Data Reliability: What is it and how to engineer it?
Introduction: The Challenge of Reliable Data
Most organizations have some kind of data quality processes in place, yet consistently delivering reliable data remains an elusive goal, so what’s missing from the traditional approach?
If your focus is simply on measuring the data quality dimension of the final product, then your data quality efforts are just treating the symptoms, because you are totally neglecting the underlying data and processes responsible for data generation.
For example, if you were working in a factory producing knives: a blade that cuts perfectly once but does not guarantee consistent performance over time is essentially worthless.
To deliver a reliable knife, you must go beyond checking the final knife and ensure that quality raw materials are used, and the manufacturing processes are set up correctly.
These same manufacturing principles apply to your data factory. But most organizations, unfortunately, are focused only on measuring the data quality dimensions in production, while ignoring:
- Testing data pipelines during development.
- The raw data and files used as input in production.
- Monitoring data pipelines in production.
Since the root cause of a production data defect might be due to improper development and/or operations, simply checking the final data will not help. This results in inconsistent data quality, leaving you with data defects in production that are very expensive to mitigate.
Hence, the focus needs to change and remain on delivering reliable data. Let’s dive deeper into the concepts of data reliability and data reliability engineering.
Data Reliability: What Exactly is it?
Data reliability refers to the additional promise of delivering consistent data quality over time. It extends beyond a snapshot of data quality by incorporating a temporal dimension of delivering data quality consistently and dependably over time.
In a real-world example, when you, as customers, check product inventory on a website, you will expect items marked as “In Stock” to be available. However, if the underlying processes populating the data are flawed, the inventory data will be partially accurate. This will hamper your user’s experience and will clearly frustrate you.
Data Reliability is time dependent. Hence, to deliver it consistently, you need to look beyond data quality into the development process, operations of data pipelines and the raw data used by the data centric system.
This is exactly where a reliable data system differentiates itself: it ensures consistent delivery of data over time.
Key Characteristics of Data Reliability:
Data reliability takes a comprehensive approach to ensure data dependability from development to collection, processing, execution, storage, and use. This includes:
- Testing and certifying data pipelines in development.
- Verifying raw data and files before processing in production.
- Monitoring the data pipelines in production.
- Observing the data generated in production.
- Measuring and tracking data reliability metrics.
Measuring Data Reliability
Measurement is a key aspect that differentiates data reliability from data quality. Unlike data quality dimensions, data reliability uses distinct metrics to assess the overall health of the data system. These key metrics include:
- Mean Time to Failure (MTTF): Average time before system failure
- Mean Time Between Failures (MTBF): Average operational time between failures
- Mean Time to Repair (MTTR): Average time to restore system functionality
- Failure Rate: Frequency of system failures
- Availability: Probability of system operational readiness
- Reliability Function: Probability of consistent performance over time
Key Differences: Data Quality vs. Data Reliability
| Aspect | Data Quality | Data Reliability |
| Timeframe | Moment-in-time snapshot | Consistent performance over time |
| Scope | Final production data | Entire data ecosystem - input data, files, data pipelines, and infrastructure |
| Metrics | Data Quality Dimensions such as Accuracy, Completeness, and more | Reliability metrics such as MTTF, MTBF, system availability |
| Environment | Production only | All environments: Development, QA, and Production |
| Approach | Reactive inspection | Proactive engineering |
The 3 Building Blocks of Data Reliability
In the data factory analogy, organizations navigate two crucial phases before delivering final data: building the data factory and running it effectively. Mistakes at any point in this lifecycle directly impact the final data quality.
A. Build the Data Factory and Data Testing: In this phase, requirements are collected, processes are coded, and the data pipelines are orchestrated.
- Why is data testing important in data reliability?
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- Incorrectly captured requirements.
- Bugs in data processing code.
- Scheduling and orchestration errors.
- Corner or edge test cases.
- Deployment issues.
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B. Run the Data Factory and Data Monitoring: Once the data factory is assembled, it starts receiving data, and all the processes begin running in an orchestrated manner. Apart from the defects from the build phase, additional data errors can be introduced due to a lack of operational checks and controls.
- Why is data monitoring crucial for data reliability?
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- Poor quality of raw input data or files.
- Scheduling delays.
- Unauthorized configuration or code changes.
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C. Final Production Data and Data Observability: Any carry-over defects that were missed in the prior two phases, along with ongoing challenges can still cause defects in final production data.
- Why is data observability critical for data reliability?
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- Defects that escaped detection in the earlier two phases can surface later.
- Business changes: Evolving business processes, rules, and reference data can introduce new errors in data.
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We’ve seen that just checking data quality at the very end isn’t enough. Instead, a more comprehensive approach called data reliability engineering is needed. Let’s explore data engineering in the next section.
What is Data Reliability Engineering
Data Reliability Engineering involves the systematic application of the best engineering practices and techniques, integrating people, processes, and products, to ensure the delivery of reliable data.
This practice identifies potential failures, analyzes their root causes, and implements measures to prevent or mitigate them by scientifically designing, developing, testing, operating, and monitoring throughout the data development cycle.
Key Strategies for DRE Implementation
Successful implementation of DRE requires the three P’s: People, Processes and the iceDQ Platform in all phases of data development life cycle.
- Data Reliability Engineer: A data reliability engineer is a professional who builds, runs, and manages a data-centric system to deliver reliable data consistently over time. They do this by ensuring that the system is built, and data is tested properly. They employ proper data monitoring and data observability. Their primary goal is to minimize failures and downtime, thereby maximizing efficiency and reducing costs.
- Automate Data Testing: One of the first steps towards DRE is to automate data testing, ensuring that each ETL process is tested before being deployed into production.
- Enable Chaos Testing with Full Data: Instead of sampling data, use the complete data set for testing. This will ensure complete test coverage by discovering any corner test cases missed during analysis.
- Adopt Integrated QA + QC: Ensure that not only quality controls are done on the final data, but also that the tooling and data pipelines are thoroughly tested.
- Adopt Left Shift: Shift the focus from the end of the data development lifecycle to the left by ensuring business users are involved in requirements gathering, establishing data audit rules and ensuring that the checks and controls are established in operations.
- Deploy Whitebox Monitoring: A part of code deployment is not only deploying the ETL code but also embedding checks. This will allow the operations team to monitor the production pipelines with minimal effort.
- Create Organizational Memory: Store all the testing, monitoring and data observability rules in a centralized repository that can be accessed by teams over a long period of time.
- Monitor Operations: Establish checks and controls in the production pipeline to capture any data issues before they can cause damage in the downstream systems.
- Observe Production Data: Observe the production data for anomalies and notify incidents in real time as they happen, with supporting information for triage and root cause analysis.
- Measure Failure Rate and Magnitude: Measure and report both the frequency and magnitude of data errors.
Conclusion: Your Path to Data Reliability
By adopting a holistic data reliability approach, you can significantly reduce the hidden effort involved in correcting data issues. This leads to direct savings in costs and time, and improved delivery timelines. Most importantly, it helps build a reputation for delivering reliable, high-quality data.
Transforming your approach from reactive data quality to proactive data reliability isn’t just a technical upgrade – it’s a strategic business decision.
By adopting Data Reliability Engineering, you:
- Reduce hidden data management costs
- Improve operational efficiency
- Build trust in your data ecosystem
- Minimize risks of data-related failures
Ready to Get Started?
- Assess your current data reliability
- Implement automated testing
- Create a culture of data observability
- Continuously monitor and improve
If there’s anything else you’d like us to cover in this blog, let us know by tweeting @iceDQ
Sandesh Gawande
CEO and Founder at iceDQ.
First to introduce automated data testing. Advocate for data reliability engineering.