Inside Bigeye: How We Use AI to Solve Enterprise Data’s Biggest Problems

Back in 2023, our State of Data Quality research found something stark: 70% of business leaders didn’t trust analytics dashboards to make critical decisions because data quality incidents happened so regularly. As we talked to the teams behind those dashboards, the picture got worse. The tools that were supposed to reduce risk were often creating new problems.

Data engineers were drowning in alerts that said “something’s wrong” but gave no usable next step. Incident response turned into an hours-long scavenger hunt across logs, orchestration tools, and Excel sheets. And as organizations began to use more AI, a fresh problem appeared: AI agents and LLMs were starting to touch — and potentially expose — sensitive data with no clear way for data teams to govern that access.

At Bigeye we set out to address those failures by rethinking what a modern data observability platform should do for enterprise teams. We’ve deliberately integrated AI across the product to solve four recurring, practical problems: noisy monitoring; slow incident response; manual setup and maintenance; and the governance gap around AI itself. Below is how we approached each problem, the specific AI-powered responses we built, and why they matter so much to our enterprise customers.

Problem: Monitoring created more problems than it solved

Imagine the on-call engineer at 3 a.m.: their phone buzzes with “Data anomaly detected in user_transactions.” They investigate, only to learn the signal is a fairly predictable weekend dip that happens every few weeks. The alert was technically correct, but useless. Multiply that by thousands of tables and dozens of teams, and you have an exhausted organization that either buries itself in alerts or starts ignoring them and misses the ones that matter.

Why rule-based monitoring breaks at scale

Rule-based thresholds are blunt instruments. They can’t account for seasonal patterns, upstream job schedules, or business events (promotions, holidays) that change normal behavior. In a large enterprise the combinatorics of tables × columns × schedules means static rules generate enormous false-positive volume and cost people time and money.

Our answer: ML-driven anomaly detection

We built a core monitoring engine that doesn’t start operating of rules but from learning what’s normal. The engine uses probabilistic and ML models that:

• learn historical baselines and trends (seasonality, growth, weekday/weekend behavior),
• detect real outliers in metrics such as row counts, null rates, distributions and schema drift,
• and surface only those deviations that the model estimates are unlikely under learned normal behavior.

That combination of statistical learning and domain awareness gives you far fewer false positives, and the alerts that arrive are more likely to be actionable.

But good anomaly detection alone isn’t enough. If your system can’t tell which incidents actually hurt the business, it still creates noise.

Smart alert prioritization 

To get from “alert” to “do this first,” we paired anomaly detection with prioritization: Bigeye understands which datasets feed critical dashboards and reports, and applies weighting so teams see the most important alerts first. We also surface the dependencies so engineers can see the chain of impact, not just the symptom.

What customers love: teams spend less time chasing irrelevant alerts and more time fixing incidents that affect customers and revenue. In practice this reduces MTTR because the system routes attention where it belongs.

Problem: Incident response is costing millions

When an incident occurs in a complex, hybrid data stack, context disappears fast. Engineers must stitch together metadata, pipeline logs, job histories, and change events, a manual process that can take hours and costs the business in delayed decisions and lost confidence.

Our answer: bigAI, an incident co-pilot 

bigAI is built to convert incident alerts into clear, human-readable narratives with practical next steps. We filed a provisional patent for the core approach in April 2025. At a high level, bigAI:

• Summarizes the incident in plain language, saying what happened, who/what’s affected, and why the system thinks it happened;
  
  
• Suggests targeted remediation steps, e.g., “Check Airflow job etl_user_sync failed 2 hours ago” or “A recent dbt change removed null handling on email column”;
  
  
• Produces stakeholder-friendly impact statements so managers and executives get one-paragraph summaries, while engineers still get the deep context they need.

How it actually finds the root cause

bigAI isn’t pulling guesses out of the air. It combines:

• anomaly context (what metric deviated and how),
• lineage (which upstream jobs and datasets feed the affected table),
• orchestration signals (job failures, retries),
• and (when customers permit it) row-level patterns that point to the subset of data responsible for the change.

The result is a prioritized, plausible hypothesis: a starting point for remediation that often eliminates the early hours of manual triage.

Built for enterprise security and practical use

We designed bigAI to be enterprise friendly: models can run inside secure environments (we run on Amazon Bedrock in secure configurations) and customers can choose self-hosted deployment. There are explicit modes to restrict bigAI’s access to row-level data; when row-level analysis is disabled, bigAI produces reduced summaries that still provide value without exposing raw values.

Prevention over reaction

bigAI also looks upstream for patterns in ETL and dbt code that commonly cause incidents and suggests defensive fixes, better null handling, safer joins, or simple code edits that prevent a class of future problems. The aim is less firefighting and more durable pipeline hygiene.

What customers love: Instead of spending 2-3 hours piecing together what went wrong, teams get actionable root cause analysis in minutes. Early beta customers report cutting their mean time to resolution in half.

Problem: Manual setup and maintenance doesn’t scale

Enterprise data estates are huge. When you're working with thousands of tables, dozens of sources and hundreds of owners, relying on a manual setup process is a recipe for bottlenecks.

Our answer: automation that understands your data

We're building automation capabilities that tackle this challenge head-on. Our upcoming data profiling feature exemplifies this approach: it automatically profiles tables to reveal schema characteristics, quality patterns, and monitoring opportunities. The AI analyzes those profiles and recommends a targeted set of monitors: completeness checks, uniqueness constraints, or pattern validations based on what it discovers in your specific dataset.

The profiling runs on-demand with smart sampling that's both fast and secure. No raw data values are ever exposed, only safe aggregates that give you the insights you need. Teams are able to deploy suggested monitors with a click, transforming setup time from hours per table to minutes across hundreds of tables.

Data engineers and data stewards get the context they need before applying data quality rules. Instead of guessing where to start monitoring, they'll see a sortable, column-by-column breakdown that shows exactly where attention is needed first.

What customers love: Data engineers can set up comprehensive monitoring for dozens of tables in the time it used to take for one.

Small automations with big leverage

We also ship lots of small wins that greatly reduce friction, for example, generating cron schedules from a natural language description, or producing an initial set of SQL checks from table profiles. Individually these save minutes; at enterprise scale they save weeks of human work each year.

Problem: AI governance is the new wild west

As enterprises adopt more agents, LLMs and AI tools, a new risk emerges: AI systems that access enterprise data without a clear way to govern or audit that access. Regulations like the EU AI Act raise the bar for enterprise controls, and data teams need runtime enforcement, not just policies buried in text files.

Our answer: The AI Trust Platform 

We built the AI Trust Platform to solve a practical enterprise problem: you need to know what AI agents are doing with your data, you need to control those interactions in real time, and you need to prove it. The platform (which we plan to launch later in 2025) combines observability, governance, and enforcement so teams can run agentic AI with confidence.

When we look at where AI projects fail or become risky, three operational questions keep coming up:

• Quality: Is the data the agent is using accurate and up to date?
  
  
• Sensitivity: Is the agent touching data it shouldn’t?
  
  
• Certification: Is the agent only using datasets that are approved for its purpose?
  

Our platform is designed to give teams practical answers to those questions.

Built for enterprise realities

We designed the platform with real regulatory and deployment constraints in mind:

• Hybrid & self-hosted deployments. For customers that need it, the platform supports private model instances and self-hosting so inference and processing can run inside a customer’s cloud or region, avoiding external API exposure.
  
  
• Compliance readiness. Our goal is to produce certification evidence with auditable logs that compliance teams can use.
  
  
• Lineage-aware decisions. Every allow/warn/block decision is intended to take lineage into account, so policy outcomes are explainable during audits and incident reviews.

What customers will love: Finally having visibility into which AI systems are accessing what data, with the ability to control it in real-time. No more hoping your AI data access policies are being followed, you can actually enforce them.

Enterprise problems require enterprise-grade solutions

These targeted solutions to the problems that matter most when you're operating data at enterprise scale. Alert fatigue, slow incident response, manual overhead, and AI governance challenges all share one trait: they get exponentially worse as you grow. That's exactly what our AI is designed to solve.

Industry analysts recognize this shift. Gartner notes that "only vendors with advanced technologies offer this feature in their data observability tools, and recommendations aren't always available for all types of issues. This is a differentiating factor among vendors."

The patent-pending technology behind bigAI, our ML-driven detection capabilities, and our AI Trust Platform represent our commitment to solving the problems that enterprise data teams face every day. As organizations continue to scale their data and AI initiatives, they need observability that's as intelligent as the systems it monitors.

Ready to see how AI can solve your biggest data challenges? Book a demo to experience intelligent, problem-solving data observability in action.