Data matters more than ever – we all know that. But at a time when being a data-driven business is so critical, how much can we trust data and what it tells us? That’s the question behind data reliability, which focuses on having complete and accurate data that people can trust. This article will explore everything you know about data reliability and the important role of data observability along the way, including:
- What is data reliability?
- Why is it important?
- How do you measure it?
- Data quality vs. data reliability: What’s the difference?
- What is a data quality framework?
- How can observability help improve data reliability?
- Top data reliability testing tools
What is data reliability?
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.
Why is data reliability important?
Data reliability is so important because, without it, businesses either wouldn’t be able to trust their data to power any kind of decision or action. Even worse, businesses could trust inaccurate data and make ill-informed decisions that could have a negative impact.
Depending on the business and the decision, this negative impact could mean millions of wasted dollars or even risk to customers. In fact, Gartner estimates bad data costs businesses nearly $13 million annually.
As HBR puts it: “An incorrect laboratory measurement in a hospital can kill a patient. An unclear product spec can add millions of dollars in manufacturing costs. An inaccurate financial report can turn even the best investment sour. The reputational consequences of such errors can be severe.”
Additionally, once unreliable data does surface, the longer it goes unnoticed, the more expensive and time-consuming it can be to solve the issue. Issue resolution also diverts valuable resources away from other projects.
On the flip side, prioritizing data reliability can result in a competitive advantage by allowing teams to make more insightful decisions. In fact, research reveals that data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain customers, and 19 times as likely to be profitable.
How do you measure data reliability?
Measuring data reliability requires looking at three core factors:
- 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.
- 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.
- 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.
Data quality vs. data reliability: What’s the difference?
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.
What is a data quality framework?
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:
- Qualify: Understand a list of requirements based on what the end consumers of the data need.
- Quantify: Establish quantifiable measures of data quality based on the list of requirements.
- Plan: Build checks on those data quality measures that can run through a data observability platform.
- Implement: Put the checks into practice and test that they work as expected.
- Manage: Confirm the checks also work against historical pipeline data and, if so, put them into production.
- 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.
How can observability help improve data reliability?
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.
Top data reliability testing tools
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. Their solution for
3) Great Expectations
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.
4) New Relic
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.
Make Data Reliability a Priority
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.