High-quality data is important to the operation of every organization, but how do you best ensure the data quality? One way to maintain data quality is an SLA—a service level agreement that defines and sets target levels for data quality in your organization.
Quick Takeaways
- A service level agreement (SLA) details the provided services to be provided and standards to be met
- By specifying data quality metrics and target levels, data SLAs can help organizations maintain high data quality
- A data SLA includes six key elements: purpose, promise, measurement, ramification, requirements, and signatures
- Data SLAs benefit all parties involved, including data engineers, data team leaders, and data consumers
What Is an SLA?
A service level agreement (SLA) is an agreement between a service provider and a customer that details the services the provider will furnish and what standards the provider will meet. Providers use SLAs to manage customer expectations and for customers to ensure they’re getting the services they paid for.
A data SLA is an agreement between two parties to provide a specified level of service regarding data storage or delivery. For example, a data SLA might specify a certain percentage of uptime and what the provider will do if that promise is unmet.
SLAs can be between a company and its paying customers or between one department and another within a company. For internal SLAs, the provider is the department providing a particular service and the customers of those other departments or employees receiving that service.
Why SLAs Are Important
SLAs, both internal and external, help avoid misunderstandings, minimize disagreements and provide clarity for both parties involved. Unlike informal promises that parties can easily misinterpret (and just as easily ignore or break), an SLA provides a formal commitment from one party to the other—a commitment that both parties mutually agree to.
Within a company, the IT or data team functions as a service provider to internal users, and data SLAs codify the data team’s level of accountability. While internal SLAs typically don’t include fines or refunds for unacceptable service, as most external SLAs do, they still detail the responsibility owed to internal customers.
Data SLAs are especially important for measuring and tracking data reliability. Given that poor data quality costs the average organization $15 million each year, it’s essential that data coming into and used within an organization be of the highest possible quality.
A data SLA builds trust and strengthens relationships between all parties, whether internal or external. It helps to minimize unreasonable assumptions on the part of data users while holding data providers to a defined set of data quality standards.
Data SLAs also ensure that all parties are using the same data metrics—speaking the same language, as it were. This helps eliminate misunderstandings and puts all parties on the same page regarding data quality.
How Can You Use SLAs to Maintain Data Quality?
One of the most effective ways to ensure high-quality data is to create a data SLA that focuses on the core aspects of data quality. A data SLA between a data provider and its consumers should define key quality metrics and set reasonable targets for those metrics.
A data SLA goes beyond saying “we want high-quality data” to defining what “high-quality data” means. This way, the service provider can focus on those areas impacting data quality the most, and the consumers of that data know acceptable data quality targets are met.
In particular, a data SLA should define and set targets for the following data quality metrics:
- Accuracy, how correct the data is
- Completeness, whether all fields are filled in
- Consistency, whether the data is consistent across systems and over time
- Timeliness, the age of the included data
- Uniqueness, measuring duplicate records
- Validity, or how well data conforms to desired formatting
How to Structure a Data SLA
A data SLA is a written document, typically around 250-500 words, agreed upon by both parties. It typically includes six key components:
- Purpose
- Promise
- Measurement
- Ramification
- Requirements
- Signatures
Purpose
The SLA should start by addressing why the document exists. You should delineate what issues the SLA covers and how the use cases for the document.
Promise
Next, define what exactly the provider is guaranteeing to the clients. These service level objectives (SLOs) are the target ranges the provider agrees to hit, such as guaranteeing 99% accuracy or no more than 5% duplicate records.
Measurement
This section defines how to measure the objectives and in what time frame. These service-level indicators (SLIs) spell out the key data quality metrics and who will measure them.
Ramification
It’s important to clarify what happens if the provider does not achieve the stated objectives. There could be fines or reduced pricing, or the entire agreement might be nullified.
Requirements
This section details what the client provides for the services rendered. While this might be unnecessary in an internal agreement, it’s essential for external SLAs.
Signatures
Finally, the SLA must be agreed upon by both parties and signed by appropriate representatives.
(The following video discusses the differences between SLAs, SLIs, and SLOs.)
Who Benefits from a Data SLA?
A data SLA offers benefits to all parties involved. In particular, a data SLA provides value to:
- Data engineers
- Data team
- Data consumers
How Data SLAs Help Data Engineers
Data SLAs help data engineers benefit from improved communication with stakeholders. Because the SLA clearly defines data quality and how it’s to be measured, there is less confusion over what is acceptable data quality.
In addition, the promises spelled out in a data SLA help data engineers better prioritize their efforts. If the data SLA promises a certain level of data accuracy, for example, engineers know to focus their resources on improving data accuracy.
Finally, data engineers benefit from being able to quantify their success. They either meet the requirements of the SLA, or they don’t. There’s no fuzziness surrounding their performance.
How Data SLAs Help Data Team Leaders
Data SLAs help data team leaders prioritize their teams’ efforts. Leaders know that they need to focus on the goals promised in the SLA over other work. Only when the SLA’s promises are assured should they redirect their teams’ efforts to other projects. The guarantees in an SLA also provide a way for team leaders to measure their teams’ efforts.
How Data SLAs Help Data Consumers
Data consumers benefit from the promises guaranteed in a data SLA. They know that the performance metrics promised in the SLA will be met or that penalties will ensue. It provides confidence in the service provider and gives the data consumer one less thing to worry about. This is important in a world where 60% to 85% of data initiatives fail because of poor quality data
A data SLA also helps consumers define what is or is not acceptable performance. They no longer have to argue with providers about the level of service provided. The SLA spells out promises precisely.
Ultimately, higher quality data has the biggest impact on consumers from data SLAs. Because service providers focus on those areas defined in the SLA, they provide better service and higher quality data to all their clients.
Let DataBuck Improve Your Organization’s Data Quality
When working with a data SLA, turn to the experts at FirstEigen to ensure clean, accurate, and up-to-date data in your organization. Our DataBuck software is an autonomous data quality management solution that automates more than 70% of the data monitoring process. Use DataBuck to improve the quality of data in your organization and get the most possible value out of all the data you collect.
Contact FirstEigen today to learn more about data SLAs and data quality.
Check out these articles on Data Trustability, Observability, and Data Quality.
- 6 Key Data Quality Metrics You Should Be Tracking (https://firsteigen.com/blog/6-key-data-quality-metrics-you-should-be-tracking/)
- How to Scale Your Data Quality Operations with AI and ML (https://firsteigen.com/blog/how-to-scale-your-data-quality-operations-with-ai-and-ml/)
- 12 Things You Can Do to Improve Data Quality (https://firsteigen.com/blog/12-things-you-can-do-to-improve-data-quality/)
- How to Ensure Data Integrity During Cloud Migrations (https://firsteigen.com/blog/how-to-ensure-data-integrity-during-cloud-migrations/)
