innovation, helping customers keep up with ever-quickening change.

Embracing the Cloud: SAP Analytics Cloud (SAC) offers a modern, modular step forward.

SAP Analytics Cloud (SAC) was introduced in 2015 and distinguishes itself by converging business intelligence (BI), planning, and predictive analytics into a single cloud-based solution. This unified approach provides a unique and powerful platform for data-driven decision-making. Here’s a breakdown of its key differentiators:

• Unified Analytics and Planning:

• • Unlike many BI tools that focus solely on reporting and analysis, SAC integrates planning capabilities directly into the platform.
  • This allows users to move seamlessly from gaining insights to taking action, simulating scenarios, and creating plans within the same environment.

• Deep Integration with SAP Ecosystem:

• • SAC is designed to work seamlessly with SAP’s extensive suite of applications, particularly SAP S/4HANA and SAP Datasphere.
  • This tight integration enables real-time access to SAP data and ensures data consistency.

• Emphasis on Augmented Analytics:

• • SAC leverages machine learning and artificial intelligence to automate insights, discover patterns, and generate predictions.
  • This “augmented analytics” approach empowers users to uncover hidden insights and make more informed decisions.

• Collaborative Planning:

• • SAC facilitates collaborative planning by providing tools for users to share plans, discuss assumptions, and work together on forecasts.
  • This fosters alignment and improves the accuracy of planning processes.

• Pre-built Business Content:

• • SAP provides a library of pre-built business content for various industries and lines of business.
  • This accelerates the implementation of SAC and allows organizations to quickly gain value from the platform.

• Generative AI Capabilities:

• • SAC is implementing generative AI capabilities, through the Joule AI assistant, to automate reporting, and assist with data analysis. This is a very new and evolving part of the SAC platform.

 

In essence, SAP Analytics Cloud’s uniqueness lies in its ability to combine BI, planning, and predictive analytics into a single, integrated cloud platform, enhanced by AI, that is deeply connected to the SAP ecosystem.

Embracing the Cloud: SAP Datasphere strives to simplify the data and create a data fabric:

SAP Datasphere is designed to revolutionize how businesses manage and utilize their data, and it distinguishes itself through several key aspects:

• Business Data Fabric Architecture:

• • SAP Datasphere is built upon a business data fabric architecture.
  • This means it’s designed to create a unified data landscape where business context and logic are preserved, enabling users to access and understand data more effectively.

• Emphasis on Business Context:

• • A core differentiator is its focus on maintaining and delivering data with its original business context.
  • This allows business users to work with data that’s meaningful and relevant to their specific needs, without requiring deep technical expertise.

• Unified Data Experience:

• • SAP Datasphere provides a unified environment for various data management tasks, including data integration, data cataloging, semantic modeling, data warehousing, data federation, and data virtualization.
  • This consolidated approach simplifies data management and reduces complexity.

• Open Data Ecosystem:

• • It’s designed to work within an open data ecosystem, meaning it can connect with both SAP and non-SAP data sources.
  • This flexibility allows organizations to integrate data from diverse environments, including cloud and on-premise systems.

• Data Governance and Discovery:

• • SAP Datasphere places a strong emphasis on data governance, providing tools for data cataloging, quality management, and security.
  • This helps organizations ensure data accuracy, compliance, and trust.

• Enabling Data Sharing:

• • It facilitates the ability to share data in a controlled and governed method, enabling organizations to create data products, and share them internally, and potentially externally.

In summary, SAP Datasphere is unique because it prioritizes delivering data with its business context intact, within a unified and open environment, while also emphasizing data governance and simplified data sharing.

In the meantime: The rapid growth of unstructured data:

All industry started collecting unstructured data in the 2005 – 2010 timeframe, and the volumes exploded after 2010. Data piled up, usually in Apache Hadoop powered open source computing systems, but little was leveraged for profitable outcomes. The complexity of maintaining and working with Hadoop gave way to data lake services such as Amazon S3. The great potential of finding better ways to leverage all this “dark” data offered opportunity for companies such as Databricks to simplify taking advantage of the data.   

In the meantime: The simultaneous evolution of key SAP partner, Databricks:

Databricks has experienced a rapid and significant evolution, solidifying its position as a leader in data and AI. Here’s a look at its history and key milestones:

• Origins in Apache Spark:

• • Databricks was founded in 2013 by the original creators of Apache Spark, a powerful open-source distributed computing system. This foundation in Spark is crucial to understanding Databricks’ core technology.
  • The founders, including Matei Zaharia, came from the AMPLab at the University of California, Berkeley, where they developed Spark.

• Early Focus on Cloud-Based Spark:

• • Initially, Databricks focused on providing a cloud-based platform that simplified the use of Apache Spark.
  • This addressed the complexities of managing and scaling big data processing.

• Development of the Lakehouse Architecture:

• • A pivotal development was the creation of the “lakehouse” architecture. This approach combines the best elements of data lakes and data warehouses, enabling organizations to perform both data science and business intelligence on a single platform.
  • This innovation significantly streamlined data management and analysis.

• Key Technological Advancements:

• • Delta Lake: An open-source storage layer that brings reliability and performance to data lakes.
  • MLflow: An open-source platform for managing the machine learning lifecycle.
  • These technologies have been integral to Databricks’ growth and adoption.

• Strategic Partnerships:

• • Partnerships with major cloud providers, such as Microsoft Azure and Amazon Web Services (AWS), have been essential to Databricks’ expansion.
  • Databricks’ 2025 partnership with SAP offers great potential to create truly effective data fabrics for SAP ERP customers.

• Emphasis on AI:

• • Databricks is now very heavily involved in AI, and providing the tools and platform for companies to create their own AI models.

• Continued Growth:

• • Databricks has experienced rapid growth, attracting significant investment and expanding its customer base. It is now a major player in the data and AI space.

In essence, Databricks has evolved from a company focused on simplifying Apache Spark to a leading provider of a unified data and AI platform, driven by its innovative lakehouse architecture.

One click deeper: What is Lakehouse Architecture and why does it matter?

The Lakehouse Architecture represents a significant shift in data management, offering a unique blend of the strengths of data lakes and data warehouses. Here’s what makes it distinctive:

• Bridging the Gap:

• • Traditionally, organizations had to choose between data lakes (for storing vast amounts of raw, unstructured data) and data warehouses (for structured, analysis-ready data).
  • The Lakehouse Architecture eliminates this trade-off, enabling both types of data and workloads on a single platform.

• Combining Best of Both Worlds:

• • It inherits the flexibility and cost-effectiveness of data lakes, allowing for the storage of diverse data types.
  • Simultaneously, it incorporates the structured data management and performance characteristics of data warehouses, including ACID transactions, schema enforcement, and optimized querying.

• Enabling Diverse Workloads:

• • This unified approach supports a wide range of analytical workloads, including:

• • • Business intelligence (BI) and reporting.
    • Data science and machine learning (ML).
    • Real-time analytics.

• Open Formats and Standards:

• • Lakehouse architectures typically utilize open data formats like Parquet, which enhances interoperability and avoids vendor lock-in.

• Emphasis on Data Governance:

• • A core component of the Lakehouse is a robust metadata layer that enables data governance, ensuring data quality, consistency, and compliance.

• Decoupling Storage and Compute:

• • Lakehouse architectures are designed to decouple storage and compute. This allows organizations to scale these resources independently, optimizing costs and performance.

In essence, the Lakehouse Architecture revolutionizes data management by providing a unified, flexible, and scalable platform that supports diverse analytical needs.

Cold Fusion: Why is SAP Business Data Cloud (BCD) unique and different?

SAP Business Data Cloud (BDC) represents a significant evolution in SAP’s data management and analytics strategy. Its uniqueness stems from its focus on providing a unified and intelligent data foundation for businesses, particularly in the context of increasing AI adoption. Here’s a breakdown of its key differentiators:

• Integration of SAP and Non-SAP Data with Preserved Business Context:

• • A core strength of SAP BDC is its ability to seamlessly integrate data from both SAP’s core business applications (like S/4HANA) and external, non-SAP sources.
  • Crucially, it prioritizes preserving the original business context and semantics of the data, which is often lost when data is moved to other platforms.
  • This ensures that data remains meaningful and actionable for business users.

• Emphasis on Enabling Business AI:

• • SAP BDC is designed to be a fundamental platform for powering business AI initiatives.
  • It provides the trusted and reliable data foundation that AI models require to deliver accurate and relevant insights.
  • This includes integration with AI tools such as the Joule AI assistant.

• Partnership with Databricks:

• • A key differentiator is the deep integration of Databricks technology.
  • This partnership enhances SAP BDC’s capabilities for data engineering, machine learning, and AI workloads, allowing organizations to work with diverse data types and build sophisticated AI models.

• Focus on Data Products:

• • SAP BDC facilitates the creation and management of “data products,” which are curated and readily consumable data sets.
  • This approach simplifies data access and utilization, empowering business users to derive insights more quickly.

• Unified Data Experience:

• • SAP BDC aims to provide a unified data experience by bringing together various SAP data and analytics solutions, such as SAP Datasphere, SAP Analytics Cloud, and SAP BW, into a cohesive platform.

In essence, SAP BDC is unique in its focus on providing a comprehensive and intelligent data foundation that bridges the gap between transactional and analytical data, and that is optimized for the era of AI. This enables SAP and partners to offer ready-to-consume Insight Applications (Insight Apps) that move the needle for business leaders through a corporation. 

One click deeper: Why are Data Products a significant evolutionary step?

SAP data products represent a fundamental shift in how organizations approach data management and consumption. They move away from traditional, monolithic data architectures toward a more modular, user-centric approach. Here’s a breakdown:

What are SAP Data Products?

• Essentially, SAP data products are curated, reusable, and easily consumable data assets.
  • They are designed to meet specific business needs and are treated with a “product mindset,” meaning they have defined owners, lifecycles, and quality standards.
  • They aim to provide data in a way that is:

• • • Accessible: Easy to find and use.
    • Understandable: Accompanied by clear business context and metadata.
    • Trustworthy: Reliable and governed.

• They are designed to be consumed by various applications, including SAP, customer, and partner applications.

What Makes the Data Product Approach Unique?

• Product Thinking Applied to Data:

• • This is a key differentiator. It treats data as a product, with defined ownership, quality standards, and a focus on user experience.
  • This contrasts with traditional data management, which often treats data as a purely technical asset.

• Emphasis on Business Context:

• • Data products prioritize delivering data with its original business context intact.
  • This makes data more meaningful and actionable for business users.

• Decentralized Data Ownership:

• • The data product approach often promotes decentralized data ownership, where domain experts are responsible for their respective data products.
  • This ensures that data is managed by those who understand it best.

• Increased Data Reusability:

• • Data products are designed to be reusable across multiple applications and use cases, reducing data duplication and improving efficiency.

• Enhanced Data Discoverability:

• • Data products are typically accompanied by rich metadata, making them easier to find and understand.

• Facilitating Data Sharing:

• • Data products are designed to enable secure and governed data sharing, both internally and externally.

In essence, the data product approach aims to democratize data access and empower business users to make data-driven decisions. This is achieved by treating data as a valuable product, with a focus on usability, quality, and governance.

One click deeper: Why is Delta Sharing a significant evolutionary step?

Since the release of SAP HANA and SAP Analytics Cloud, SAP has been a strong proponent of businesses becoming real-time enterprises, able to react in time to change outcomes based on the data. SAP is leveraging Databricks’ Delta Share capabilities, augmenting important technological contributions from Confluent (Kafka), SAP Advanced Enterprise Mesh, and SAP’s data transfer technologies like SLT.

Delta Sharing is a groundbreaking open protocol developed by Databricks that enables the secure sharing of data across organizations, regardless of the computing platforms they use. Its uniqueness stems from its focus on open standards and its ability to facilitate real-time data sharing without the need for data duplication. Here’s a breakdown of its key differentiators:

• Open Protocol:

• • Delta Sharing is an open-source protocol, meaning it’s not tied to a specific vendor or platform.
  • This allows organizations to share data with anyone, even if they don’t use Databricks. This open nature fosters interoperability and breaks down data silos.

• Direct Data Sharing:

• • Unlike traditional data sharing methods that often involve copying or moving data, Delta Sharing allows for direct access to live data.
  • This eliminates the need for data replication, which reduces storage costs, ensures data freshness, and minimizes data latency.

• Secure and Governed Sharing:

• • Delta Sharing prioritizes data security and governance.
  • It provides mechanisms for controlling access to shared data, auditing data access, and ensuring compliance with data regulations.

• Broad Compatibility:

• • Delta Sharing is designed to work with a wide range of data tools and platforms, including popular BI tools like Tableau and Power BI, as well as programming languages like Python and Apache Spark.
  • This broad compatibility makes it easy for data recipients to access and analyze shared data.

• Real-time Data Access:

• • Because delta sharing shares the data where it resides, it allows for near real time data access. This is very important for many modern data applications.

In essence, Delta Sharing revolutionizes data sharing by providing a secure, open, and efficient way to share live data across organizations.

Last one / one click deeper: Why are Insight Apps a significant evolutionary step?

SAP Insight Apps represent a significant evolution in how businesses can derive and utilize insights from their data, particularly within the SAP ecosystem.   

Essentially, SAP Insight Apps are pre-built, packaged analytical content designed to deliver specific business insights. 

• • They leverage the capabilities of SAP technologies like SAP Datasphere and SAP Analytics Cloud to provide ready-to-use dashboards and reports.
  • These apps are designed to streamline the process of accessing and understanding critical business data.
  • They are designed to be deployed very quickly, so that business users can gain insights faster.

• How they are unique compared to previous BI approaches:
  • Accelerated Time-to-Value:
    • Traditional BI projects often involve lengthy development cycles to build reports and dashboards.
    • Insight Apps offer a “plug-and-play” approach, significantly reducing the time required to gain actionable insights.
  • Simplified Data Consumption:
    • They aim to bridge the gap between complex data and business users.
    • By providing pre-configured dashboards, they make it easier for non-technical users to understand and analyze data.
  • Integration and Automation:
    • Insight Apps are designed to seamlessly integrate with SAP’s data platforms, automating data ingestion, transformation, and visualization.
    • This reduces manual effort and improves data accuracy.
  • Focused Business Outcomes:
    • Unlike generic BI tools, Insight Apps are tailored to address specific business needs and use cases.
    • This ensures that users get the most relevant insights for their particular roles and responsibilities.
  • Pre-defined content:
    • One of the main differences is that the content is pre-defined by SAP.
    • In older BI approaches, much of the content would have to be created by the businesses IT department.

In essence, SAP Insight Apps represent a shift towards a more agile and user-friendly approach to business intelligence, enabling organizations to derive value from their data more quickly and efficiently.

In Summary

No technology vendor has been as focused on overcoming complexity, simplifying the overall landscape, and delivering great time-to-value than SAP.

It is important to understand that many technological advances have come together to coalesce as the real-time business fabric known as SAP Business Data Cloud. SAP is now building hundreds of Insight Applications in every line-of-business that will draw data from this real-time, governed data foundation to deliver extraordinary value and flexibility to business users. Partner firms such as McKinsey, Deloitte, Accenture, PwC and others are also taking advantage of this singular moment and are creating Insight Apps that will enhance companies capabilities in profound ways.

Artificial Intelligence is going to change the world in more profound ways that we imagine today. Great AI requires great data, and BDC delivers great data. The potential of AI to improve business process efficiency and effectiveness is incredible.

Contact your SAP Account Executive to schedule a BDC Exploration / Innovation day at your firm, or look for one of SAP’s BDC workshops that are being offered throughout cities worldwide in 2025. 

Is really is a great time to specialize in “data and AI!” Connect with me at Linkedin at sapbob.com — I’m happy to help find the right contact for you at SAP or answer any questions that you may have.

Bob Sakalas
Innovation Strategist
SAP Customer Advisory, North America

 

Please note that Bob leveraged Generative AI when producing parts of this blog post.