2.1 SAP BW customers—evolving requirements
We see many requirements in the market, and from many classic SAP BW customers; for example, complex and evolved architectures, where company data is stored in a wide variety of data platforms to handle different requirements. So many companies are using hyperscalers for AI or machine learning scenarios. Many are also faced with the challenge that users need a special qualification and many years of experience to understand all the SAP BW functionalities. Companies would like to have a more generic and simpler way for new employees and business users to get up and running on the company’s systems through an SQL approach. Another pain point, for example, is the cost of hardware and the ongoing effort required for upgrades, so companies want to reduce the TCO (total cost of ownership).
Many companies have a complete public cloud strategy to form the foundation for their digital business, so they want to get rid of on-premise SAP BW and move everything to the public cloud.
We previously mentioned complex and evolved architectures. This means that companies have a requirement to establish a central semantic layer over their different data technologies. The goal is to reduce the effort to operate numerous data solutions and keep data/models synchronous.
Another difficulty faced by SAP BW customers is scalability. Scalability can especially help during peak times, such as month-end closing or during cycle planning, to provide more computing power and be more agile, especially when the system is under a heavy load.
Many SAP BW customers want to secure existing investments and not start from scratch. The requirement is to move the various parts in the direction of SAP Datasphere, ideally mostly with automation.
To handle all these different company and market requirements, the business data fabric approach with SAP Datasphere comes into play. There are already a large number of connection options, much more than SAP BW ever had.
Data can be easily integrated, federated, or replicated via standard connections. The data can then be prepared accordingly and integrated into analytic models to replace a regular OLAP Query from SAP BW. Data models can also be made more easily accessible in the desired target technology or offered in the company in the form of data products (see Figure 2.1).
Figure 2.1: Business data fabric with SAP Datasphere
Today, many organizations are struggling to meet their needs with their existing data warehousing solutions. They need to handle architectural complexity and large volumes of business data while preserving business context and offering self-service data access to business users. Companies are therefore looking for scalable and cost-efficient data management solutions in the cloud which support open standards and make data easily accessible while keeping valuable semantics. Moreover, organizations are eager to leverage new innovations such as AI to automate and improve efficiency to unlock the full potential of their business data.
