Unlock the power of data in your SAP ecosystem by applying Data Mesh Principles
In today’s data-driven world, the value of your data assets can define your organizational success. Companies need a powerful strategy to unlock the potential of their data, and the concept of Data Mesh has emerged as a transformative approach. In the following chapters of this blog post, we’ll take a closer look into why Data Mesh with SAP is essential for businesses, and how our expertise can lead you on a transformative journey. Additionally, we’ll highlight when customers should consider integrating data governance and data catalog tools, into their Data Mesh strategy.
Advantages of implementing Data Mesh with SAP Data platform
Implementing Data Mesh principles within SAP’s ecosystem offers a variety of benefits, reshaping the data landscape and enabling businesses to become truly data-driven. Thus, we’ll explore the advantages of applying Data Mesh to SAP:
Improved Data Quality and Governance
Adopting Data Mesh with SAP enhances the data quality and governance by an approach of a decentralized ownership and responsibility for data. This way, data is managed by those who have the deepest knowledge of it, which naturally leads to better quality and governance. By ensuring that data is owned and controlled at the domain level, organizations are able to significantly reduce data quality issues and improve overall data governance.
Accelerated Innovation
Following the principles of Data Mesh, it empowers businesses to accelerate their innovation efforts, because it becomes easier for a wider range of individuals within the organization to access and utilize data for decision-making by democratizing the data access. It is evident, that this inclusive approach encourages creativity and fosters an environment where data-driven innovation becomes a collaborative effort, which leads to faster and more impactful innovations.
Fostering Enhanced Collaboration
With enhanced collaboration, various teams and departments are able seamlessly share insights and data, which leads to more informed decisions, heightened problem-solving abilities, and a more efficient organization. This synergy paves the way for innovation throughout the company.
Unlocking New Revenue Streams: Data Monetization
Enabling efficient and transparent data monetization, organizations can capitalize on data asset by creating valuable data products that can be monetized. This translates to the opening of new revenue streams, which will significantly impact the bottom line. The potential advantages are manifold, from selling data products, providing data-driven services, or leveraging data for innovative offerings, data monetization is a tangible benefit that opens the opportunity for increased revenue and enhanced business sustainability.
What are possible challenges?
Monolithic Data Architectures
Conventional data architectures often suffer from monolithic structures, which makes it difficult to scale and adapt to the dynamics of modern companies. Although SAP environments are powerful, they sometimes can contribute to this challenge by centralizing data control.
Data Silos
Many organizations have grown siloed data, that hinders collaboration and the flow of information within an organization. This situation with isolated datasets make it difficult to obtain holistic insights.
Scalability and Flexibility
As businesses grow, the ability to scale the data infrastructure becomes a key success factor. Conventional architectures struggle to keep pace with growing data volumes and different data sources.
Data Governance Framework
Establishing a robust data governance framework is a critical achievement to maintaining data quality, security, and compliance. SAP environments should be guided by these governance principles to ensure consistency across the data mesh.
How SAP Technologies integrate into an enterprise Data Mesh journey?
When considering the implementing the Data Mesh concept in enterprises, it’s crucial to understand the cultural shift, which is required to move away from legacy approaches.
The following four principles characterize this shift, and they must be accompanied by the right technical services. Fortunately, SAP, both independently and in collaboration with technology vendors like Collibra, Databricks, GCP offers a range of services to facilitate and support the Data Mesh approach. In this blog post, we discuss how SAP Datasphere can play a central role in implementing these four data mesh principles.
#1: Domain oriented ownership
Domain-oriented ownership is the principle of assigning data ownership and accountability to the domain teams responsible for creating and managing it. In SAP Datasphere — “Spaces” allows to create an environment that meets domain requirements. These Spaces serve as dynamic environments that can be tailored to meet the specific domain requirements of an organization. What’s truly remarkable is that these domain teams are given the autonomy to manage essential factors, including user access, connections, schema management, auditing, and resource allocation. In essence, they become the architects of their own data ecosystem.
These domain teams can take various forms, depending on the organization’s structure and needs. They may manifest as self-service teams for Sales, Finance, HR, or can be further segmented into specialized sub-domains. For example, in the Finance domain, teams may be established for General Ledger, Accounts Payable, Accounts Receivable, and more. These are decoupled from each other but are open to flexible access to provide the possibility to collaborate between domains. This requires a diverse skillset in each domain, like the Domain Owner, Data Product Developer, Data Product Consumer to create ready to use data products and use them for data-driven decisions within their domain. The governance Team provides enterprise-wide policies to all domains to meet newest standards and compliance regulations. The data platform team is responsible to maintain and provide the self-service (SAP Business Technology Platform) data platform to the domain team, so the data products can be shared across the company.
However, the principle of “domain ownership” extends beyond mere data management. In addition, a harmonious mix of semantic expertise and specialist knowledge in data management is required. This combination is essential for coordinating and ensuring that domain-specific knowledge about e.g. customers, suppliers, employees, financial frameworks, materials, supply chains, and other critical aspects is seamlessly integrated into the data ecosystem. In response, SAP has published on their website the development of a universal data model known as the “One Domain Model.” This model empowers our customers to provide trusted master data, facilitating the integration of both SAP and non-SAP applications.
#2 Data as a Product
In the ever-evolving landscape of data management, the concept of treating data as a product has emerged as a pivotal paradigm shift. This approach addresses the high friction and costs associated with discovering, understanding, trusting, and effectively utilizing quality data. Data products are consumption ready building blocks that can be consumed by an end user, system, or data product to improve decision making. Following factors contribute for the design of a successful data product.
“Data Marketplace” of SAP Datasphere provides the foundation for this transformative approach. It serves as a hub for both internal and external data sources, seamlessly combining internal data, such as data from the SAP Datasphere, with external sources, including commercial and public data providers. Here, data product sharing and consumption are efficiently managed, ensuring data products can fulfill their intended purpose.
Data products can be published using the Data sharing cockpit which provides easy approach to create and manage the Data products and secure them using the license and context management.
Data consumers can search, discover, and consume trustworthy data products from Data Marketplace into their own spaces.
Define strong governance policies and implement them using the SAP Datasphere Catalog features enabling well documented, consistent use of business language, securely accessed, high-quality and trustworthy data products. Organizations can also assess if they would like to leverage the SAP’s partnership with Collibra and apply an enterprise level data governance and data cataloging.
#3 Self-Service Data Platform
The basis for the distributed approach of data mesh is a self-service data platform, where the domain teams can create, maintain, and consume data products across multiple domains. This includes a variety of services, which need to operate seamlessly.
The reference architecture shown in the figure below provides a comprehensive list of features of SAP Datasphere using which the domain teams have access to a full-fledged self-service data platform.
The platform can connect to different kinds of sources SAP and non-SAP, Cloud and On-Premises etc. Provides ability to both federate and storage data from the source systems. Powerful ETL features, strong data modeling capabilities, even business friendly modeling options via Business builder empower domain teams to work in a seamless way. The interface services are required for users and external applications to interact with the platform.
As announced by SAP and published on their website they teamed up with renowned partners like Databricks, Collibra, Confluent, and DataRobot to revolutionize the way customers manage their data environments using a cutting-edge business data fabric. This collaboration involves deep integration of their data and AI platforms with SAP Datasphere, enabling organizations to seamlessly access their vital business data from any cloud infrastructure, see Figure 4.
#4 Federated Data Governance
Performing the shift from a centralized top-down governance to a federated computational governance to empowering the domain teams to control decisions close to their domains and implementations. The concept is as following: Global policies and standards are set by the central governance council and enforced by the self-service data platform to ensure a certain level of quality and interoperability. In addition, local governance policies are set by the domain teams itself specifically to their data and requirements. This guarantees interoperability and trust in our data products. In addition, we enable a high degree of flexibility by incentivizing domains to bring in their business knowledge as local policies and apply them on their specific data products.
Domain teams leverage the features of self-service data platform like Data access control, Authorization Scenarios of SAP Datasphere and Data Anonymization features of SAP HANA Cloud like k-anonymity, l-diversity, and Differential Privacy. Additionally, the teams can enforce more control by using the features of column selection and filters while publishing a data product and applying license and context management via Data sharing cockpit.
Data Products Example Blueprint
The following picture illustrates a fictional scenario where 2 domain teams are responsible for building data products by consuming data from different kind of source systems and ensuring that all the above mentioned 4 principles are followed.
Start your journey by assessing your current status quo.
Before you start your journey into Data Mesh, you should access their individual status quo by asking the following questions.
- What is our ambition?
- Where do we stand?
- How do we get there?
This figure provides an in-depth exploration of these questions, and Deloitte’s expertise is visible in each offered consulting services, as indicated in Figure 7. Deloitte has developed a range of comprehensive, readily available templates, reference architectures involving multiple technologies over its extensive work with various organizations. Enabling them to craft a robust Data Mesh strategy paves the way for organizations to transition into a data-driven organization
Conclusion
In summary for an organization to embrace the current trends and become an data driven organization, Data Mesh provides an approach into that journey by following the 4 principles and to be successful in this journey one needs to expect the challenges, how to navigate them and prepare for the cultural shifts. Deloitte has prepared a strong framework to make this journey successful with the feedback it has received from its clients.
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