8 critical principle you should never miss while designing “Data Contracts”

Introduction

In the realm of modern data architectures, the concept of a “Data Mesh” has emerged as a paradigm shift, addressing the complexities inherent in handling vast and varied data across large organizations. Central to this architecture is the notion of ‘Data Contracts.’ This article aims to elucidate the concept of data contracts, their critical role in data mesh architectures, and the key parameters to consider while implementing them.

What are Data Contracts?

Data contracts represent a formal agreement between data producers and data consumers. They define the structure, format, quality standards, and access policies of the data being exchanged. Much like a legal contract in the business world, data contracts ensure that all parties involved have a clear understanding of their responsibilities and expectations regarding data.

Importance(of data contracts) in Data Mesh Architecture

1. Decentralization and Domain-oriented Design:

Data Mesh advocates for a decentralized approach to data management, where data is owned and managed by domain-specific teams. Data contracts in this context provide a structured way for these teams to communicate and share data, maintaining autonomy while ensuring interoperability.

2. Data Quality and Consistency:

By establishing clear guidelines on data formats and quality, data contracts help in maintaining consistency across different domains. This is crucial in a data mesh, where diverse data sources can lead to fragmentation and inconsistencies.

3. Compliance and Security:

In a landscape with increasing regulatory requirements, data contracts include compliance rules and security protocols, ensuring that data sharing adheres to legal and ethical standards.

4. Facilitating Data Discovery and Governance:

They help in cataloging data, making it easier for users to discover and understand what data is available, its source, and its fitness for use.

How do you implement Data Contracts?

There is no easy answer, but certainly there are widely recognized standards to keep it well organized. I am going to explain one of the well structured framework today, Open Contracting Data Standard (OCDS)

What is OCDS?

OCDS is a set of standards designed to enhance transparency and accountability in public contracting. It provides a structured and standardized format for the publication and dissemination of data regarding public procurement processes. This standardization enables more accessible, consistent, and comparable data across different sectors and geographical locations.

Importance of OCDS

1. Transparency and Accountability: OCDS promotes open access to public procurement data, allowing for scrutiny by citizens and organizations, thereby fostering transparency and reducing corruption.
2. Standardization: It offers a common framework for data representation, facilitating easier data sharing and comparison across different entities.
3. Improved Procurement Outcomes: By making procurement processes more transparent, OCDS helps in achieving better value for money, reducing fraud, and encouraging fair competition.
4. Enhanced Data Utilization: Standardized data formats improve the usability of data, aiding in analysis, policy-making, and public understanding.

The diagram outlines eight critical categories that form the backbone of a comprehensive data contract, providing a roadmap for both the contributors to and the consumers of data within an organization. Below, we unpack each of these categories to elucidate their significance in the data contract ecosystem.

1. Fundamentals: This foundational category captures the essential identifying details of the data contract. It includes the contract’s name, version number, and descriptive elements that convey its purpose and scope. These fundamentals serve as the anchor point for all stakeholders, ensuring that everyone is aligned on the basic parameters of the data agreement.
2. Datasets & Schema: Datasets and schema represent the structural heart of the data contract. They include the physical storage and logical design of the data, encompassing how it is organized, related, and managed within databases. This category ensures that the data’s architecture is clearly defined, facilitating efficient storage, retrieval, and manipulation.
3. Data Quality: Quality is paramount when it comes to data. This category outlines the rules and governance policies that dictate the expected standard of data, addressing its accuracy, completeness, and reliability. It ensures that data consumers can trust the data they use, which is critical for analytics and decision-making processes.
4. Pricing: In many cases, data comes with a cost. This component of the data contract addresses any fees associated with data acquisition, whether from external sources or internal cross-charging mechanisms. Transparency in pricing helps in budgeting and the evaluation of the data’s return on investment.
5. Stakeholders: The history of stakeholders involves documenting the various parties that have interacted with the data over time. This might include data owners, custodians, users, and auditors. Keeping a detailed stakeholder history enhances accountability and clarifies roles and responsibilities within the data lifecycle.
6. Security: Security is non-negotiable in the context of data contracts. This section specifies the roles and responsibilities of individuals regarding data access and protection. It ensures that data is safeguarded against unauthorized access and breaches, which is crucial for maintaining the integrity and confidentiality of sensitive information.
7. SLA (Service Level Agreement): The SLA category defines the performance expectations surrounding data use. This includes the latency in data access, retention policies governing how long data is stored, and the frequency of data updates. SLAs are crucial for setting benchmarks that align with business needs and regulatory requirements.
8. Custom: Recognizing that not all data needs are uniform, the custom category provides flexibility within the data contract. It allows for the incorporation of unique requirements that cater to specific business contexts, regulatory landscapes, or technological capabilities.

Appealing it so far? Think how much challenging it can go to implement in your organization?

Let’s understand implementation considerations,

The implementation of the Open Contracting Data Standard (OCDS) in an organization involves setting up an architecture that supports the standardized collection, management, and publication of procurement data. Implementing OCDS can be a complex process, depending on the existing systems and the scope of the procurement data. Below is a general outline of how the architecture for implementing OCDS might be structured:

1. Assessment and Planning

• Evaluate Existing Systems: Assess the current procurement systems and data practices to determine how they align with OCDS requirements.
• Identify Data Sources: Identify all sources of procurement data that need to be integrated into the OCDS format.
• Stakeholder Engagement: Involve stakeholders from procurement, IT, legal, and other relevant departments.

2. OCDS Data Model Adaptation

• Map Existing Data to OCDS: Adapt the current data model to align with the OCDS schema, which involves mapping existing data fields to those prescribed by OCDS.
• Identify Gaps and Extensions: Identify any gaps in the data and determine if any extensions to the standard schema are needed to cover specific requirements.

3. Data Extraction and Transformation

• Data Extraction Tools: Implement tools for extracting data from existing systems.
• Data Transformation: Transform the extracted data to conform to the OCDS format. This might involve data cleaning and conversion processes.

4. Database and Storage

• Central Database: Establish a centralized database to store OCDS-compliant data.
• Data Storage Policies: Ensure that the database aligns with data storage policies, especially regarding data security and privacy.

5. Publishing Or API Development

• API for Data Access: Develop an Application Programming Interface (API) to allow easy access to the OCDS data.
• Publishing Platform: Set up a platform for publishing OCDS data, ensuring it is accessible to the intended users.

6. Quality Assurance and Validation

• Data Quality Checks: Implement routines to ensure data quality, completeness, and accuracy.
• OCDS Validation Tools: Use OCDS validation tools to ensure compliance with the standard.

7. Integration with External Systems

• Interoperability: Ensure the OCDS data system can interact with other external systems, if necessary.
• Data Sharing Agreements: Establish agreements for data sharing with external entities, if applicable.

8. User Interface and Reporting

• User-Friendly Interface: Develop interfaces that allow users to interact with the OCDS data easily.
• Reporting Tools: Implement tools for generating reports and insights from the OCDS data.

9. Compliance and Legal Considerations

• Legal Compliance: Ensure that the OCDS implementation adheres to all relevant legal and regulatory requirements.
• Data Governance Policies: Establish clear data governance policies.

10. Training and Capacity Building

• Staff Training: Train staff on OCDS principles and the new system.
• User Education: Educate users and stakeholders on how to access and use the OCDS data.

11. Monitoring and Maintenance

• Continuous Monitoring: Regularly monitor the system for performance and adherence to OCDS standards.
• Feedback Mechanisms: Implement mechanisms to gather feedback and continuously improve the system.

12. Documentation and Transparency

• Maintain Documentation: Keep detailed documentation of the OCDS implementation process.
• Promote Transparency: Use the OCDS implementation as a tool to promote transparency in public contracting.

Examples of OCDS meta data formats

Examples are refereed from official repo — below example is to show how you can describe your schema,

- table: da_lookup_countries
  columns:
  - column: country_code
    businessName: look-up country code
    logicalType: string
    physicalType: varchar(2)
    authoritativeDefinitions:
      - url: https://collibra.com/asset/748f-71a5-4ab1-bda4-8c25
        type: Business definition
      - url: https://github.com/myorg/myrepo
        type: Reference implementation

Implementing OCDS requires a multifaceted approach, involving technical system adjustments, stakeholder engagement, compliance considerations, and ongoing management. The successful implementation of OCDS can significantly enhance the transparency and effectiveness of public procurement processes.

Conclusion

In the data mesh architecture, data contracts are not just a technical necessity but a cornerstone for ensuring seamless, secure, and effective data collaboration across diverse domains. By meticulously implementing data contracts with the above parameters in mind, organizations can harness the full potential of a decentralized data ecosystem, leading to improved data governance, quality, and value generation.

The adoption of the Open Contracting Data Standard (OCDS) can be a transformative step for any organization looking to revamp its data management practices, particularly those embarking on projects that involve complex data contracts. By implementing OCDS, organizations can ensure that the principles of transparency, accountability, and standardization are deeply embedded in their data ecosystems.