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 this guide, we’ll review what data observability is, why it’s a key driver of business growth, and what tools are needed to support the process.
Advanced data teams automate the movement of raw data from data sources (like a CRM, advertising platform, and marketing automation tool) to a modern data platform, where data sets are cleaned, organized, and centralized for storage in a data warehouse. It’s from this point that transformed data can be synchronized with business intelligence tools for reporting, creating visualizations, conducting analyses, and generating insights. The flow of data from disparate origin sources to the destination, whether it’s a data warehouse or business intelligence tool, is known as the data pipeline.
Data observability is the ability to see what’s happening in your entire data pipeline, optimize performance, and monitor the health of your data. Without data observability, businesses are only viewing data at one point in time and won’t have a real-time, transparent view of the workflow, nor insight into previous versions of saved data sets. Data observability, therefore, is critical for rapidly detecting, troubleshooting, and resolving problems, as well as ensuring that the data flowing into your business intelligence tool is reliable for analysis, detecting patterns, and developing insights.
Data observability is a key driver of business growth, as it ensures data quality and enables you to be agile and make sound, data-driven decisions in real time.
Imagine, for example, a business called Intrepidly runs a B2B subscription-based productivity tool that’s accessible to users on its desktop website, Android app, and iOS app. Intrepidly has already launched a new feature on the web, and next it’ll be released in the iOS app. The updated version has been deployed and appears to be running fine. New subscriber profiles are being created in the CRM via the iOS point of sale. But Intrepidly’s revenue operations team notices that key profile fields, such as subscription type and discount level, are being populated with NULL values.
Where along the data pipeline is the issue occurring? Is it before or after the transform? Are these key pieces of missing information retrievable? Depending on what went wrong and where, Intrepidly could experience significant effects on revenue and the customer experience.
Errors and omissions in the data pipeline can lead to downtime, which is when data is inaccurate or unavailable. In businesses that lack data observability, it could take multiple developers hours to answer these questions and rectify the issue. Troubleshooting data issues is not where you want your high-value employees spending their time.
Data observability could also mean the difference between catching an error right after the version release versus moments before the start of an all-hands presentation when the data in the deck looks “off." In other scenarios, data issues can delay product launches, internal technology implementation, reporting obligations, and decision-making.
Maintaining data observability helps prevent “drop everything to find the cause” situations because it takes a proactive approach to monitoring the health of a business’s data pipelines.
There are specific tools, including data lineage and run history, that provide businesses with a transparent view of their data pipeline. Although these capabilities may not prevent human errors that result in situations like that mentioned above, businesses that have complete visibility into their data pipelines will be able to detect issues early and significantly accelerate the resolution process.
Data lineage is one tool that’s used to monitor the pipeline so businesses can see what information their transforms and reports are relying on and quickly trace errors back to the source. Mozart Data’s modern data platform infers lineage so businesses don’t have to set it up themselves.
Run history makes troubleshooting failed transforms easier by showing whether they were run manually or automatically. To get to the root of broken transforms, you can use version history to see when changes were made, what those changes were, and who made them.
Snapshots record historical information by capturing transform or source table information at a specific point in time and at a set frequency — typically daily. These are helpful for performing automated checks. For instance, when you need to track down the date that a particular field goes missing or automatically compare relative changes (e.g., is the amount we're billing this month close to what we billed last month).
Mozart Data’s modern data platform provides data lineage, run history, version history, snapshots, and other data observability tools to give you visibility into your data systems and processes. This enables teams to streamline workflows, optimize their pipelines, and realize operational efficiency gains. It’s also key to maintaining trustworthy data that org leaders feel confident using for analyses, insights, and business decisions.
You’ve likely heard about ELT — Extract Load and Transform… the Modern Data Stack’s evolution on ETL. This is a game changer by nature in that it enables organizations to ingest raw data into the data warehouse and transform it later. ELT gives end-users access to the entirety of the datasets they need by circumventing downstream issues of missing data that could prevent a specific business question from being answered.
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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.
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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:
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.