# Security and Ownership in User-Defined Data Meshes
## 2 Technical Background
### Understanding Data Lakes and Data Meshes
Figure 2: Data mesh architecture and the concept algorithmic approach. ensuring a comprehensive and efficient data handling process. By adopting a DM approach, organizations aim to address the challenges of scaling data operations in a complex environment, where multiple teams work on diverse data domains. It provides a framework for decentralizing data ownership and enabling more effective, scalable, and resilient data operations. Conversely, a data market is an ecosystem or marketplace where individuals, companies, or systems can buy, sell, or exchange data by leveraging the idea of DMs. Data suppliers in a data market offer datasets for purchase or access by data consumers for a range of applications, such as analysis, research, machine learning, and more [7]. Data markets facilitate the efficient sharing and monetization of data, allowing businesses to leverage external sources of information to enhance their insights and decision-making processes. Using a large manufacturing company in the field of poultry farming and poultry meat trading as our case study and example demonstrator, we were able to identify various operational areas, including livestock records, agricultural data, supply chain management information, financial transactions, and trading analytics. Each operational area is assigned to a specialized team responsible for its monitoring and upkeep. Moreover, each team is tasked with generating specific data products tailored to their respective domains, such as APIs for accessing data, algorithms for analyzing trading trends, tools for secure data sharing, and reporting mechanisms for financial analytics. This makes the proposed approach an ideal way of sharing portions of data across authorized groups (e.g., departments) or individuals. Adopting a federated computational governance approach ensures that each team defines and enforces the computational logic for their specific domain, facilitating the implementation of specialized algorithms and quality checks. Additionally, each team has access to a self-serve data infrastructure, equipped with tools and resources for managing their data products independently, thereby ensuring autonomy and operational efficiency. To enhance interoperability within the field, the implementation of APIs and adherence to industry standards are prioritized, allowing seamless communication and data exchange between different operational areas. This approach contributes to the optimization of data management and the creation of tailored products, ultimately benefiting stakeholders in poultry and farming trading, including producers, traders, and administrators. It is important to note that while DMs are more about organizational and conceptual principles for data management, DLs refer specifically to the technology and infrastructure for storing large volumes of raw data. These concepts are not mutually exclusive, and, in practice, organizations can implement a DM framework while utilizing a DL as the underlying basic component of their technical infrastructure for data storage and processing.