Thought leadership
April 25, 2023

The complete guide to understanding data reliability

Your one-stop shop for all things data reliability. Learn what data reliability is, who uses it, how it works, use cases, and best practices.

Kyle Kirwan

What is data reliability?


How dependable is your data? “Data reliability” is the measure that answers this question, for a set of applications.

Those applications might be simple, like a dashboard with some metrics on it. Or complex, like the machine learning model behind a computer-vision system that identifies shoplifting in real time.

Applications depend entirely on the data feeding them. Therefore, interruptions to the flow of data have an impact. Those interruptions—or “outages”—cost money and cause headaches. The higher-impact the application, by definition, the more disruptive an outage will be.

In a state of perfect data reliability, a team removes guesswork and makes informed decisions, backed by the data they have on hand. It’s simple: if you have achieved “data reliability” within your organization, that means your data is accurate and complete.

Data reliability is the end-goal state for any organization relying on data (spoiler alert: that’s probably every organization). Through data reliability, organizations build trust across their entire ecosystem of internal and external stakeholders.

“Data reliability” is a relatively new term. You may see shades of gray in comparing it to terms like “data quality” and “data observability”, but it is different. We’ll explain how in the upcoming paragraphs. Data reliability is an essential piece of an organization’s overall data health.

What does assessing data reliability look like in practice? Let’s take an analytics dashboard for a revenue leader, for example. The following questions help inform the reliability of the data:

  • Does the data need to be refreshed every 15 minutes, or is every 24 hours good enough?
  • What exactly is the data being used for, across all functions and departments?
  • Does the data need to be correct to the penny, or is rounding up to the thousand-dollar mark ok?
  • If the data isn’t fresh enough, or isn’t accurate enough, what are the business impacts?

The importance of “reliable enough”

When data is consistently reliable, it can be depended on to feed applications with data that’s fresh enough and high quality enough. The word “enough” is important here. Reliability only matters insofar as the data is good enough for the applications using it. Investing beyond that point in the freshest, highest-quality data possible is a wasted effort.

Just as in devOps, data reliability is measured in “nines” of uptime. If data is flowing as expected for 713 hours out of a 720-hour month (that’s 30 days), then it met it was 99% reliable (or “two nines”). That accounts for 7 hours where there was some issue either with the freshness or quality of the data that impacted its dependent applications. If a team cuts those 7 hours down to just 43 minutes, then the data becomes 99.9% reliable (or “three nines”).

As a team works to make their data more reliable for a wider array of applications, their organization will come to trust (and expect) that the data “just works” all the time. In turn, that expectation unlocks more creativity and investment into data-driven applications. This virtuous cycle is how some of the world’s most successful companies leverage data as a competitive advantage.

Why is data reliability important?

Organizations rely on data for decision-making. Executives working with bad data are probably making bad decisions.

The goal of every data team should be to create a virtuous cycle for their organization. In this cycle: the data is trusted, so various teams use it to drive improvements, so the business realizes those benefits, they invest further in their data, that data supports further improvements, and so on.

Any impact to reliability can stunt this cycle, because it hinders the data feeding these applications. If teams attempt to use data but it’s wrong or confusing, they’ll be hesitant to rely on it the next time around. If data is trustworthy, teams will use it.

Data-driven applications can be powerful. But data can also add more risk. De-risking requires a higher degree of reliability. A commitment to data reliability is a commitment to your organization’s future.

Data reliability also helps justify the investment in various BI and analytics tools. Those tools are meant to automate data analysis and dashboard reports. When they work correctly and data is reliable, your teams can speed up calculations, analyses, and time-to-market for new features and products.

Data reliability reassures stakeholders and customers. When you can collect, process, handle, and manage data while keeping under compliance and governance regulations, you earn long-term trust and respect from your customers.

What are the benefits of data reliability?

What do you get once your data is fully reliable? The bottom line is that your teams build trust and function better.

Let’s break down the tangible benefits of data reliability:

  1. Improved decision-making – Decision-makers can trust the data they work with.
  2. Increased efficiency – Employees spend less time correcting errors and discrepancies, and more time actually analyzing and using their data.
  3. Better risk management – By ensuring accurate and consistent data, organizations can identify risks and take proactive steps to mitigate them.
  4. Enhanced customer satisfaction – Increased loyalty, repeat business, and referral by word-of-mouth are all positive side effects of customer trust.
  5. Improved compliance – Reliable data is crucial for compliance with regulations and laws. Organizations avoid penalties and legal issues with reliable data.

Who works on data reliability?

Data reliability touches many departments in an organization. But if you’re wondering who it’s for and who works with it, there are some generalizations we can make. Let’s explore the relationship between data reliability and some common data roles within teams.

RolesResponsibilitiesData reliability engineersData reliability engineers design, build, and maintain the systems and infrastructure that store and process data. They ensure that data is captured accurately, stored securely, and can be accessed and retrieved when needed.Data scientists/ML OpsData scientists analyze data and extract the insights that inform business decisions. They need reliable and trustworthy data to avoid feeding into errors and bad business strategies. Data analystsData analysts work to identify patterns, trends, and insights with data.QA engineersQA engineers test software applications to ensure that they function correctly and meet user requirements. They need to know data is being processed correctly and their tests produce accurate results.Database administratorsDatabase administrators manage and maintain databases to ensure data accuracy. They may be involved in defining data requirements, setting data quality standards, and monitoring data quality.Data governance professionalsGovernance professionals develop and enforce policies and procedures related to data management, including data accuracy and reliability.Other stakeholdersOther stakeholders, including customers, mostly care whether data is currently reliable. They don’t want data issues to bog them down, delay access, or cost them money.

What’s the difference between data reliability and…

What’s the difference between data reliability and data quality?

Data reliability refers to the consistency and dependability of data. Can the same results be obtained over and over again when the same data is collected and analyzed multiple times? In other words, reliable data results in consistent, reproducible results.

On the other hand, data quality refers to the accuracy, completeness, timeliness, relevance, and usefulness of data. How well does data meet the needs of its intended users?  How well does it represent the real world? High-quality data is free from errors, inconsistencies, and biases. It’s fit for its intended purpose. Context is a large part of data quality. Not only does it concern data reliability, it also concerns how well a dataset matches its intended use.

Essentially, data reliability has to meet a certain standard for data quality to be possible. A dataset can be reliable but of low quality if it consistently produces the same results but doesn’t do much to prove a point, move the bottom line in the right direction, or help a team.

What’s the difference between data reliability and pipeline reliability?

Pipeline reliability is a factor in data reliability. It’s a smaller subset of a greater overall data reliability.

A data pipeline is a series of processes that extract, transform, and load (ETL) data from various sources into a data warehouse or other data storage system. Pipeline reliability means all of those processes operate effectively, without disruption.

Issues in the pipeline can result in delayed or inaccurate data, which impacts overall data reliability. So while data reliability focuses on the accuracy of the data itself, pipeline reliability focuses more on the processes that data undergoes. Effective pipeline reliability means that all of the processes managing the data are functioning correctly. Pipeline reliability feeds into data reliability.  

What’s the difference between data reliability and data monitoring?

Data reliability and data monitoring are both important factors in effective data management, but they refer to different aspects of the process. Data monitoring is one tool in your toolkit for achieving data reliability.

Data reliability refers to the accuracy, consistency, and completeness of data. Reliable data is trustworthy and can be used with confidence to draw conclusions. Ensuring data reliability involves measures like data validation, data cleansing, and data quality checks.

Data monitoring refers to the ongoing process of tracking and analyzing data over time to detect changes or anomalies, to ensure that the pipeline is as reliable as it needs to be and data quality is as high as it needs to be. Data monitoring is often used to identify trends, patterns, or outliers in the data that may require further investigation or action. Monitoring can involve the use of automated tools or manual review of data reports and dashboards.

Data reliability ensures the quality and integrity of data, while data monitoring is about tracking and analyzing data to detect changes or patterns over time.

What’s the difference between data reliability and data testing?

Data reliability is a measure, data testing is a technique.  

With data reliability, teams are assessing the degree to which data is accurate, consistent, error-free, and bias-free. Data testing is the process of validating and verifying the data to ensure it meets certain criteria or standards.

In other words, teams reach data reliability through the process of data testing.

What’s the difference between data reliability and site reliability?

Data reliability and site reliability are two distinct concepts in computer science and IT.

Data reliability comprises the accuracy and consistency of data stored or processed by a system. It ensures that the data remains intact and consistent throughout its lifecycle, from creation to disposal. Teams aim to achieve data reliability in order to avoid data loss, corruption, or duplication.

Site reliability refers to the ability of a system or website to remain operational at all times. It involves ensuring that the system can handle user requests efficiently, scale as needed, and recover from failures or outages quickly.

Site reliability and data reliability share some of the same goals: maintaining user trust, preventing revenue loss, and minimizing disruptions to business operations. But if they were sports games, they would each take place in a different arena; data reliability takes place in the data pipeline, and site reliability takes place on a website or overall system.

A data reliability engineer serves the same role in maintaining data reliability as a site reliability engineer serves in maintaining site reliability. But, while data reliability focuses on data accuracy and consistency, site reliability focuses on a whole system or website’s performance. Data reliability can be a factor in overall site reliability.

What’s the difference between data reliability and data observability?

Data reliability is an outcome, and data observability is a framework for reaching that outcome.  

Data observability refers to the ability to monitor and understand how data is being used in real-time. It involves analyzing the behavior of data pipelines, applications, and other systems that use or generate data. Data observability can ask questions in real time, for example, “What just went haywire during this specific data test, and where along the pipeline did the problem occur?”

There are specific data observability tools that aid organizations in understanding the performance, quality, and indeed reliability of their data. Through observability, teams can make informed decisions about optimizing their data infrastructure.

So, while data reliability is the optimal outcome, data observability is the framework for achieving it. For more information, check out our data observability dictionary.

10 techniques for achieving data reliability

In general, a good data reliability program comprises many facets. In many cases, you’ll work toward delivering self-serve data and analytics dashboards at a scale that reaches your entire organization. Your general techniques for achieving data reliability are as follows:

  • Data validation – Check the accuracy and completeness of data at the time of entry, before it is saved to the database.
  • Data normalization – Organize data into a consistent format to eliminate redundancy and improve data consistency.
  • Data backup and recovery – Create regular backups of data to prevent loss in case of system failure or other disasters. Also, have actionable recovery plans in place, in the event a disaster does occur.
  • Data security – Protect data from unauthorized access, theft, and other security threats. This is done through encryption, access control, and firewalls.
  • Data quality control – Regularly monitor and improve data quality by identifying and fixing errors, as well as implementing data standards and best practices.
  • Data cleansing – Identify and correct or remove inaccurate, incomplete, or irrelevant data from databases.
  • Data audits – Review data regularly, to ensure that it meets established standards and identify areas for improvement.
  • Data culture – Demand excellence culturally, and enact standards and practices to ensure that all teams have buy-in and care about data; not just as a suggestion but as built-in accountability.
  • Data reliability engineering – From data SLAs to testing to observability to incident management, invest in a data reliability engineer is wholly responsible for maintaining data quality.
  • Data ownership – Data pipeline jobs must have owners, and upstream service owners should be identified as well. For example, SaaS app admins and database administrators are downstream from data pipelines; identify who is responsible for them and who is impacted by data.

The seven principles of data reliability engineering

Google’s Site Reliability Engineering (SRE) principles were born of a need to run reliable production systems. The Data Reliability Engineering (DRE) principles were born of the same need, applied to data pipelines. How do we solve data challenges in a scalable way? How do we keep data reliable? DRE is the set of practices that data teams use to maintain reliability of data for all stakeholders. They are:

  1. Embrace risk The only perfectly reliable data is zero data at all. Data pipelines will break - so embrace the risk and plan to manage it effectively.
  2. Set standards – You must clarify what stakeholders depend on, whether that’s through SLAs or otherwise. Use hard numbers, concise definitions, and clear cross-team consensus.
  3. Reduce toil – Do what you can to remove repetitive tasks from your data platform, so you can reduce overhead, human error, and time spent.
  4. Monitor everything –  If you want to understand data and infrastructure behavior, you must monitor data and infrastructure behavior with comprehensive, always-on monitoring.
  5. Use automation – When you automate, you reduce manual error and free up your team to tackle higher-order strategic problems.
  6. Control releases – Make your releases manageable and controlled so that you can review and release pipeline code without causing massive breakage.
  7. Maintain simplicity – Minimize the complexity of any pipeline job, and you’ll be most of the way through the battle toward keeping data reliable.

Data reliability all along the pipeline

What does data reliability look like, all throughout the data lifecycle? From initial ingestion to final storage, these are the following checkpoints for data reliability, all along the data pipeline.  

1. Pre-ingestion

In this phase, data is introduced to the pipeline from its various sources: databases, APIs, sensors, and files. The data is brought in and stored in a data lake, warehouse, or other storage system. In this phase, data reliability means that data contracts are agreed upon, and schema changes are pre-announced to prevent changes from breaking downstream ingestion or ETL/ELT jobs. Additionally, teams that implement input validations and other defensive tactics can prevent incomplete or inaccurate data from being recorded at the source.

2. Ingestion

In this phase, data is cleaned, validated, transformed, and enriched to ensure that it is ready for processing. The best way to achieve data reliability here is to start with data that’s as clean as possible. Data from all sources needs validation parameters at the point of ingestion, like rejected null data, empty fields, and invalid information. If you can efficiently gate-keep data at ingestion, as opposed to letting anything and everything in, you’re already closer to achieving data reliability through speedier and more accurate data processing.

3. Processing

In this phase, data undergoes a transformation as it travels through the data pipeline, via tasks like data aggregation and filtering. The ideal state for data reliability here means that teams are using dbt-like frameworks to implement DRY (“Don’t Repeat Yourself”) principle for ETL pipeline code. Additionally, teams should conduct code review on changes to the data model and ETL jobs. Also, data lineage can portray the journey data takes from its source to its destination, including what changes are made along the way.

4. Continuous monitoring

During Processing, your data pipelines are constantly shifting and being updated. New business logic in your transformations means that previous assumptions may now be incorrect. It’s important to continuously monitor your data to understand when changes are impacting your assumptions and adjust them accordingly. Your ideal state for data reliability in this phase will likely involve a data observability tool. Also, look to tools that help you catalog and manage business rules for data in motion.

5. Analysis/Visualization/Reporting

At the end of the day, your data needs to be fit for purpose to inform better business decisions. At this stage, data reliability looks like easily extracted insights and trends. Reliable data analysis means you understand if the data needs to be fixed immediately, or is actually never used, so a problem with it is less urgent. Data reliability is all about building trust in your data. The most important stakeholders for that trust are the end-users: the executives, PMs, and other decision-makers in your organization. If they understand your reports and dashboards, and can self-serve custom reports, you’re operating at peak data reliability.

Data reliability tools, frameworks, and best practices

Now that we’ve covered the basics about data reliability, what concrete steps can you take to put it into practice? Fortunately, there are a variety of tools, frameworks, and best practices that can help ensure data reliability. Here are some of them:


  • Bigeye: A data observability platform uses machine learning to detect and prevent data issues. It provides real-time monitoring and alerts to ensure data reliability.
  • Anomalo: Anomalo is a data quality platform that uses machine learning to detect and prevent data issues. It provides automated issue detection and resolution to ensure data reliability.
  • Cribl: A data pipeline management platform that allows data teams to route, filter, and transform data in real-time.
  • Unravel: A data operations platform that provides end-to-end visibility into data pipelines.
  • dbt: A data transformation tool that allows data teams to transform data in a structured and repeatable way. It helps ensure data reliability by providing a framework for data transformations that can be easily audited and tested.
  • Great Expectations: A data quality framework that allows data teams to define, document, and test data quality expectations. It’s a framework for data quality that can be easily audited and tested.
  • lakeFS: A data version control platform that allows data teams to track and manage changes to data pipelines. It provides a version history of data pipelines and the ability to revert to previous versions if issues arise.


  • SLAs/SLIs/SLOs: Service level agreements (SLAs), service level indicators (SLIs), and service level objectives (SLOs) are frameworks for measuring and managing the performance of data pipelines. SLAs define the level of service that is expected, SLIs define the metrics that are used to measure service, and SLOs define the target level of service that must be achieved.
  • Data contracts: Agreements between data producers and consumers that define the format, schema, and quality of data that will be exchanged. Data contracts help ensure data reliability by providing a framework for consistent and high-quality data exchange.

Best practices

  • Create a “service map” for data products: A service map is a visual representation of the data products and pipelines that are critical to business operations. Through it, data teams can identify the most important dashboards and models and prioritize efforts to ensure their reliability.
  • Keep pipeline changes small and focused: Making small, focused changes to data pipelines can help reduce the risk of introducing data quality issues. By breaking down pipeline changes into smaller, more manageable pieces, data teams can ensure that each change is thoroughly tested and audited before it is deployed.
  • Run blameless post-mortems after data outages: Blameless post-mortem focus on understanding the root cause of data outages without assigning blame. By focusing on identifying and addressing systemic issues rather than individual mistakes, data teams can improve reliability over time, without creating internal team conflict.
  • Apply data monitoring liberally: Liberally apply monitoring across your data pipelines, thereby creating proactive reliability, versus reactive issue mitigation and panicked scrambling.

Common signs that it’s the right time for data reliability

There’s no specific time on the calendar or size your organization needs to reach before you implement data reliability. The name alone, “data reliability,” should indicate that there’s never a bad time to consider it. But if your business model depends heavily on data for decision-making, data reliability is vital. That’s because inaccuracies and inconsistencies could get in the way of a streamlined customer experience or a path to revenue generation. Here are some common signs and pressure points where organizations should institute data reliability.

Your datasets are growing larger in volume

More data means more potential for errors, duplications, and glitches; in other words, more potential for unreliability. As your data grows in volume, it’s more difficult to identify and correct errors. If your dataset feels unwieldy and teams are starting to complain or notice inconsistencies, it could be time to implement more robust data reliability practices.

Nobody trusts your internal analytics and dashboards

You’ve probably invested money, time, and strategy into setting up internal analytics and dashboards. But if they’re too complex or consistently come up with data that can’t be trusted, something needs to change. All it takes is one visible embarrassment for teams to entirely forego a dashboard or analytics report. If those tools are sitting unused, it’s time to find a way to feed them with reliable data.

You had an incident impact your customer-facing ML models

When corrupt data feeds your customer-facing ML models, you risk exposing your entire business model. Think back on the most severe public data spectacles, whether they were security breaches, automation nightmares, dangerous data catastrophes.

For example, social media sites recommend things to your feed based on your interests. If that model trains on bad data, you’ll get bad predictions. So if someone is interested in dogs and the algorithm keeps feeding information about hamsters, the user will eventually churn.

How long have your ML models been working off of incorrect assumptions? How much information reached your end user before you realized it? As the expression goes, “garbage in, garbage out.” ML and automation can do wonders for your organization, but not if you’re working with unreliable data.

If you’ve had one or more incidents with your ML models that negatively impact customer trust, it’s time to institute more robust data reliability checks and balances.

Your data quality initiatives are failing

Missing values. Duplicates. Errors. Corrupt data sets. It’s easy for these problems to occur; especially when your datasets are growing in number and likely coming from multiple sources. If whatever data quality checks you have in place are failing to discover errors and mishaps, it’s time to step up your game.

Your industry is subject to regulatory compliance

If you work in a heavily regulated industry, like finance, insurance, or healthcare, it’s important to ensure that your data is reliable enough to meet the required standards. In an environment where regulations change often, your data should be easily tracked and as flexible as possible, so you can institute the changes that keep your organization in compliance.

Use cases to solve with data reliability

Data reliability is critical in various contexts and industries, essentially whenever data feeds into key business decisions. But when exactly should you implement a data reliability framework? Which industries are more likely to benefit? What teams should prioritize data reliability immediately? Let’s walk through some common use cases where data reliability makes a positive difference.

Financial reporting

If you work in finance or financial reporting is a key component of your business, data reliability is critical. Without it, your reports don’t accurately reflect your financial performance for the quarter. Data reliability ensures that you don’t have missing information or misleading analysis when you present to your board and stakeholders. It can also help you generate better spending strategies as you evaluate your profit and loss.

Risk management

All teams build risk into their day-to-day. But to manage risk effectively, most teams rely on accurate data. When does a risk become too dangerous to take, and how do you identify potential risks in advance? To evaluate their likelihood and impact, you need data reliability. Data reliability then feeds into the best strategies for risk mitigation and management.


The healthcare industry is super fragmented. Data comes from many sources, which often aren’t even located in the same physical environment. Data reliability is essential for several functions in healthcare, from diagnosis to treatment to patient outcomes and communications. Electronic health records and patient data need to be secure and up-to-date to ensure that every patient is safe and being effectively managed.


Quality control and process improvement are two key areas in manufacturing that need data reliability. If production data is inaccurate, teams can’t identify areas for improvement and optimization. All along the supply chain, accurate data needs to be fed into the systems that rely on it to produce the right number of products, the right way, in a timely manner.


Marketers want to target the right audience and measure the effectiveness of campaigns. They’re  awash in data, from Google Analytics to email open-rates to traffic sources and audience growth data. But how do marketers ensure that analytics accurately reflect customer behavior and campaign performance? How do they ensure that they’re allocating budget toward the most impactful actions along the marketing funnel? They need reliable data.


Scientists and research firms need reliable data to ensure that their studies are valid and replicable. A controlled environment with accurately-recorded data leads to accurate conclusions. Inaccurate data will produce misleading results and faulty conclusions. To make sure that the money and time spent on conducting studies and experiments is worth it, data reliability is of the utmost importance.

Getting started with data reliability

If you’re rolling out data reliability, what’s the first step? It can be helpful to pose the following questions on your team:

  • Where does data come from?
  • Where does it go?
  • Which teams work directly with data?  
  • Where can problems occur?
  • Where have we had problems in the past?
  • Who and what is impacted when problems occur?  
  • What business outcomes are impacted by data?

A successful data reliability practice is not a one-and-done situation. It works in a continuous loop to serve as a check on your datasets, no matter how large or complex they become. If you’re stuck, use this data quality survey template to take the pulse on your team.

Successful data reliability in practice looks like:

  • Your dashboards are up-to-date and provide useful analysis
  • Your data is consistent and available at all times
  • Your teams work with data, trust its integrity, and use it to make better decisions
  • Your data is secure; it’s protected from unauthorized access, modification, or destruction
  • Your datasets remain in compliance, even when regulations change
  • Your data infrastructure is scalable, adaptable, and can handle increasing amounts of data and traffic as your business grows

A data observability tool goes a long way toward keeping your data reliable, plus quickly  understanding when it’s in jeopardy of growing unreliable. Find out more today.

If you’re ready to implement data reliability across your whole organization, turn to Bigeye. Or, for more information, check out the following resources:

The Bigeye blog

Bigeye case studies

Request a Bigeye demo

share this episode
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
Data analyst
Business analyst
Data/product manager
Total cost
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights

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