Data observability is the practice of monitoring and analyzing the health of an organization’s data and data systems. Essentially, it gives you a 360o overview of what’s happening with your data at any given point in time. This practice is beneficial, as it provides all stakeholders with an in-depth insight into how their data is collected, stored, used, and processed. Moreover, it enables them to understand whether their data is reliable and can be used to fulfill overarching business objectives.
While the terms “data observability” and “data monitoring” are often used synonymously, there’s a key distinction between them. The former goes beyond simply monitoring the data, as it also regularly evaluates the applications, tools, and servers it’s being processed through – and assesses the health of those systems too. That means that you keep track of its metrics (data measured over time), analyze its logs (timestamps of events), and determine its traces (causally related events). Ultimately, your data is only as good as the system processing and analyzing it, so evaluating the backend is crucial.
Other Definitions of Data Observability:
- “A set of tools to track the health of enterprise data systems and identify and troubleshoot problems when things go wrong. Data observability provides continuous, holistic, and cross-sectional visibility into complex data systems, such as the analytics and AI applications that companies would like to use to guide their businesses and personalize customer experiences.” (Forbes)
- “Eliminates data downtime by applying best practices of DevOps Observability to data pipelines. Like its DevOps counterpart, data observability uses automated monitoring, alerting, and triaging to identify and evaluate data quality and discoverability issues, leading to healthier pipelines, more productive teams, and happier customers.” (Towards Data Science)
- “Your ability to understand the health of your data and data systems by collecting and correlating events across areas like data, storage, compute and processing pipelines.” (Microsoft)
Use Cases Include:
- Data monitoring: The biggest issue that data teams face with their internal data pools is the lack of data reliability. They usually find out only after it has been processed, leading to wasted time and effort. Using data observability tools, you can combine log data with data streams to flag issues as soon as possible and get ahead of them.
- Cost optimization: For FinOps (financial optimization) teams, data observability allows businesses to map their tech stack and understand its utilization. They can pinpoint areas where the resources are not maximized to their fullest potential. It can also help them flag errors in the pipeline and prevent over-provisioning of resources – in turn, saving costs.
- Automation and efficiency: A good data observation workflow would include using automation tools that help DataOps (data operations) teams monitor and alter their data streams when needed. It detects anomalies beforehand and reduces the mean time to detection (MTTD) and mean time to resolution (MTTR) over time.
Benefits of Data Observability:
- Increases the trust in your data as it’s continuously monitored, leading to higher data quality and reliability
- Ensures that the quality of your data is in line with stakeholders’ expectations and readily available at any given time
- Due to end-to-end data visibility, you can conduct root cause analysis in a shorter period, identifying and resolving bottlenecks quickly
- Helps reduce the MTTD and MTTR, as you can analyze data movement across the entire infrastructure faster
- Helps you catch issues with your data and troubleshoot them before it gets processed, saving time, effort, and loss of investment
- It ensures that data teams adhere to Service Level Agreements (SLAs) to meet business objectives and operationalize their data when needed.
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