Why does data observability matter?

If you're reading these docs, you and your team have probably put in massive amounts of effort to get data into one place and help your organization 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, not the last, to know about data issues. You can learn more about data observability in our blog:

• What is data observability?
  • Signs you need data observability
  • When to prioritize data observability
• How to evaluate data observability tools
• Business benefits of data observability

What does Metaplane do?

Metaplane continuously monitors the data flowing through your data stack then alerts you when something may be going wrong. We do this by collecting metrics, metadata, lineage, and logs, training anomaly detection models on historical values, then sending you alerts for outliers with options to provide model feedback.

How do I get started?

You've come to the right place, fellow traveler! If you like to learning through practice, you can create an account and get started now. If you like acquiring knowledge first, here are three places to start:

1. See how exactly you can add observability to your data stack in <10 minutes: Getting started.
2. Integrate data warehouses like Snowflake, transformation tools like dbt Cloud, and business intelligence tools like Looker. You'll only need to select data warehouse to get started!
3. Read the latest from our blog: Blog

Read more about where we recommend placing monitors here.

Additional resources

To learn more at a higher-level, check out these resources:

metaplane.dev

The Four Pillars of Data Observability

How much data do we need to reconstruct a useful picture of our data? That’s the question we attempt to answer in this article. The groupings of questions we need to answer in order to describe our data are the “pillars” that underlie data observability.

metaplane.dev

Data Observability vs. Software Observability

Overview of the similarities and differences between the established field of Software Observability and the emerging category of Data Observability.

metaplane.dev

Prioritizing Data Observability: Why Now?

Five important reasons for why you should consider making data quality a priority now, instead of waiting until it becomes an issue.

metaplane.dev

How To Justify Investing in Data Observability

The four most common reasons for how data leaders justified an investment into data observability, spanning concrete to intangible (but still critical) reasons.