As the Data and Analytics leader, ensuring robust data SLA adherence is paramount for maintaining data integrity and reliability. DQLabs takes a holistic approach to ensure robust data SLA (Service Level Agreement) adherence across the pillars of Data Observability, addressing the key aspects of reliability, relevance, and overall data health.

This is encapsulated in a three-fold process involving Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs).

 

Step	Description
SLIs	Selecting the Right Metrics: Our platform offers an extensive array of checks, providing a comprehensive list that encompasses the health of data, data pipelines, data infrastructure, cluster analysis, cost-per-query analysis (FinOps), and security metrics. These checks are semantically augmented, providing a contextual understanding of their significance. Users can choose from a diverse set of semantic-augmented metrics to precisely measure the performance and health of their data ecosystem.
SLOs	Performance Goals with AI Augmentation: Setting up performance goals for each metric is a critical step in data SLA adherence. DQLabs leverages the power of Artificial Intelligence (AI), Generative AI (GenAI), and Semantics in this process. Through a combination of supervised learning, unsupervised learning and custom inputs our platform automates the assignment of accepted levels for each metric. This ensures a more efficient and accurate determination of performance goals, reducing the manual effort required in traditional approaches.
SLAs	Call to Action for Deviations: The final step involves the formulation of data SLA, where predefined actions are specified in case of deviations from the accepted levels outlined in SLIs and SLOs. This ensures a structured response mechanism to address any anomalies and maintain the desired standards of data quality. The platform supports full audit and stewardship across all activities as well.

Details below on how the Modern Data Quality platform executes this:

Real-Time Monitoring Dashboards:

DQLabs provides sophisticated, built-in dashboards tailored for Observability and Data Quality. These dashboards serve as a control center, offering real-time insights into the performance of diverse data processes. 

Users can easily navigate through visualizations and metrics, gaining a holistic view of key parameters such as data freshness, completeness, accuracy, and availability. DQLabs offers semantic-driven metrics, allowing organizations to define, measure, and track the reliability and fitness for the purpose of data throughout its lifecycle.

Dynamic Metric Measurements:

Our platform allows organizations to define and track essential Service Level Indicators (SLIs), which serve as dynamic metrics for evaluating data quality. For instance, SLIs can encompass measurements like volume consistency, schema conformity, and duplicate record prevention. By establishing these SLIs, enterprises gain granular insights into the health of their data. Infact, the data SLA monitoring is conducted at various levels, including domain, application, and organizational levels, or even through custom groupings made possible by using tags.

Targeted Performance Goals:

DQLabs facilitates the creation of precise Service Level Objectives (SLOs) aligned with SLIs. These objectives set specific performance goals for each metric, providing a clear understanding of what constitutes acceptable data quality. For example, an SLO might define the permissible percentage of data duplications within a specified dataset.

Comprehensive Data SLA Definitions:

Building upon SLIs and SLOs, organizations can articulate comprehensive data SLA or Service Level Agreements . These data SLAs serve as contractual agreements that encompass predefined consequences for data SLA deviations. Actions embedded in data SLAs may include halting changes to data pipelines, triggering automated remediation workflows, or initiating escalation procedures through Slack, Jira etc.

Automated Alerting Mechanisms:

DQLabs empowers users to configure automated alerts triggered by any deviations or violations of established data SLAs. These alerts serve as early warning signals, allowing organizations to swiftly respond to potential data quality issues. For instance, if data freshness falls below the agreed-upon threshold, an automated alert can notify relevant stakeholders, enabling timely intervention.

Root Cause Analysis and Remediation Workflow:

In addition to monitoring and alerting, DQLabs offers robust root cause analysis capabilities. When data SLA deviations occur, our platform provides detailed insights into the underlying causes. This information is pivotal for initiating targeted remediation workflows, ensuring that corrective actions are precise and effective.

Data Lineage for Visibility:

Understanding the journey of data from origin to consumption is crucial for data SLA adherence. DQLabs provides comprehensive data lineage tracking, allowing organizations to trace the flow of data across the entire ecosystem. This visibility ensures that data SLA requirements are met at each stage of the data lifecycle.

Example Scenario:

Consider a scenario where an organization has set a data SLA for data freshness, requiring data to be updated within a specific timeframe. DQLabs, through its monitoring dashboards, can visually depict the current status of data freshness in real-time. If a deviation occurs, automated alerts are triggered, prompting users to investigate the root cause using the platform’s detailed analysis features. Once identified, a remediation workflow can be initiated, and necessary actions, such as halting affected data pipelines, can be executed. 

An example of  how an organization may use SLI, SLO and data SLA using DQLabs. 

SLIs are quantifiable and agreed-upon measures of the data. E.g.,, 

1. Freshness – table has to be updated periodically 
2. Null – checks for % of null at a column level
3. Enumerations or Valid Values – cannot have any new unknown values and automatically checked for from what is known. 
4. Pattern Distribution – checks column level values for any formatting issues or new formats that may cause pipeline failures. 
5. Duplicate – no duplicates using a composite key or one or more column values 

SLOs are targets set for the performance of the various attributes measured by SLIs. E.g.,, 

1. Freshness – table has to be updated every 24hrs 
2. Null – checks for % of null not to exceed 10%
3. Enumerations or Valid Values – no new values with zero tolerance
4. Pattern Distribution – no new formats with zero tolerance 
5. Duplicate – duplicates can be tolerated to less than or equal to 2%

Data SLAs are agreements not only that the SLI will stay within the SLO, but also define what happens when that SLO is not met and extend to a remediation plan.  For example, if all of those 5 SLIs are not met or even if one SLI such as duplicate doesn’t meet the 98% reliability over the 30 days resulting in up to 14.61 hours of downtime. An example from an actual customer is that anything above the 2% will impact downstream operational analytical models used in route planning or a price estimate model for an automotive industry involved in buying and selling used cars. If the data team doesn’t meet the expectation set, then it results in impacting the downstream application and the use of data will be halted until it is fixed.

📊🔍DATA TEAMS – Ready to elevate your data quality? Book a consultation with DQLabs today and kickstart your Data Quality journey guided by Data SLAs. Explore how DQLabs can empower your organization to proactively monitor, analyze, and remediate data quality issues throughout the data lifecycle.