What is Data Reliability and How Observability Can Help

Data reliability looks at the completeness and accuracy of data, as well as its consistency across time and sources. The consistency piece is particularly important, as data needs to be consistent to be truly reliable, that way it’s always trustworthy.

Data reliability is one element of data quality.

Specifically, it helps build trust in data. It’s what allows us to make data-driven decisions and take action confidently based on data. The value of that trust is why more and more companies are introducing Chief Data Officers – with the number doubling among the top publicly traded companies between 2019 and 2021, according to PwC.

In this article:

• Why is data reliability important?
• Data quality vs. data reliability
• Data reliability vs. data validity
• What is a data quality framework?
• Data reliability issues and challenges
• Basic steps to ensure your data is reliable
• How can observability help improve data reliability?
• Top data reliability testing tools?

Measuring data reliability requires looking at three core factors:

1. Is it valid? Validity of data looks at whether or not it’s stored and formatted in the right way. This is largely a data quality check.
2. Is it complete? Completeness of data identifies if anything is missing from the information. While data can be valid, it might still be incomplete if critical fields are not present that could change someone’s understanding of the information.
3. Is it unique? The uniqueness of data checks for any duplicates in the data set. This uniqueness is important to avoid over-representation, which would be inaccurate.

To take it one step further, some teams also consider factors like:

• If and when the data source was modified
• What changes were made to data
• How often the data has been updated
• Where the data originally came from
• How many times the data has been used

Overall, measuring data reliability is essential to not just help teams trust their data, but also to identify potential issues early on. Regular and effective data reliability assessments based on these measures can help teams quickly pinpoint issues to determine the source of the problem and take action to fix it. Doing so makes it easier to resolve issues before they become too big and ensures organizations don’t use unreliable data for an extended period of time.

All of this information begs the question: What’s the difference between data quality vs. data reliability?

Quite simply, data reliability is part of the bigger data quality picture. Data quality takes on a much bigger focus than reliability, looking at elements like completeness, consistency, conformity, accuracy, integrity, timeliness, continuity, availability, reproducibility, searchability, comparability, and – you guessed it – reliability.

For data engineers, there are typically four data quality dimensions that matter most:

• Fitness: Is the data fit for its intended use, which considers accuracy and integrity throughout its lifecycle.
• Lineage: Where and when did the data come from and where did it change, which looks at source and origin.
• Governance: Can you control the data, which takes into account what should and shouldn’t be controllable and by whom, as well as privacy, regulations, and security.
• Stability: Is the data complete and available in the right frequency, which includes consistency, dependability, timeliness, and bias.

Fitness, lineage, and stability all have elements of data reliability throughout them. Although taken as a whole, data quality clearly encompasses a much larger picture than data reliability.

Data reliability and data validity address two distinct aspects of data quality. In the context of data management, both of these qualities play a crucial role in ensuring the integrity and utility of the data at hand. Note that data validity is sometimes considered a part of data reliability.

Data reliability, narrowly defined, focuses on the consistency and repeatability of data across different observations or measurements. Essentially, reliable data should yield the same or very similar results each time a particular measurement or observation is repeated. It’s about ensuring that the data is stable and consistent over time and across different contexts.

Data validity, in the sense of data validation, concerns the accuracy, structure, and integrity of the data. It ensures that the data is formatted correctly, complies with the necessary rules, and that it’s accurate and free from corruption. For instance, a date column should have dates and not alphanumeric characters, or a primary key field in a database should have unique values. Invalid data can lead to a variety of issues, such as application errors, incorrect data analysis results, and overall poor data quality.

While these two concepts are related, they’re not interchangeable. For example, you might have a highly reliable data collection process (providing consistent and repeatable results), but if the data being collected is not validated (it doesn’t conform to the required rules or formats), the end result would still be low-quality data.

Conversely, you could have perfectly valid data (meeting all format and integrity rules), but if the process of collecting that data is not reliable (it gives different results with each measurement or observation), the utility and trustworthiness of that data becomes questionable.

It is vital to ensure both data reliability and data validity. To maintain the reliability of data, a consistent method for collecting and processing data must be established and adhered to. For data validity, rigorous data validation protocols must be in place. This could include things like data type checks, range checks, referential integrity checks, and others, to ensure that the data is in the right format and adheres to all the necessary rules.

A data quality framework allows organizations to define relevant data quality attributes and provide guidance for processes to continuously ensure data quality meets expectations. For example, using a data quality framework can build trust in data by ensuring what team members view is always accurate, up to date, ready on time, and consistent.

A good data quality framework is actually a cycle, which typically involves six steps largely led by data engineers:

1. Qualify: Understand a list of requirements based on what the end consumers of the data need.
2. Quantify: Establish quantifiable measures of data quality based on the list of requirements.
3. Plan: Build checks on those data quality measures that can run through a data observability platform.
4. Implement: Put the checks into practice and test that they work as expected.
5. Manage: Confirm the checks also work against historical pipeline data and, if so, put them into production.
6. Verify: Check with data engineers and data scientists that the work has improved performance and delivers the desired results, and check that the end consumers of the data are getting what they need.

Data reliability poses a considerable challenge in many areas of research and data analysis. The key issues and challenges associated with data reliability include:

• Data collection: The way data is collected can greatly affect its reliability. If the data collection method is flawed or biased, the data will not be reliable.
• Measurement errors: Errors can occur at the point of data collection, during data entry, or when data is being processed or analyzed. These errors can reduce the reliability of data. For example, if a faulty instrument is used for measurement, it may consistently provide incorrect data.
• Data reproducibility: Data needs to be reproducible to be reliable. However, reproducibility can be challenging due to a variety of factors such as variability in the data collection process or lack of detailed documentation about how the data was collected or processed.
• Data consistency: Data must be consistent over time and across different contexts to be reliable. Inconsistent data can arise due to changes in measurement techniques, definitions, or the systems used for data collection.
• Human error: Human error is always a potential source of unreliability. This can occur in many ways, such as incorrect data entry, inconsistent data coding, misinterpretation of data, and more.
• Incomplete data: Missing or incomplete data can also lead to reliability issues. If data is missing randomly, it may not significantly affect the reliability, but if the missing data follows a certain pattern, it could lead to biased and unreliable results.
• Changes over time: In some cases, what is being measured can change over time, causing reliability issues. For instance, a machine learning model predicting consumer behavior might be reliable when it’s first created, but over time, as the underlying consumer behavior changes, the model might become inaccurate unless it is re-trained. 

Ensuring data reliability is a fundamental aspect of sound data management. Here are some best practices for maintaining and improving data reliability:

1. Standardize data collection: Establish clear, standardized procedures for data collection. This can help reduce variation and ensure consistency over time.
2. Train data collectors: Individuals collecting data should be properly trained to understand the methods, tools, and protocols to minimize human errors. They should be aware of the importance of reliable data and the consequences of unreliable data.
3. Regular audits: Regular data audits are crucial to catch inconsistencies or errors that could affect reliability. These audits should not just be about finding errors but also about identifying root causes of errors and implementing corrective actions.
4. Use reliable instruments: Use tools and instruments that have been tested for reliability. For example, if you’re using stream processing, test and monitor event streams to ensure data is not missed or duplicated.
5. Data cleaning: Employ a rigorous data cleaning process. This should include identifying and addressing outliers, missing values, and inconsistencies. Use systematic methods for handling missing or problematic data.
6. Maintain a data dictionary: A data dictionary is a centralized repository of information about data, like meanings, relationships to other data, origin, usage, and format. It helps maintain data consistency and ensures everyone uses and interprets data in the same way.
7. Ensure data reproducibility: Documenting all the steps in data collection and processing ensures others can reproduce your results, an important aspect of reliability. This includes providing clear explanations of methodologies used and maintaining version control for data and code.
8. Implement data governance: Good data governance policies can help improve the reliability of data. This involves having clear policies and procedures about who can access and modify data and maintaining clear records of all changes made to datasets.
9. Data backup and recovery: Regularly backup data to avoid loss of data. Also, ensure that there’s a reliable system for data recovery in case of data loss.

Data observability is about understanding the health and state of data in your system. It includes a variety of activities that go beyond just describing a problem. Data observability can help identify, troubleshoot, and resolve data issues in near real-time.

Importantly, data observability is essential to getting ahead of bad data issues, which sit at the heart of data reliability. Looking deeper, data observability encompasses activities like monitoring, alerting, tracking, comparisons, analyses, logging, and SLA tracking, all of which work together to understand end-to-end data quality – including data reliability.

When done well, data observability can help improve data reliability by making it possible to identify issues early on to respond faster, understand the extent of the impact, and restore reliability faster as a result of this insight.

Understanding the importance of data reliability, how it sits within a broader data quality framework, and the importance of data observability is a critical first step. Next, taking action to invest in it requires the right technology.

With that in mind, here’s a look at the top data reliability testing tools available to data engineers. It’s also important to note that some of these solutions are often referred to as data observability tools since better observability leads to better reliability.

Databand is a data observability platform that helps teams monitor and control data quality by isolating and triaging issues at their source. With Databand, you can know what to expect from data by identifying trends, detecting anomalies, and visualizing data reads. This allows a team to easily alert the right people in real time about issues like missing data deliveries, unexpected data schemes, and irregular data volumes and sizes.

Datadog’s observability platform provides visibility into the health and performance of each layer of your environment at a glance. It allows you to see across systems, apps, and services with customizable dashboards that support alerts, threat detection rules, and AI-powered anomaly detection.

Great Expectations offers a shared, open standard for data quality. It makes data documentation clean and human-readable, all with the goal of helping data teams eliminate pipeline debt through data testing, documentation, and profiling.

New Relic’s data observability platform offers full-stack monitoring of network infrastructure, applications, machine learning models, end-user experiences, and more, with AI assistance throughout. They also have solutions specifically geared towards AIOps observability.

Bigeye offers a data observability platform that focuses on monitoring data, rather than data pipelines. Specifically, it monitors data freshness, volume, formats, categories, outliers, and distributions in a single dashboard. It also uses machine learning to set forecasting for alert thresholds.

Datafold offers data reliability with features like regression testing, anomaly detection, and column-level lineage. They also have an open-source command-line tool and Python library to efficiently diff rows across two different databases.

In addition to these five tools, others available include PagerDuty, Datafold, Monte Carlo, Cribl, Soda, and Unravel.

The risks of bad data combined with the competitive advantages of quality data mean that data reliability must be a priority for every single business. To do so, it’s important to understand what’s involved in assessing and improving reliability (hint: it comes down in large part to data observability) and then to set clear responsibilities and goals for improvement.