Data Mesh with SAP Business Technology Platform, BTP: efficiency, insights and innovation

Modern companies are constantly looking for new methods to manage their data efficiently and grow as a data-driven organization. One particularly promising approach is the implementation of the data mesh principle with the SAP Business Technology Platform (BTP). This cloud platform integrates various SAP services and bundles on-premises and cloud-native technologies. Data Mesh is a modern data management concept that enables companies to provide data more efficiently and tailored to specific business objects such as customers, materials or projects from a strategic and architectural perspective. It determines which objects from the data fabric can best be used to provide employees with data. Data Mesh determines how different domains and data products can be linked together to provide interesting information for customers and suppliers.

83% of companies with more than 1,000 employees are trying to establish data mesh in their company. This was the result of a survey conducted by auditing firm PwC in 2023. By using data mesh, companies can improve their data quality and increase their flexibility and agility in dealing with it. This is crucial for making data-based strategic decisions and tapping into innovation potential.

Master data optimization

In order to develop strategies on the basis of data, it is essential that this data is available in high quality. A central element is the optimization of master data. Only if it is properly recorded in the system can it be optimally used and combined. Many companies face the challenge of isolated data silos, which makes it difficult to access important information. By integrating and harmonizing these silos, information can be used more efficiently. Quality and consistency are the basic prerequisites for reliable analyses and decisions. Measures to ensure this are therefore essential. Flexible and agile data management systems are also necessary in order to be able to react quickly to changes in the market and business environment. Traditional data warehouse architectures often reach their limits here, particularly when it comes to extracting, harmonizing and storing data from different sources.

The implementation of a data mesh in conjunction with the SAP Business Technology Platform (BTP) represents a modern approach to overcoming the above-mentioned challenges in data management. As a unified, extensible and scalable platform, the BTP combines innovative technologies and proven methodologies. Its key features include database and data management, integration and extension capabilities, and analytics solutions with intelligent technologies such as AI and Robot Process Automation (RPA).

When implementing the data mesh concept in companies, data products serve as the core to fulfill analytical requirements. They are the basis for structured and cross-domain data exchange. A practical example of this is official sales reporting in the financial sector, which is made available to other company domains. A data product, such as the "order item mart", contains comprehensive information on the product, channel, organization and relevant key figures. This data is assigned exclusively and used uniformly throughout the company. This enables consistent further use in various analytical applications, self-service evaluations, plug-ins and dashboards.

The importance of a structured form of data exchange is particularly evident in the customer journey. Here, different stakeholders with different analysis requirements and solutions need to access data from neighboring areas. In contrast to a central data lake, the data mesh approach allows the responsibilities, structure and content of the data products to be clearly defined. This makes the existing data exchange across system boundaries more efficient.

Clear principles for successful implementation

The successful implementation of a data mesh is based on the consistent application and integration of four fundamental principles. These include domain ownership, data as a product, self-service and computational federated governance. These four principles aim to maximize the efficiency and effectiveness of data provision and use. They address several challenges that arise with traditional centralized data architectures, such as scalability, data quality and consistency, flexibility and innovation (see green box).

Implementing a data mesh involves several steps: identifying domains and business objectives, setting up spaces and data containers and using data fabric for data virtualization. By defining and managing business objects, it is possible to retrieve data precisely, which increases the quality and relevance of the information provided. In addition to the necessary infrastructure, the SAP Business Technology Platform supports this process with the right tools to successfully implement Data Mesh. The combination of Data Mesh and SAP BTP helps companies to optimize their agility, data quality and resource efficiency, which ultimately brings financial benefits.

Companies use data mesh concepts for a 360° customer view, for example, to optimize their own decision-making processes and improve customer loyalty. In HR management, they can use the combination of SuccessFactors and machine learning to create fluctuation analyses and forecasts in order to optimize their HR strategy. The data mesh concept can also be used to create completely new business models.

Implementing a data mesh with SAP BTP offers numerous advantages such as improved data quality and availability, increased flexibility and agility as well as support for self-service data analyses. However, companies must overcome challenges such as data governance and security aspects as well as technical and organizational hurdles. A strategic implementation of SAP BTP and the data mesh is a decisive step towards a future-proof IT landscape. IT partners such as the SAP consulting firm NTT Data Business Solutions, which now employs over 100 consultants and developers for SAP BTP, support companies in the individual implementation of the data mesh principle. The aim is to improve the provision of logistical, financial and insurance services. At the same time, a definition of architectural and technical guidelines is offered.

Clear principles for successful implementation

Four principles aim to maximize the efficiency and effectiveness of data provision and use. They address several challenges that arise with traditional centralized data architectures, such as scalability, data quality and consistency, flexibility and innovation.

Domain ownership describes the decentralized responsibility for data of operational domain teams. This allows the individual departments of a company to develop and provide their own data products in smaller, modular parts. This decentralization allows them to react more quickly to changes and facilitates the scalability of their data products. The principle of data as a product guarantees the quality of the data, for example in terms of structure and comprehensibility. Specialist departments create and manage their own data products, which leads to improved data quality and consistency. As these teams have a deeper understanding of how their data is generated and used, they can ensure that it complies with company standards.

Self-service allows data products to be created, maintained and used by different teams from different departments according to the guidelines of a central control team. Departments can work independently of central IT teams, enabling faster development of new data products and innovative solutions. In addition, the end user is able to perform master data enrichment with self-service components, for example to create simulations based on external data.

Finally, the principle of federated governance ensures that centrally defined standards for the cross-platform exchange and use of data products apply in a standardized and consistent manner across the entire company. This leads to more efficient IT teams that can concentrate on technological tasks and make better use of their resources, while specialist departments act more independently.

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