A Brief Introduction to Data Contracts

Data is one of the key factors driving change in this century. It powers massive systems, including AI, that can perform complex tasks that captivate the world. However, for these systems to perform very well, they must rely upon quality and seamless data. So, how do we ensure seamless and high-quality data? This is where data contracts come into play.

What Is a Data Contract?

A data contract is an agreement between a data producer and a data consumer, which might be between stakeholders, systems, or organizations. It serves as a handshake between the two parties to agree on the structure, semantics, and expectations of the data being exchanged.

Why Are Data Contracts Important?

Data contracts are important because they provide a structured and enforceable agreement between stakeholders, organizations, or systems regarding the nature of the data. They ensure data quality, mandating that data pass through rigorous checkpoints and rules to ensure its consistency, reliability, and lack of errors.

If a data contract is not in place, for example, a data entry individual in the sales department or a software engineer building a customer app could update services that will break downstream systems or affect the data consumers due to data quality issues.

Types of Data Contracts

Data contracts fall into two broad categories: technical-level data contracts and legal-level data contracts.

Technical-Level Data Contracts

These are types of data contracts that are enforceable programmatically. They are usually common with individual teams or stakeholders and are then implemented in data systems or products. They include things such as:

• Schema—This refers to the constraints agreed upon for a data attribute or a column, such as data types.
• Business logic—This refers to checks put on data to ensure it meets functional criteria within the business context—for instance, the range of expected data, whether it accepts null values, etc.
• Service-level agreements (SLAs)—SLAs commit to the data's availability. They define the data's functionality, including availability and how data producers will update new data. For example, SLAs may include checks for the latest expected data update time due to regular system updates. This is very crucial, and platforms such as Acceldata’s data observability solution help organizations meet these SLAs by providing key insights into data pipelines, ensuring data trust, and driving innovation.

Legal-Level Data Contracts

This is another type of data contract that organizations typically agree upon rather than individual teams, and it is not enforceable programmatically. It includes:

• Data-processing agreement—This agreement outlines how a third party processes data on behalf of a company. Companies often use it when they outsource data processing to another entity. The agreement details responsibilities, security measures, and compliance with regulations like the General Data Protection Regulation (GDPR).
• Data-sharing agreement—This provides a commitment to a policy related to data sharing. It refers to an agreement that binds organizations to specific terms for sharing data. These agreements specify usage restrictions, confidentiality requirements, and how organizations can share and use the data.
• Data-use consent—This agreement is used to obtain consent from individuals or organizations about the use of their data. This type of consent is typically required under data protection laws such as GDPR.

Key Components of Data Contracts

• Definition and scopes—This component contains the clearly defined elements of the data models, such as data attributes and their scope in terms of data type, expected data range, etc.
• Quality and standards—This component contains the quality and standards set for the data attributes that conform with the business logic.
• Privacy and security measures—These represent checks put in place to ensure the privacy of sensitive information or how to deal with cases of sensitive information in the data.
• Roles and responsibilities—This component defines the roles of each party involved in the data exchange or management process. It clarifies who owns the data, who manages it, and who is responsible for its protection.
• Compliance and regulatory requirements—This component includes compliance with legal frameworks like GDPR or other privacy laws and sets rules for audits and legal obligations.
• Data retention and destruction policies—These refer to the formal guidelines and methods that govern how long an organization has to keep data and how to dispose of it when it's no longer needed or legally required.

Benefits of Data Contracts

• Data quality—By specifying the data quality standards and validation rules in a data contract, the contract ensures that the incoming data meets the predefined criteria and reduces data error. Additionally, this will make debugging as fast as possible in case of error, which saves time and resources for manual quality checks.
• Transparency and trust—Data contracts help provide transparency. Data producers and consumers are all aware of the data content, thereby increasing trust.
• Data consistency—Another benefit is that it ensures the data format and structure are consistent across the data collection, processing, and usage stages.
• Effective collaboration and communication—It ensures effective collaboration between both parties as both parties, i.e., data producer and data consumer, come together to agree on a data contract collectively.

Challenges with Implementation

• Alignment across teams and stakeholders—Data contracts often involve multiple teams agreeing with different goals and understandings of data. For example, the sales department may have a different understanding or meaning to a particular column than the finance department. So, this could be a challenge in data contracts in establishing a common agreement on schema, semantics, and expectations.
• Evolving data and business need—As the business grows and becomes more complex, it will need more data to make informed decisions. This creates a challenge, as the business must maintain and adapt the data contract to the current reality. So, designing data contracts that can adapt to changing business needs is a significant challenge.
• Legal and regulatory complexity—Due to diverse data protection laws, developing a data contract to comply with all these laws could be challenging. For example, a company operating in both the U.S. and European Union will ensure that its data contract includes specific clauses for laws in these different regions.
• Technical complexity—Integrating data contracts into existing data infrastructure and ensuring compatibility across diverse data sources and formats could be challenging.

Best Practices for Implementing Data Contracts

• Training and awareness—Ensure the training and awareness of all relevant staff in the organization about data contract principles and obligations. This also includes nontechnical teams or staff, such as people in the sales department of an organization responsible for data entry.
• Involve all stakeholders—Before building a data contract, all relevant teams—ranging from data producers, including software engineers and nontechnical teams, to data consumers like data analysts and data scientists—must define and agree on its terms.
• Regular monitoring—Data contracts have to be frequently reviewed or monitored to ensure contract compliance. This involves checking whether or not all parties comply with the terms of the agreement and ensuring that data is handled securely; accurately; and in accordance with technical, legal, and regulatory requirements.
• Ensure clear communication—To prevent misunderstandings and misalignment, all parties should clearly define and understand all terms in the data contract.
• Technical integration—The organization must ensure that the technical infrastructure and capabilities align with the contract terms before implementing a data contract.

Conclusion

In conclusion, data contracts serve as a means to ensure data quality, consistency, and trust between data producers and consumers. Data contracts establish clear agreements on the structure and expectations of the data. This improves collaboration and fosters a positive work culture. It also reduces data errors and ensures compliance with technical and legal requirements.

However, while it might be challenging to align teams and adapt the contract to evolving business needs, following best practices such as regular monitoring, clear communication, and involvement of all stakeholders can overcome these issues. The best way to ensure effective monitoring of data along with automated rigorous quality checks is to invest in a data observability solution. 

Acceldata’s data observability solution enables organizations to observe their data stack further to improve data quality, platform performance, and SLAs. This, in turn, helps organizations maintain their data trust and drive innovation in an increasingly data-driven world. Request a demo to get started.

This post was written by Ibrahim Ogunbiyi. Ibrahim is a data scientist and an IT support specialist. He loves writing anything about  data and IT operations.

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