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.
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.