Founding Father Benjamin Franklin knew a few things about a few things. However, what he couldn’t know is how much one of those things relates to the use of data in our modern age—that it’s far easier to prevent a fire than it is to put one out.
This insight, the basis of the proverb “An ounce of prevention is worth a pound of cure,” came from Franklin’s work to counsel city leaders in 18th-century Philadelphia, helping them deal with particularly hazardous urban fires.
Because Franklin knew what the stakes were. He’d witnessed firsthand how panic and disorganization prevented any response to a blaze from being efficient, coordinated, and effective. And he realized that, when everything around you is on fire, success at best can only be less of a failure.
So it goes with data, especially with the sheer impact it has on modern business, society, and well, life in general. But here, in our more modern world, Franklin’s favoring ounces over pounds relates squarely to data contracts and the role they play in preventing our need for high-quality data from erupting into flames.
That said, as vital a role as they’re already playing, data contracts are a newer concept to many, even unheard of by some. So let this article serve as our proverbial Bucket Brigade, raising awareness of the value of data contracts by defining exactly what they are, how they work, and valuable ways they can be used.
A data contract is a formal agreement between two or more parties that outlines specific terms and conditions of data sharing. These contracts can exist between organizations, systems, or individuals.
Where traditional contracts exist as written or spoken agreements, data contracts tend to encompass a combination of documents, tools, and artifacts that provide clear specifications, assurances, and systems to monitor and manage the data exchange between parties.
We now live in a world where the amount of new data created each day, structured by complex data architecture, rests comfortably in the quintillions of bytes. In this world, where business leaders work to enable digital transformation initiatives and more data-driven decision-making, data contracts are proving to be invaluable.
In practice, the formal agreement that data contracts represent will marshal the exchange, handling, storage, and usage of data. Contracting parties agree to ensure that any data outlined in the contract is managed accurately and securely, remaining in compliance with relevant regulations.
These agreements aren’t hypothetical. A contract only works if it can be enforced. This is why data contracts that involve software should be programmatic.
Operationally, contracts will commonly include the following:
Schema definitions: A data contract should explicitly define the schema, including semantics, so the structure of data is clear. As part of a contract, schema definitions may involve specifying which formats, structures (e.g., Avro or JSON), and data types will be used.
Validation rules: These rules make sure all ensuing data adheres to the defined schema and will meet data quality standards the contracting parties require.
Access control: Data contracts may specify who approved data producers and data consumers will be for the duration of time specified within the contract.
Data flow management: Contracts typically outline how datasets should flow between specific systems like data pipelines and data ingestion processes. They may also determine how data should be managed as it moves between contracting parties, such as data engineers and software engineers.
Communication protocols: API specifications and communication protocols may also be outlined to standardize the data exchange between systems and platforms.
Versioning: Data contracts implement versioning to manage changes made to the data schema. This management ensures that all changes, including breaking changes, will be handled in a way that won’t disrupt existing systems.
Dependency and metadata management: To make sure changes don’t negatively impact dependent systems, dependencies between different data entities may also be managed as part of a data contract. Data contracts may also dictate how metadata will be managed and exchanged between systems, ensuring data will be understandable and usable.
Compliance and auditing: Data contracts will ensure that all data exchange and management adhere to legal and compliance requirements. Mechanisms for auditing and tracking data usage and changes can also be specified.
Error handling: Finally, as much as data contracts work to make sure things go right, they should also guide responses when things go wrong. Defining how errors and discrepancies in data will be handled ensures that any issues that arise will be logged, addressed, and communicated properly.
Having defined what a data contract is and what it should typically entail, we can turn to how they’re implemented.
While implementation specifics of a data contract will certainly vary from case to case, the blend of technical, organizational, and governance practices will, more or less, consist of the following nine aspects:
Effective data contracts begin with defining the landscape in which they’ll operate. At their most basic, these landscapes should include all relevant stakeholders and use cases to which the contract will pertain.
As part of the stakeholder identification process, it’s important to understand their needs and challenges. Use cases can contain as much or as little detail deemed necessary, but should at least include the specific data requirements of both data producers and data consumers. With this foundational information, the ideal duration of the data contract can also be outlined.
Next, a clear and comprehensive scheme should be defined using formats like YAML, JSON, or Avro to detail data structure, formats, and types. However, know that the data contract does not need to describe the entire data schema. Comprehensive as it relates to data contracts refers to everything that will be useful and can be well-owned.
Doing so ensures schema changes that occur over the course of the contract will not disrupt existing systems. Further, contract parties should align how metadata will be managed to ensure valuable context about data will be accurate and accessible.
Validation rules can then be established to ensure optimal data quality and consistency. Take email for instance—are we validating that information entered in an email field is actually a real address? Or are we employing value-based validation, ensuring for example that numbers added to an age field are never less than zero?
Data quality metrics should also be identified at this time. Doing so ensures all parties agree on how the data quality will be defined, which metrics will be monitored, and the role the data contract will play in ensuring adherence to these standards.
While this step may seem straightforward, it shouldn’t be undervalued—especially considering the humbling costs poor data quality enacts on businesses each year.
Based on the constraint, different enforcements of the contract, such as those involving SQL queries, will be needed depending on the technologies used. This makes sense as, for example, the actualities of protecting against NULLS in a database like MySQL will differ from checking value constraints in Kafka.
API design should also be accounted for, as needed. If APIs will be used for data exchange, they should be designed to follow GraphQL or RESTful principles, as per requirements.
A format like JSON or Avro will need to be chosen for appropriate data serialization. Plans to implement robust data ingestion mechanisms and data pipelines that will handle data flow also need to be outlined.
Specifics regarding data quality, latency, and availability will be clearly defined and minted in the data contract’s SLA. Any service-level agreement will also require monitoring to ensure adherence by all contracting parties. Monitoring and detection pre-deployment here is also essential for identifying any post-implementation issues proactively.
Security factors should involve the use of access control mechanisms that will regulate who can produce and consume data. The contract should clearly establish how data management and data exchange will comply with all relevant legal and regulatory requirements. Working in parallel, outlined data governance policies can further guide data management throughout its lifecycle.
Data contracts should be authored to facilitate collaboration between data engineers, other data teams, and data consumers affected by the contract. This collaboration relies on clear lines of communication being established and maintained among stakeholders. Moreover, providing as much context as possible bi-directionally helps teams changing the contract understand why constraints are needed while keeping consumers aware of how and when updates are being made.
Communication is key for managing expectations and providing the right updates at the right time throughout the course of the contract. That said, a data contract should at least include the basics of how productive lines of communication will be established and maintained—and who is responsible for doing so. Error logging and a defined resolution workflow must be established as part of the drafting process. Contract owners should also be alerted if the contract itself is ever violated.
While the contract is being drafted, the formalities of robust documentation and change management must also be captured. While both are important parts of the data contract process, each is its own distinct element. They support the contract by providing clarity and the framework for managing changes and maintaining records.
Documentation during the course of the data contract should be a clear and clearly accessible chronicle of operating facets of the data contract, including schemas and workflows.
Changes in the data contract or schema should be recorded separately to ensure they can be communicated to stakeholders as needed.
As a data contract actively guides the use of data in a given situation, feedback loops need to be established with data consumers and producers. Feedback provides valuable information that can be used to continuously improve how the data contract functions.
Feedback loops also enable contracting parties to adopt an iteration approach to making these improvements, gradually enhancing and refining the data contract as needs evolve.
With the previous aspects defined, appropriate tools and technologies can be chosen to put the contract to work. Automation should be implemented if and when possible in order to streamline the data management and validation processes.
Once completed, data contracts require thorough testing to ensure they meet all requirements and handle edge cases effectively. This testing should also validate the performance of data exchange mechanisms under various loads and scenarios.
Finally, to stitch the why of data contracts together with their respective what and hows, the following three distinct use cases illustrate their importance and application in practice.
It’s increasingly common for financial institutions to rely on real-time data streaming—essential for making instantaneous decisions related to trading, fraud detection, and customer service.
But, with stakes this high, data must be accurate, timely, and reliable. Data discrepancies or delays can cripple decision-making and lead to financial losses.
In this use case, data contracts provide the following:
Healthcare ecosystems present their own substantial regulatory and privacy concerns, as stakeholders such as doctors, hospitals, insurance companies, and research institutions need to share sensitive patient data to function at their best.
The challenges here then involve not just data privacy and consistency, but also extensive interoperability required among diverse systems to ensure compliance with regulations like HIPAA (Health Insurance Portability and Accountability Act).
In this use case, data contracts provide the following:
Retailers engage with supply chains that can consist of a huge network of logistics providers, manufacturers, and suppliers. Doing so enables retailers to keep products manufactured, transported, and stocked efficiently.
With 74% of supply chain leaders reporting increasing their investment in technology and innovation in 2023, the need to guarantee traceability, transparency, and efficiency within retail supply chains increasingly hinges on the availability of consistent, high-quality data.
In this use case, data contracts provide the following:
In the ever-evolving landscape of data management, the age-old wisdom of thought leaders like Benjamin Franklin remains strikingly relevant. If “an ounce of prevention is worth a pound of cure,” the importance of proactive measures in data handling cannot be overstated.
Data contracts, as highlighted in this article, serve as that crucial ounce of prevention, safeguarding businesses from potential pitfalls and ensuring seamless data operations. As we move forward in this digital age, the value of such preventive measures only increases.
For those looking to stay ahead of the curve and embrace the future of data management, we invite you to experience the cutting-edge solutions offered by Gable.ai. Join our product waitlist and be on the right side of data history.