Introduction to SAP Datasphere

Over the last few decades, billions of dollars have been spent on developing architectures with the intent to capture data to drive better decision-making. However, these initiatives have failed to deliver the expected value. This has primarily been due to the fact that these initiatives built on extracting and replicating data have been unsuccessful in maintaining the business context, metadata, logic, or any other relationships which define a business object. Organizations have struggled to reverse engineer these “Knowledge Graphs” and to establish the business contexts in their data architectures. They key challenges enterprises currently face are:

1. Data without context
2. Diverse data needs for diverse team
3. Growing costs

This is particularly true for SAP systems, which generate data across multiple mission-critical business processes and hold tremendous value for analysis and influencing business decisions. Yet, in the endeavor to manage this data, organizations end up focusing on the technology itself rather than the data where the actual value lies. Access to this data is also essential in allowing organizations to achieve true digital transformation. The organizations that harness the full power of their data transform into enterprises which innovate faster and adapt to unanticipated changes, thus creating new revenue streams. To support customers in this journey, SAP recently announced the SAP Datasphere. SAP Datasphere is the next-generation data management offering from SAP, which enables customers to rapidly access data across diverse enterprise landscapes. Additionally, SAP has also formed strategic partnerships, industry-leading data, and AI companies to enrich the data sphere with open data capabilities. SAP Datasphere balances the technology and business needs of data, as shown in Figure 1. Figure 1: Balancing technology and business needs

SAP Datasphere Driving the Business Data Fabric

Data fabric enables driving democratization of data across the enterprise by automating data streams through orchestrating mechanisms. I have already explored SAP’s vision of the Data Fabric in detail here. Even with enterprise-grade data fabric, there is a lack of business context. Without a business context, it is very difficult for organizations to drive the right business decisions driven by a holistic view of enterprise data. A business data fabric, on the other hand, enables the following:

1. Self-service access to diverse data sets
2. Comprehensive data governance
3. Real-time data enrichment
4. Simplified data landscape

The SAP Datasphere thus aligns perfectly with the key asks from a business data fabric, enabling organizations to drive decisions based on business context. The architecture of the SAP Business Fabric with SAP Datasphere is illustrated below: Figure 2: SAP Datasphere architecture SAP Datasphere enables driving the business data fabric through the following key capabilities:

1. Connectivity

SAP Datasphere enables seamless connectivity to SAP and non-SAP data sources and the ability to maintain the business context while connecting to SAP data sources.

2. Openness

It offers support for multi-cloud and open interfaces facilitated by partners.

3. Data Exchange

Users can implement the exchange of data across interfaces facilitated by SAP Data Marketplace.

4. Self-service

It also allows business users to reduce dependence on IT by facilitating self-service data analysis.

5. Collaboration

SAP Datasphere enables sharing data spaces across multiple users and easily collaborating with stories on the SAP Analytics Cloud.

6. Analytics and Planning

It augments the power of analytics and planning of the SAP Analytics Cloud with contextualized data from the SAP Datasphere.

7. Semantic Modeling

The decoupled business and data layers enable semantic modeling across the data as well as the business layers.

8. Data Governance

The SAP Datasphere enables governance across the multiple datasets, spaces, and objects via the catalog. The key components of the SAP datasphere that enable the business data fabric follow:

1. Spaces

This component enables creating dedicated data spaces tailored to business needs.

2. Data Integration and Data Flow

This component enables users to connect to multiple data sources as well as ingesting the data. This is facilitated by ready-to-consume connections as well as through partners’ connectivity options.

3. Data Builder

The data builder enables the data engineers to structure the data from tables, views, and intelligent lookups into data models and analytic models ready for consumption. The data models further are sourced into the Business Builder, whereas the analytic models can be consumed directly into SAC stories.

4. Business Builder

While the IT teams clean and prepare the data into models, the business users can query this data in business terms via the business layer. The business layer enables the users to maintain the business context to the data to ensure that decisions maintain relevance.

5. Business Catalog

The catalog enables good data governance across all the objects within the datasphere.

Benefits

1. Integrate data across multiple sources to drive better decisions in real time.
2. Empower business users with data available in business context, driving better self-service analysis and enable self-service data enrichment across multiple source systems.
3. Decouple the business layer, enabling IT teams to manipulate data without having an impact on business decision-making and analysis.
4. Develop citizen data analysts to drive better data-driven business decisions.

Conclusion

SAP Datasphere, the next-gen data warehousing solution from SAP, addresses the key issue of maintaining business context in data warehouses. While integrating across a variety of sources, the datasphere enables driving real-time business decisions while decoupling the business layer from the data layer. It is a strategic solution for SAP for driving self-service data analysis across the business users with a roadmap to drive further innovations regularly.