# Implementing Federated Governance in Data Mesh Architecture +
## 3 Data Mesh Governance: Requirements and Challenges
### Properties of Data Governance System  
[PERSON] et al. [16] consider properties required for the data mesh platform. Here, we provide detailed information on each and extend the list with two additional elements, specifically with _contract management_ and _product compositionality_. These properties are based on the years-long research of data platforms, and data lakes in particular [22], principles of the domain-driven design, and data mesh concepts. 1. **Semantic Enrichment (SE)** is related to providing the descriptive information that defines the meaning of the underlying data and facilitates product comprehension and utilization [22]. On the basic level, the use of semantic tagging helps in relating different data sources, e.g., 'Financial' data, 'Medical' data, etc. On a more advanced level, the use of semantic web technologies and knowledge graphs can be implemented [21]. This can provide automatic product descriptions, data linking, relationship discovery, etc. 2. **Data Indexing (DI)** is a core element for efficient metadata querying and navigation. Almost all modern data storage providers have indexing functionality that enables fast data look-ups. In the data mesh case, the metadata system should provide the search capabilities to retrieve the data product information based on a diverse set of user requests: keywords, history, ownership, usage pattern, etc. 3. **Link Generation (LG)** helps to identify and integrate the related information [22]. For instance, the use of identical semantic tags helps to cluster the products into different groups. Data lineage establishes the tracing history of the upstream dependencies and enables the user alert or automatic product suspension in case of breaking changes. Product linking can also consist of similarity measurements, which is a further extension of product clustering with a relevance degree. 4. **Data Polymorphism (DP)** or polyglot data is related to the operation and management of multiple product facets [5]. Since in data mesh the data consumption is conducted via connecting ports, the same product can have multiple consumers as well as multiple forms. For instance, real-time analytics may require the streaming data interface while monthly reports do not need much time precision. Moreover, the data can have variable degrees of quality, representation (tabular data, vector), maturity, etc. 5. **Data Versioning (DV)** describes the management of metadata updates within the system while retaining the previous metadata states. It is very relevant since it ensures the system state recovery, reproducibility, and auditing. Moreover, versioning allows branching, enables backward compatibility, and parallel data product evolution [22]. 6. **Usage Tracking (UT)** consists of providing and tracking access to the underlying data resources. It is a backbone for implementing the security and protection of data. Data security comprises multiple elements, such as information and communication encryption, firewall, etc [23]. In fact, in the aspect of metadata management, it is important to keep records of product access, user identities, usage patterns, locations, time schedules, etc., in order to detect and prevent unauthorized activities. * **Computational Policies (CP)** play a vital role in automatic governance execution. Beyond the access control enforcement, it also enables data quality verification, consistency, uniqueness, lifecycle management, service contract tests, etc [5]. This reflects the need to define the rules on each level--global and local, which are then applied in the mesh. Such governance execution also requires an appropriate platform infrastructure as well [10]. * **In the context of micro-service architecture, Independently Deployable (ID)** products provide the opportunity to update the performed services without the overall system interruption (e.g., canary deployment) [17]. In the context of data mesh, it means an option to deploy the new data products without affecting the established data consumption.