Data Analytics – Trends and Stability¶

Purpose¶

The Data Analytics module reveals long-term patterns, stability, and volatility in your datasets — turning raw metrics into meaningful insights.
It provides a higher-level analytical layer over the results of Data Anomalies, enabling teams to understand changes over time and improve both Data Quality and observability of data pipelines.

By identifying trend breaks, recurring patterns, and volatility shifts, digna Data Analytics helps you distinguish between expected seasonal behavior and real data quality issues.

Technical Overview¶

Derived Statistics¶

digna Data Analytics calculates statistical properties such as:

Trend – long-term direction of a metric (increasing, decreasing, stable)
Volatility – how much a metric fluctuates within a given time window
Seasonality – recurring temporal patterns (daily, weekly, monthly)
Change Points – statistically significant shifts in behavior

Supported Metrics¶

The module can analyze any metric generated by other digna modules, including:

Record counts
Missing value rates
Distribution statistics (min, max, mean, variance)
KPI aggregations (e.g., revenue, transactions, claims)
Timeliness deviations or anomaly frequencies

Time-Series Analysis¶

Data Analytics evaluates stability across periods — comparing one week, month, or quarter to another — using statistical confidence and visual metrics for trend stability.

How It Works¶

Input Data – digna collects time-series metrics from other modules (e.g., number of anomalies).
Statistical Modeling – AI and statistical functions identify underlying trends and volatility levels.
Comparison Across Periods – digna compares historical and current performance for KPIs or quality indicators.
Insights Generation – dashboards display detected trends, stable periods, and change points in Inspection Hub and analytics views.

This enables proactive detection of slow drifts or gradual degradation in data quality before they become critical.

Example Use Cases¶

Use Case	Description
Monitoring KPI stability	Track sales, transactions, or claims over time and detect unusual volatility.
Detecting hidden data drift	Observe slow shifts in data distributions or missing-value rates that typical rules overlook.
Change point analysis	Identify when a metric changes its behavior (e.g., sudden increase in anomalies).
Operational reliability	Evaluate periods of high vs. low data stability across systems or departments.
Business insights	Highlight top-performing categories or products over rolling periods.

Benefits¶

Area	Benefit
Visibility	Provides long-term insight into trends and patterns of data quality.
Early Warning	Detects slow drifts before they trigger anomalies or SLA breaches.
Optimization	Helps identify unstable data sources or systems needing process tuning.
Cross-Module Analysis	Combines data from Anomalies, Validation, and Timeliness for holistic insights.
Actionable Insights	Supports both technical teams and business users in unders