Data Integration Package for SAP Datasphere

Implement a business fabric architecture using SAP Datasphere to bring together and make available data from diverse source systems, including SAP and non-SAP platforms

Proof of concept

Objectives

Scope of service

Discover the potential of SAP Datasphere for data integration in a proof of concept

The data landscapes of companies are becoming increasingly complex. In order to remain competitive and to be successful in the long term, it is essential to efficiently consolidate data from various sources and make it usable for business decisions. SAP Datasphere offers the opportunity to create a central platform for data management that can be used to connect and combine a wide variety of data sources.

Our Data Integration Package for SAP Datasphere tests your individual requirements in a manageable proof of concept (PoC) with an effort of 20 to 30 days. The primary aim of the PoC is to assess the performance of SAP Datasphere in your system landscape.

The proof of concept package includes the integration and consolidation of data from a selection of source systems, including cloud services such as Microsoft Azure, Salesforce, Amazon Web Services (AWS) and enterprise SAP systems such as SAP BW/4HANA and SAP S/4HANA. We combine our best practices in the areas of architecture, data modeling and data governance with your specific business requirements.

Your contact person

Andreas Mayer

andreas.mayer@ibsolution.com

+49 7131 2711-3000

With our Data Integration Package for SAP Datasphere, you will benefit from seamless data integration using SAP Datasphere and the creation of a state-of-the-art data architecture in the form of a business data fabric. This enables you to create the conditions for comprehensive data analyses and informed business decisions.

What are the objectives of the Data Integration Package for SAP Datasphere?

Validation of technical feasibility

Learn how the proposed business data fabric architecture using SAP Datasphere effectively consolidates and harmonizes data from different systems (SAP and non-SAP systems).

Validating data integration and processing

Test the functionality of data pipelines and transformations to ensure that data is seamlessly extracted, transformed, loaded and presented from the various source systems.

Recognizing risks and challenges

Identify potential technical and organizational obstacles that may arise when implementing a business data fabric and develop strategies for overcoming them.

The core elements of the Data Integration Package for SAP Datasphere

Integration of various data sources

Connection of data platforms, taking into account different data formats and structures, in order to obtain a uniform view of the company data

Data harmonization and consolidation

Leverage the data harmonization capabilities of SAP Datasphere to provide consistent and high-quality data for analytical purposes

Data visualization

Integration of the data model into a suitable analytics platform and creation of meaningful dashboards that provide valuable insights into the consolidated data

Validation of the system architecture

Testing the designed architecture with regard to its performance, scalability and reliability in dealing with large amounts of data and complex data integration processes

Business Data Fabric

Creating a scalable and flexible business data fabric architecture that promotes an agile, data-driven organization and supports data-based business decisions

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The scope of the Data Integration Package for SAP Datasphere

Project preparation

Initial workshop: Coordination of project goals, identification of stakeholders, clarification of requirements
Definition of PoC objectives and scope
Planning the project phases, defining schedules and allocating resources

Requirements analysis

Analysis of the existing system landscape
Evaluation of the technical requirements
Examination of the current data landscape
Identification of relevant data sources

Technical conception

Design of the business data fabric architecture with a focus on data integration
Development of data models that represent the interaction of data from different sources
Best practices (Space architecture and naming conventions in SAP Datasphere)

Realization

Configuration of the data connections for merging the data from the various sources
Creation of data models that reflect the business logic
Development of one or more prototypes to demonstrate data integration and visualization

Data integration with SAP Datasphere: Architecture

Data Integration Package for SAP Datasphere Architektur

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