In 2022, data observability will be a must-have for every data team. But what is it and what does a good approach look like?
Across industries, companies in today’s rapidly changing workforce are relying on data more than ever. But while our ability to collect, store, and visualize data has kept up with the needs of modern teams, we still face a complex challenge: assessing the quality and integrity of the data itself. Companies need data that’s recent, complete, and within accepted ranges in order for it to be useful to the broader business, from informing sales forecasts to powering marketing campaigns.
When it comes to using data to drive organizational outcomes, companies might face a plethora of obstacles. Broken dashboards and reports resulting in inaccurate data-powering digital services are all too common obstacles for data engineers. Even the best-thought-out strategic plans can fail if the data your cloud pushes downstream isn’t accurate.
So, what turns good data bad? In our opinion, this boils down to three key reasons.
First, the rapid growth of data teams within organizations definitely plays a role as more leaders recognize the importance of data in decision making. As businesses hire more data analysts, data scientists, and data engineers, there are bound to be internal growing pains and coordination issues—and if you’re not proactive, you could compromise the quality of your data.
Second, data comes from so many different internal and external sources that organizations are bound to face challenges in upholding the data’s integrity, especially as data sources can change unexpectedly without any notice.
And finally, data pipelines are becoming increasingly complex, with multiple stages of processing and non-trivial dependencies between various data assets. Even a small change made to a single data set could have far-reaching consequences.
When it comes to catching and even preventing bad data from corrupting your perfecting good pipelines, you need to understand what broke and who was impacted. That’s where data observability comes in.
The inspiration for data observability stemmed in part from application performance monitoring in the field of software engineering. Tools like New Relic and Datadog have made it easier for organizations to assess the health and user experience of software applications over the last decade.
We should apply the concept of observability and reliability to our data systems in order to prevent or fix what we refer to as data downtime, time periods when data is missing, inaccurate, or partial. The effects of data downtime compound rapidly in complex ecosystems.
Data observability differs from existing solutions as it extends beyond traditional data quality monitoring and anomaly detection. It eliminates data downtime by leveraging automated monitoring, alerting, and triaging to assess data quality and identify discoverability issues. This benefits customers and data teams alike as you get healthier pipelines and greater data team productivity. By understanding the root cause of your data downtime, you can fix data issues before they surface downstream.
