# Implementing Federated Governance in Data Mesh Architecture +
## 3 Data Mesh Governance: Requirements and Challenges
### Challenges of Federated Data Governance  
Data governance refers to a comprehensive framework encompassing various processes, roles, policies, standards, and metrics, all aimed at improving the extraction of business value from data while staying compliant. This framework should provide a platform where technical engineers, business stakeholders, legal representatives, and other involved parties can easily define business processes and regulatory policies, assign data access roles, and shape the platform's operations.  
Centralized governance lays down a unified set of rules and standards for all teams, ensuring that data systems can work together smoothly. However, this approach faces scalability challenges, as was shown by the limitations identified in data warehouse and data lake systems [19]. On the other hand, decentralized governance offers greater autonomy to individual domain teams, which can lead to the creation of incompatible data products, redundant development efforts, and overlooked compliance procedures.  
[PERSON], the pioneer of the data mesh concept, underscores the necessity for federated computational data governance [5]. This approach seeks to balance domain autonomy with overall system interoperability, merging the strengths of both centralized and decentralized governance. Federated governance aims to facilitate collaboration and simplify the processes of defining and accessing data schemas, extending semantic knowledge, and providing data lineage. It also strives to automate the enforcement of security, transparency, and legal standards. Nevertheless, the challenge lies in building such a system with the currently available tools and research.  
In our introductory section, we noted that metadata management systems often serve the roles of data governance systems in research environments. Nonetheless, in the literature [25; 26; 27; 28; 29], and in the commercial products [30; 31; 32] we see the predominant adoption of a centralized governance model. This model involves gathering metadata in a central repository, where it is analyzed, interconnected retrospectively, and often made accessible to users through web portals.  
Recent studies have explored the implementation of federated data governance using semantic web technologies [13; 21], though these systems are still in their developing stages.  
One of the approaches to making the federated governance, which is getting more attention in the data mesh community is \"data contracts\". [PERSON] et al. [24] highlight the need for data contracts in managing the use, sharing, and transaction of data within cloud marketplaces. This provides a structured method to define the rights, obligations, and constraints associated with data usage, ensuring clarity and compliance across different parties. They address key challenges such as specifying permissible data use, establishing liability in case of data misuse or quality issues, and enabling automatic and efficient data exchange and utilization. Furthermore, data contracts contribute to the scalability of data marketplaces by automating compliance and governance processes. By formalizing these aspects, we facilitate a trustworthy and regulated environment for data sharing, promoting innovation and value creation within data ecosystems.  
Consequently, there is a significant research gap in the design and development of federated governance systems, signaling a pressing need for advancement in this field. In the next section, we attempt to provide the formal model for building the federated metadata management system while using the data contract approach.