Introducing Scout: Your always-on data SRE

We’ve been building what we believe is our most exciting product to date: an autonomous data SRE. We call it Scout. 

You can think of Scout as a seasoned data engineer, with all the context of historical issues, a photographic memory of your 2,000-model lineage, and the ability to traverse code, business logic, and git commits in seconds.

Watch our CEO give a 3-minute introduction to Scout‍

Data quality is still a business-critical, unsolved problem

A lot has changed since we started SYNQ in 2022. The ‘modern’ data stack is now just the data stack. Enterprises are migrating to Snowflake and Databricks by the thousands, and dbt adoption is growing 85% year-over-year in the Fortune 500.

But a few things haven’t changed. dbt just released their ‘2025 State of Analytics Engineering Report’. When asked about their biggest problems, two came out on top: 56% of data practitioners cite poor data quality, and 40% say ambiguous data ownership. 

In other words, data quality is still a largely unsolved problem.

At the same time, data’s gone from important to business-critical. It’s now directly used in business operations, from deciding what to reimburse customers at a delivery company to powering automated trading at quant hedge funds.

When something breaks, it’s not just a failed dashboard. It’s millions of dollars on the line. Data observability tools help teams detect issues, but most data teams weren’t trained for these workflows. Instead, they’re overwhelmed by alerts, stuck in reactive debugging.

That’s why we developed the Data Product Observability Workflow in 2024: a clear approach to building reliable data with five principles from defining data products, ownership, severity, strategic testing, and quality metrics.

Scout: Using AI to supercharge data quality workflows

Scout is the missing piece of the puzzle for empowering more data teams to adopt the Data Product Observability workflow. 

By the end of the year, we believe that Scout will become your most active contributor across the entire observability workflow. The majority of root cause analysis, new monitor and test deployments, and continuous optimization will be done by the AI agent. And the data team is freed from tedious work and focused on key decisions, like identifying the most important data assets.

Scout has already debugged hundreds of gnarly issues for our customers, and the results have been remarkable. In one example, an anomaly monitor triggered, showing a significant drop in row count. Within seconds, Scout had correctly concluded it was due to an intentional code change, linked the relevant git commit, and suggested marking the issue as “expected.” It got it all so right that we had to go back and check that nobody had fiddled with it.

Scout is only possible because SYNQ captures everything: lineage, logs, usage, issues, incidents, and their outcomes. That history gives Scout a foundation that most humans or tools don’t have. So far, SYNQ has monitored millions of tests and monitor runs, tracked and understood hundreds of thousands of assets, and managed thousands of data issues and incidents, making it possible to kickstart the agent from day one.

Whether you're a 5-person team or a 50-person data org, Scout meets you where you are. For small teams, it nails solo debugging and makes alert triage fast and accurate. For larger orgs, it distributes ownership, removing bottlenecks or “data heroes” bearing all the weight.

We believe that AI, paired with data quality workflows, will create a cultural transformation, not just a technical benefit.

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Here are some of the jobs Scout will be able to take off your plate.

Deploy smarter tests. Scout analyses lineage, usage patterns, issue history, and contextual data to intelligently recommend what and where to test.

‍Triage issues 24/7. Scout automatically goes through all your data issues, triages alerts based on importance, and makes recommendations about what action you should take.

‍Autoresolve issues directly in code. Once an issue is identified, Scout generates ready-to-ship code suggestions so you can implement the fix instantly. Scout also gives you all the evidence so you can understand how each conclusion was made.

Scout should be judged by what it delivers. In the first few weeks, you should see most issues automatically root cause–analyzed by Scout, with the majority of new tests suggested and implemented by the AI. Over time, the results should become tangible to the rest of the business, too. Data incidents go down. Time spent firefighting drops. And you start to see a visible shift in how much the business trusts your data.

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We have ambitious plans for the future. Scout is just the first application for AI in data observability. In collaboration with our customers who like to be on the leading edge, we are expanding the context even more. We use Slack conversations to understand what is happening with data, and integrate with metric systems such as Cube or Steep to track the evolution of business metric definitions. We utilize all these signals to enhance the relevance of Scout actions, making them more approachable to a less technical audience.

We are building for a future where the data observability platform feels like you've hired a team of experts, or expert agents, that work 24/7 to strengthen the reliability of your data and data products. Whether it's auto-adjusting deployment of SYNQ, its monitors, workflows, or alerts, or helping teams manage and resolve incidents. With new AI technology, we can drive our mission forward, supporting data teams to spend a lot more time building data products for business-critical systems, less on the tedious work and workflows that no longer have to be done by humans.

Learn more

Here are a few ways to learn more about Scout and SYNQ

• Book a demo
• Try SYNQ Free
• Learn more on synq.io/scout

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