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The Business Analytics Platform ensures a clean data basis and forms a reliable foundation for data-driven business decisions. With IBsolution’s holistic framework, companies can address and overcome their most pressing data challenges.
The holistic approach takes into account not only technological aspects, but also the organization and the people without whom a data-driven company cannot function. Accompanying Organizational Change Management is an indispensable component of the Business Analytics Platform and ensures that employees accept the changes and that a new (data) culture can emerge in the company.
The Business Analytics Platform consists of different layers that build on each other. Companies usually have a variety of data sources. These can be on-premise or cloud systems from SAP or other providers. The data is available there in structured or unstructured form. The Integration Layer brings the data from the source systems to the Data Layer, where it is persisted and stored.
The data layer is usually distributed heterogeneously – across SAP and non-SAP systems. Among SAP systems, SAP BW on HANA or SAP BW/4HANA are the most common in the on-premise area and SAP Datasphere in the cloud area. The non-SAP systems are typically SaaS applications that are part of at least one of the three major hyperscalers Microsoft Azure, Google Cloud Platform and Amazon Web Services (AWS). The Virtualization Layer is necessary to create a unified view of the heterogeneous data in the Data Layer so that the various reporting disciplines can build upon it.
The Business Analytics Platform is flanked by the topics of (master) data governance and data mesh. To ensure that the insights and decisions derived from corporate data are reliable, the data quality must be high across the various layers. Data governance provides the appropriate structures and processes to ensure permanently high data quality. A data mesh creates the organizational basis for a different way of handling data in the company by decentralizing responsibility for the provision and quality of data to the business departments.
Lack of standards and guidelines for:
Permissions
Accesses
Capture
Storage
Destruction
Security
Privacy
Consistency
Quality
Data Lineage
Data Ownership
Data Democracy
Increasing demands of employees for data
Real-time data delivery
Increased user value
Faster adaptations
Skill shortages
Sufficient skill sets of employees
Lack of or dispersed know-how
Shorter and shorter upskilling cycles due to higher complexity and a constantly changing work environment
Continuously increasing amount of required tool sets, technologies and development stacks
Too little flexibility for adjustments and changes
Poor reaction speed
Low level of automation
Lack of operationalization
Lack of data lifecycle management
New types of data from business processes
Behavioral data
Streaming data from IoT applications
Semi-structured data
Unstructured data
Existing systems are often too expensive for big data applications
No or only low scalability
Complex ETL/ELT processes
Redundancies in data storage
Lack of virtualization
No benefit from available data
Large increase in data volumes
Mindset of employees
Lack of willingness to change and learn
Lack of commitment from management and employees
Data democratization
No cross-departmental working due to data silos
Lack of training and development processes
Lack of change management
Since the launch of SAP Datasphere in March 2023, various blogs, videos and other articles have appeared that primarily deal with the technical aspects of the successor product to SAP Data Warehouse Cloud.
Data Mesh represents a paradigm shift and paves the way to a domain-driven data architecture. Each business department is responsible for the definition, quality, and creation of data from its own domain.
