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AI for Data Profiling and Anomaly Detection

5 min read
Data ArchData Eng

Data Arch

AI surfaces anomalies. You define quality rules and remediation SLAs.

Data Eng

Use AI for profiling. You build the pipelines that fix the root cause.

AI for Data Profiling and Anomaly Detection

TL;DR

  • AI can profile columns, detect outliers, and flag distribution shifts.
  • AI can't tell you which anomalies matter for the business or what the fix is.
  • Use AI to surface. You diagnose, prioritize, and remediate.

Data quality tools have existed for years. AI adds: smarter profiling (inferring patterns, not just stats), anomaly detection over time (distribution drift), and natural language queries ("show me columns with unexpected nulls"). The output is still a list of "things that look wrong." You decide what to do.

What AI Profiling Adds

Smarter profiling:

  • Beyond min/max/avg — AI infers expected patterns. "Values usually look like emails; here are 50 that don't."
  • Format detection, referential integrity checks, cross-column consistency.

Temporal anomaly detection:

  • "This column's null rate jumped from 1% to 15% this week." — AI flags it. You investigate.
  • Baseline from history. Alert on divergence. Reduces alert fatigue if tuned well.

Natural language queries:

  • "Find duplicates in customer table" or "Where might we have encoding issues?" — AI translates to queries or checks.
  • Useful for ad-hoc investigation. Don't rely on it for regulated reporting.

The False Positive Problem

AI will flag:

  • Real issues (fix these)
  • Benign anomalies ("we added a new region, of course geography changed")
  • Noise (one-off data load, test data mixed in)

You need a triage process. Otherwise the alert queue becomes useless. Assign severity, add context, close false positives with a note. Over time, AI can learn — if you feed it back.

Root Cause vs. Symptom

AI finds symptoms: "Null rate is high." It doesn't fix the pipeline that's producing bad data. That's engineering work. Profile and alert are step 1. Root cause analysis and pipeline fix are step 2. Don't stop at step 1.

Manual process. Repetitive tasks. Limited scale.

Click "With AI" to see the difference →

Quick Check

What remains human when AI automates more of this role?

Do This Next

  1. Run AI profiling on one table — Pick something mission-critical. What does AI flag? Manually review. How many are true positives? Document the ratio.
  2. Define quality SLAs — For key tables: freshness, completeness, accuracy. AI can monitor; you set the bar.
  3. Build a triage workflow — When AI flags an anomaly, who investigates? What's the SLA? Document it. Run one cycle to validate.