Objectives
This lesson provides a comprehensive overview of the various SAP tools for data management and analytics, including SAP Datasphere, SAP HANA Cloud, and SAP Analytics Cloud. It explains how these tools support the integration, management, and analysis of data to gain valuable business insights and make data-driven decisions.
The lesson contains the following topics:
This section provides an overview of the various SAP tools that aim to simplify and improve the access and management of data. The tools include SAP Datasphere, SAP HANA Cloud, SAP Analytics Cloud, and SAP Master Data Governance. SAP Datasphere enables integrated data management, SAP HANA Cloud is a versatile database for intelligent applications, SAP Analytics Cloud offers solutions for business intelligence, planning and predictive analytics, and SAP Master Data Governance improves the control of master data.
The Data and Analytics area includes the following tools:
Access to authoritative data. Accelerate value creation by automatically reusing semantic definitions and associations from SAP applications.
Enrich all data projects. Harmonize heterogeneous data in a semantic business model for your diverse data landscape.
Simplify the data landscape. Access all your data in hybrid and cloud environments, regardless of where it is located.
A database for every workload. Use the powerful capabilities of the SAP HANA Cloud multimodel engine for relational data, document storage, geospatial data, graphics, vector data, and time series.
Intelligent data applications that apply your expertise and data. Go beyond transactional applications, enabling developers to create applications that use generative AI, are context-aware, and securely connect to critical business data.
Scalable performance and unwavering security. Shift developers' focus from administrative tasks to building innovative applications with elastic scalability and built-in and fully managed security, compliance, and high availability.
Reliable AI. Use generative AI to automate reporting, discover hidden insights, and create and develop business plans with Joule Copilot.
Deliver business-critical analysis. Extend BI capabilities and deliver industry-specific analysis with prebuilt business content.
Transform business planning. Enable collaborative planning by unifying financial, supply chain, and operational planning with a single solution.
Build a comprehensive master data management system. Gain a unified view of master data across all areas through consolidation, centralized management, and replication.
Work with trustworthy, business-ready master data. Support the management and quality of master data through monitoring, root cause analysis, and remediation.
Reduce time to value with prebuilt data models, business rules, workflows, roles, and industry-specific content.
SAP Datasphere is a tool that supports data integration, cataloging, semantic modeling, data warehousing, and virtualization for SAP and non-SAP data. It harmonizes heterogeneous data and provides a central platform for managing and using this data.
SAP Datasphere is a unified service for data integration, cataloging, semantic modeling, data warehousing, and virtualization of workloads for SAP and non-SAP data. With SAP Datasphere, data experts can easily distribute business-critical data across their organization's entire data landscape while maintaining business context and logic.
SAP Datasphere is the next generation of the SAP Data Warehouse Cloud.
The most important capabilities are:
SAP Datasphere enables direct access to business-critical data and metadata - and the critical relationships between them - from SAP applications. With the new SAP Datasphere Analytic Model, data experts can easily solve complex requirements for semantic modeling of business data. The automatic reuse of semantic definitions and associations from SAP applications shortens time-to-value and simplifies the steps that data experts must take across the organization to deliver business-critical data. SAP Datasphere Catalog also makes it easier to find, manage, and control data automatically. In addition, cataloging of data stored in SAP Analytics Cloud and SAP Datasphere is offered at no additional cost.
With SAP Datasphere, organizations can bring all their data together and perform transformations that ensure all stakeholders are always working with the same data. SAP Datasphere supports both data federation and the new fast data replication, providing powerful options for combining data sources and harmonizing heterogeneous data in a semantic business model for customers' different data landscapes. With the SAP Datasphere Marketplace, access to industry data is now even easier: more than 3,000 curated data sets can be purchased at the touch of a button to quickly enrich data projects with up-to-date, trustworthy data. SAP announced partnerships with industry-leading open data partners to tightly integrate their data and AI platforms with SAP Datasphere, enabling customers to combine SAP data with other data sources like never before.
SAP Datasphere runs in any cloud and hybrid environment. Thanks to this flexibility, data experts can securely access all their data, no matter where it is located. In addition, customers can maximize their investment in on-premise solutions such as SAP Business Warehouse (SAP BW). SAP Datasphere, BW bridge, enables the reuse of SAP BW models and connectors and offers customers of SAP NetWeaver BW and SAP BW/4HANA a transition path to the public cloud. Once data has been integrated, harmonized, and enhanced in SAP Datasphere, data professionals can create a "virtual workspace" with SAP Datasphere Spaces - a capability to organize, group, and manage data and control the level of visibility and collaboration. These virtual workspaces can then be made available to data consumers that rely on federated data, such as marketing, sales, supply chain or partner data, and AI teams.
SAP Datasphere and its open data ecosystem form the technological basis for a business data fabric. This is a data management architecture that focuses on providing an integrated, semantically rich data layer on top of the underlying data landscapes to enable seamless and scalable access to data without duplication. With a business data fabric, any organization is able to provide meaningful data to any data consumer - with business context and logic intact. Because organizations need accurate data that is readily available and described in business-friendly terms, this approach enables data professionals to enforce the clarity that business semantics provides in every use case.
SAP Analytics Cloud is a SaaS product for business intelligence, planning, and predictive analytics. It integrates trustworthy AI for automated reporting and business planning. Functions such as data visualization, modeling, and predictive analytics are included in this platform.
SAP Analytics Cloud is an all-in-one cloud product offered as software as a service (SaaS) for business intelligence (BI), planning, and predictive analytics. Built natively on SAP Business Technology Platform (SAP BTP), it provides a unified and secure public cloud experience to maximize data-driven decision-making.
A story is a presentation-style document that uses charts, visualizations, text, and images to describe data.
With SAP Analytics Cloud, analytics designer, you can create analytic applications for data analysis and data planning. In analytic applications, you can configure the behavior of the UI elements with a set of specific script API events and specify which actions must take place when events are triggered.
In SAP Analytics Cloud, you can start ad-hoc analyses with a data analyzer - a predefined ready-to-run service for multidimensional, pivot styled ad-hoc analysis for SAP BW Live queries, SAP HANA Live views, SAP Datasphere models, and SAP Analytics Cloud models. SAP BW queries and SAP HANA views can be accessed directly and no additional model needs to be created. The data can also be displayed in charts.
In SAP Analytics Cloud, a model is a representation of large amounts of business data from source systems. It defines measures and dimensions that are used to build visualizations, filters, and calculations in stories.
In SAP Analytics Cloud, you can create formulas to perform calculations on values and members of the account dimensions. You can also use predefined formulas, functions, conditions, and operators to build up a formula in Modeler.
SAP Analytics Cloud has many planning tools for collaboratively creating and updating model data to carry out planning tasks. At its simplest, planning involves typing values directly into table cells in planning models.
Complete the following Learning Journey: Exploring SAP Analytics Cloud
Data Fabric is a concept that centralizes, links, manages, and controls data from different systems and applications. It enables real-time access to a central data source, automates data management, and prepares data for analysis and machine learning.
A data fabric is a combination of a data architecture and special software solutions that centralize, link, manage, and control data across systems and applications.
Data fabric solutions link and manage data in real time across different systems and create a centralized data source. They enable needs-based access and automate data management. They also rationalize data in complex, distributed architectures, and prepare it for analysis and machine learning by standardizing, cleansing, and securing it. Data fabric solutions therefore allow companies to use their data efficiently and scale their systems dynamically.
Data mesh and data fabric are data architecture concepts for improved data management and integration. However, they differ in their approach.
Data Mesh is decentralized and promotes data autonomy by allowing teams to monitor, manage, and make decisions about their own data and services. It encourages the development of customized microservices and the use of APIs for data exchange.
Data Fabric, on the other hand, combines data architecture and software solutions for centralized, linked, and controlled data management. It enables real-time access, creates a centralized data source, and automates data management processes.
Both approaches have advantages. Data mesh is often considered after the implementation of data fabric infrastructures. Data Fabric provides a central data view, optimizes business processes, and enables insights from all systems.
A business data fabric extends the traditional data fabric approach. It simplifies complex data landscapes and delivers usable data while retaining the business logic and application context of the data. This avoids rebuilding the business context that is lost during data transfer. As a result, stakeholders and data users can make decisions quickly and securely as they always have full access to the data, regardless of its storage location or origin.
A data fabric architecture links, manages, and controls data across systems and applications, providing a centralized and uniform view of the data. This applies to both the teams and the systems - at every point in your organization. The most important components of a data fabric architecture include:
Data connectors: you can think of data connectors as bridges that connect different systems where the data is stored (for example, databases, applications, sensors) at a central point. In this way, all data sets can be analyzed from a central point.
Data management: The aim here is to ensure that the data is structured, secure, and of high quality. This includes tasks such as data integration (merging data from different sources), data governance (defining rules for the use and management of data), and data security (protecting sensitive data from unauthorized access).
Data modeling and semantic layer: This step helps you to understand the data by creating a common language for the data from the different systems. This involves creating a model and a semantic layer. While the model describes the data, the semantic layer represents the agreed language for the "story" of the data.
Data processing and analytics: This is where the data is processed and analyzed to gain important insights. This includes tasks such as data warehousing (storage of large amounts of data), data streaming (continuous processing during creation), and data visualization (easy-to-understand presentation of the data).
Automated data management: While data analysis is at the center of automation in various business areas, in the architecture context it is more about the efficient and consistent management of data. This includes the automation of tasks such as data integration, data governance, and data security. Through automation, companies can reduce errors, save time, and improve data quality.
A data product is a curated data set consisting of data, metadata, and standardized APIs. It provides high-quality and easily accessible data for various applications. Technical use cases include data management, data governance, and data integration, while business use cases use data for planning, insights and decision support, predictive analytics and data-driven market offerings. The aim is to develop and implement concrete solution architectures from these use cases.
At the center of this concept is the data product, a curated data set of a specific domain, consisting of data, metadata, and standardized APIs for access. Every data-to-value scenario builds on this principle to provide users with high-quality, format-appropriate, and easily accessible data.
A data product is a prepared, reliable data set designed for specific applications. It integrates and processes relevant data sources, ensures data quality, and makes the information available in a user-friendly way.
Use cases that focus on these data provision scenarios are referred to as technical use cases, while those that focus on creating value from data are referred to as business use cases. The methodology provides the standardized use case patterns organized into categories to either deliver the data or consume the data.
Use cases that deliver data are:
Use cases that use the data.
The aim is now to create concrete solution architectures from the technical and business use cases that can then be implemented. In the following, a closer look at selected use cases is taken.
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