Getting Started with Data Observability
What is data observability?
❝Data observability is the degree of visibility you have into your data at any point in time.
Dedicated data observability tools like Metaplane collect metadata about the properties of and relationships between your data, then monitor everything for changes and present actionable insights. You could call data observability analytics for your analytics.
You and your team have probably put in massive amounts of effort to get data into one place and empower your organization to use it.
Unfortunately, as you store and model increasing amounts of data for more and more use cases, your stakeholders can become the first people to learn about data issues:
- "Why does this number look off?"
- "Why is this table not updating?"
- "Why did this column disappear?"
Each time this happens, it's more than just a headache — people start losing trust in data.
The goal of a data observability tool is to help you be the first to know about data issues, ensure trust in data, and empower your organization to use data to make more informed decisions.
Why do you need data observability?
Common data issues, such as data inconsistency, outdated or incorrect data, and data silos, can be addressed with data observability. Additionally, data observability can help businesses make more informed decisions by providing a clear picture of the context and quality of their data.
For example, your organization might rely on an activated_users table to send targeted marketing e-mails. When the data in this table becomes inconsistent or outdated, your marketing campaigns could suffer. With observability into your data, you can monitor the freshness and accuracy of this data, ensuring that your campaigns are always on track.
What are the components of Data Observability?
Data teams with data observability tools leverage their historical metadata to complete the following mission-critical jobs:
- Continuous data monitoring: Is the state of our data sufficient to meet the needs of external use cases and internal standards?
- Data incident management: when a data issue occurs, how do we keep track of the state of this issue, assign owners, and measure the quality and accuracy of our data over time?
- Root cause analysis: When a data quality issue occurs, what upstream dependencies caused it to happen (and how fast can the problem be resolved)?
- Impact analysis: What are the downstream consequences of a data quality issue, when one does occur? Which downstream teams, like a data analytics team or data scientists, should be notified?
- Spend monitoring: How much money are we using for compute resources, and how is it allocated across our data stack?
- Usage analytics: By whom, when, how much, and in what manner are our data assets being used by stakeholders?
- Query profiling: How can I optimize both my data assets and stakeholder queries to minimize time and cost?
Data observability thus helps data teams deliver on their mandate to provide the high-quality data businesses need to grow.