The current enterprise landscape for Artificial Intelligence reveals a stark contrast between visionary intent and operational reality. According to the IBM Institute for Business Value’s 2025 CEO Study, a mere 16%of AI initiatives have successfully achieved enterprise-wide scaling. This struggle to move beyond localized proof-of-concepts is further highlighted by MIT’s NANDA study, which indicates that up to 95% of generative AI pilots fail to transition out of the experimentation phase.
You can have the most sophisticated machine learning models in the world. But if your data is incomplete, biased, or outdated, your AI system will produce unreliable results. The saying holds true: garbage in, garbage out.
That's where data quality and observability come in. These two practices work together to catch problems before they cost millions, keeping your AI systems running smoothly and your data trustworthy.
Data observability is your ability to understand what's happening with your data across the entire ecosystem. Think of it as a health monitoring system for your data pipelines
Instead of waiting for something to break, observability gives you real-time visibility. You can detect issues, diagnose root causes, and fix problems before they affect business operations.
This proactive approach has become necessary as companies rely more heavily on data for analytics, decision making, and AI-powered tools.
Most organizations start with basic, rule-based checks. These include:
These work fine when errors are obvious. A missing data point where one should exist? Your system catches it.
But here's the problem. Data behavior changes over time. Customer patterns shift. Seasonal trends emerge. Business conditions evolve.
A pipeline might pass all your checks but still produce bad values that harm decision making. Your alerts either miss subtle problems or flood your team with false positives.
That's why many companies are moving beyond rules and adding statistical learning, anomaly detection, and trend analysis to their observability stack
AI-powered observability doesn't just ask, "Is this value in range?" It asks, "Does this behavior match what we've seen before?"
That shift makes all the difference. The system learns what "normal" looks like for your specific data patterns. Then it spots deviations that matter while filtering out noise.
Here's an example. Your revenue might naturally fluctuate on weekends. An AI observability engine knows that. So if weekend numbers drop unexpectedly, it alerts your team. But it won't flag normal weekend variability as a problem.
AI observability platforms excel at catching:
Platforms like Sifflet use machine learning to analyze metadata from various source systems. When inconsistencies appear, the platform pinpoints where the problem started. That cuts troubleshooting time dramatically.
For large language models, tokens are units of language the model processes. More tokens mean higher costs and slower response times.
AI observability tracks:
This helps you find ways to reduce consumption without sacrificing output quality.
Unlike traditional software, AI models can gradually change behavior as real-world data evolves. This is called model drift.
Key metrics include:
Catching drift early prevents models from disrupting business operations.
You need to know if your AI is producing accurate, relevant outputs. Track:
These metrics help you maintain trust in your AI systems.
AI is usually layered on top of an existing data infrastructure in a production landscape, and observability is enhanced with the use of metadata, which consists of historical data, schema changes, execution logs, freshness metrics, etc. This metadata is captured and ingested into an observability engine, where machine learning models are built to analyze historical metadata to learn the patterns of "normal" behavior.
When new data comes in, or when a pipeline runs, the observability platform evaluates how the observed behavior deviates from previous "normal" behavior. If there are any significant differences or slight movement from the previous status quo of the data stream, the system raises an alert with information as to why the alert is relevant to maintenance teams. This alert provides context to the maintenance team in that it provides insight into the where and why of any potential problems.
Some observability platforms have incorporated predictive observability. These predictive observability systems predict potential slowdowns or failure modes before they happen using trend analysis and forecasting. Predictive observability helps teams respond sooner. The automation of mundane analysis will enable engineers to spend their time investigating higher-level architectural issues.
An industrial MNC faced an 80% failure rate on AI projects. Nothing reached production-level performance.
They spent 18 months implementing data quality transformation, including:
The results? A 95% reduction in failure rate, faster insights, and major cost savings.
The lesson is clear. No matter how sophisticated your AI models are, data quality determines success.
According to Wavestone’s 2024 Data and AI Leadership Executive Survey, Only 37% of data and AI executives report improving data quality. Part of the problem? Less than a third use a unified platform for governance, quality, and observability.
Fragmented tools create:
A unified approach lets you cross-reference policy violations at every stage and prioritize responses based on business severity.
AI doesn't replace your data engineers. It gives them better tools.
With AI-driven observability, your team can:
This shifts observability from reactive firefighting to proactive management.
Data quality isn't just about validation anymore. It's about understanding your data deeply.
Companies like Uber and PayPal prove that investing in observability delivers:
The bottom line? High-quality data forms the foundation of trusted, effective AI. As AI systems grow more complex, continuous data quality management will determine whether they perform reliably and enable informed decisions.
If you're serious about AI success, start with your data. Build observability into your pipelines. Use AI to catch what rules can't. And make data quality a strategic capability, not an afterthought.