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:
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.
Measuring data reliability requires looking at three core factors:
To take it one step further, some teams also consider factors like:
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, 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.
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:
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.
Do you know the current status — quality, reliability, and uptime — of your data and data systems? Not last month or last week, but where they stand at this moment. As businesses grow, being able to confidently answer this question becomes more important. That’s because data needs to be clean, accurate, and up-to-date to be considered reliable for analysis and decision-making. This confidence comes through what’s known as data observability.
In the past years, organizations have been investing heavily to convert themselves into data-driven organizations with the objective to personalize customer experiences, optimize business processes, drive strategic business decisions, etc. As a result, modern data environments are constantly evolving and becoming more and more complex. In general, more data means more business insights that can lead to better decision-making. However, more data also means more complex data infrastructure, which can cause decreased data quality, a higher chance of data breaking, and consequently erosion of data trust within organizations and risk of not being compliant with regulations. The data observability category — which has quickly been developing during the past couple of years — aims to solve these challenges by enabling organizations to trust their data at all times. Although the category is relatively young, there are already a wide variety of players with different offerings and applying various technologies to solve data quality problems.
The term “data lineage” has been thrown around a lot over the last few years. What started as an idea of connecting between datasets quickly became a very confusing term that now gets misused often. It’s time to put order to the chaos and dig deep into what it really is. Because the answer matters quite a lot. And getting it right matters even more to data organizations.
I started my career as a first-generation analyst focusing on writing SQL scripts, learning R, and publishing dashboards. As things progressed, I graduated into Data Science and Data Engineering where my focus shifted to managing the life-cycle of ML models and data pipelines. 2022 is my 16th year in the data industry and I am still learning new ways to be productive and impactful. Today, I am now the head of a data science & data engineering function in one of the unicorns and I would like to share my findings and where I am heading next.