What is Anomaly Detection in Data?

Anomaly Detection Explained

Your orders table averages 10,000 rows per day. Today it has 500. Is that a real business slowdown, or did something break?

Your revenue column averages $50 per transaction. Suddenly the average is $5,000. Is that a big customer, or a decimal point error?

Anomaly detection answers these questions automatically. By learning what "normal" looks like from historical data, it can flag when something unusual happens—often before anyone notices a problem.

How Anomaly Detection Works

Anomaly detection follows a three-step process:

1. Establish Baselines

First, the system analyzes historical data to understand normal patterns. Using data profiling, it calculates statistics like:

• Average row counts and typical variation
• Normal null rates for each column
• Expected value distributions (mean, standard deviation)
• Regular patterns (daily, weekly, monthly cycles)

2. Monitor Continuously

As new data arrives, the system compares it against established baselines. This happens automatically—no manual queries needed.

3. Alert on Deviations

When data falls outside expected ranges (typically measured in standard deviations from the mean), an alert is triggered. The severity depends on how far outside the range the value is.

Types of Data Anomalies

Volume Anomalies

Unexpected changes in row counts. Examples:

• A table that normally gets 10K rows/day suddenly gets 100
• Duplicate records causing row counts to double
• A full table reload causing massive count increases

Volume anomalies often indicate pipeline failures, partial data loads, or unexpected data influxes.

Distribution Anomalies

Changes in how values are distributed. Examples:

• Average order value jumps from $50 to $5,000
• A percentage field suddenly has values over 100%
• Age values containing negative numbers

Distribution anomalies often indicate data transformation bugs, unit conversion errors, or upstream data changes.

Null Rate Anomalies

Sudden changes in missing data. Examples:

• An email column goes from 2% null to 40% null
• A previously always-null column starts getting values
• Required fields start allowing nulls

Null rate anomalies often indicate source system changes, ETL logic bugs, or schema modifications.

Freshness Anomalies

Data not updating as expected. Examples:

• A table that updates hourly hasn't updated in 6 hours
• The max timestamp in a table is yesterday when it should be today
• No new rows added during a normally active period

Freshness anomalies indicate pipeline failures, source system outages, or scheduling issues.

Pattern Anomalies

Breaks in expected patterns. Examples:

• Weekend traffic suddenly matching weekday levels
• Month-end processing happening mid-month
• Holiday patterns appearing on regular days

Anomaly Detection Methods

Statistical Methods

Basic statistical approaches compare values against standard deviations:

• Z-score: Flags values more than N standard deviations from the mean
• IQR (Interquartile Range): Identifies outliers based on quartile boundaries
• Moving averages: Compares values against rolling historical averages

These methods work well for simple, normally distributed data but may miss complex patterns.

Machine Learning Methods

ML-based approaches can detect more sophisticated anomalies:

• Isolation Forest: Isolates anomalies by randomly partitioning data
• Autoencoders: Neural networks that learn to reconstruct normal data, flagging poorly reconstructed points
• LSTM models: Capture time-series patterns and seasonal variations

ML methods handle complex patterns better but require more data and can be harder to interpret.

Rule-Based Methods

Custom business rules that define acceptable ranges:

• Order amount must be between $1 and $10,000
• Null rate must be below 5%
• Row count change must be within 30% of previous day

Rule-based methods are interpretable and catch known issues but can't detect unexpected anomaly types.

Implementing Anomaly Detection

DIY Approach

You can implement basic anomaly detection with SQL:

-- Detect volume anomalies
WITH daily_counts AS (
  SELECT DATE(created_at) as date, COUNT(*) as row_count
  FROM orders
  GROUP BY 1
),
stats AS (
  SELECT AVG(row_count) as avg_count, STDDEV(row_count) as std_count
  FROM daily_counts
  WHERE date >= CURRENT_DATE - 30
)
SELECT date, row_count,
  CASE WHEN ABS(row_count - avg_count) > 2 * std_count
       THEN 'ANOMALY' ELSE 'NORMAL' END as status
FROM daily_counts, stats
WHERE date = CURRENT_DATE;

This works for simple cases but requires manual setup for each metric and doesn't scale well.

Data Observability Platforms

Data observability tools like Sparvi automate anomaly detection:

• Automatically profile data to establish baselines
• Continuously monitor all tables without manual configuration
• Use ML to detect complex patterns and reduce false positives
• Alert via Slack, email, or PagerDuty when anomalies are detected

Frequently Asked Questions

What is anomaly detection in data?

Anomaly detection in data is the process of identifying data points, patterns, or values that deviate significantly from expected behavior. It uses statistical methods or machine learning to automatically flag unusual changes that might indicate data quality issues.

How does anomaly detection work?

Anomaly detection works by first establishing a baseline of normal behavior from historical data. Then it continuously monitors new data, comparing it against this baseline. When values fall outside expected ranges or patterns change significantly, an alert is triggered.

What types of data anomalies can be detected?

Common types of data anomalies include: volume anomalies (unexpected row count changes), distribution anomalies (value shifts outside normal ranges), null rate spikes, freshness issues (data not updating), and pattern breaks (sudden changes in data patterns).