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ServiceAnalytics
Availability2014.1 or later

Business Outcomes

Link tech issues to revenue

Stream Correlation

Detect event sequences live

Errors and Failure Analysis

Find the root cause

Security and Privacy

Protect sensitive correlated data

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Overview

GermainUX correlates user activity, business events, application transactions and technology telemetry collected from multiple data sources. It helps teams reconstruct what happened across applications and systems, follow a transaction from the user experience to backend dependencies, and identify the event, component or condition responsible for a slow or failed business outcome. By combining exact-match, identifier-based and streaming correlation, GermainUX can connect data even when it originates from different applications, monitoring components, logs, APIs, databases or infrastructure platforms.

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Business Outcomes

GermainUX correlation and tracing help organizations:
  • Follow user and business transactions across applications and technology layers.
  • Connect Session Replay with application, integration, database and infrastructure evidence.
  • Reduce the time required to diagnose slow, failed or incomplete transactions.
  • Understand dependencies and the sequence of events leading to an issue.
  • Reconstruct cross-application customer journeys and employee workflows.
  • Distinguish root causes from downstream symptoms.
  • Quantify which users, sessions and business processes were affected.
  • Apply organization-specific correlation logic where standard trace identifiers are unavailable.

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From User Action to Root Cause

GermainUX can correlate activity across a transaction path such as:
User interactionBrowser or applicationHTTP requestIntegrationApplication codeDatabaseInfrastructure
Depending on the environment, correlated evidence can include:
  • User and session activity
  • Business-process milestones
  • Browser and native application events
  • HTTP requests and responses
  • Application transactions and methods
  • Logs, errors and exceptions
  • API and integration calls
  • Database queries
  • Infrastructure metrics and events
  • Customer, product, order or case data
The precise trace depends on the identifiers, timestamps, attributes and instrumentation available from each source.

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Three Correlation Methods

GermainUX provides three complementary correlation approaches. More than one method can be used in the same investigation. For example, an ID may connect backend spans while exact-match rules connect the trace with a business record and stream correlation detects the surrounding failure pattern.

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Exact-Match Correlation

Exact-Match Correlation links records when configured fields or conditions match. Examples include:
  • Same session or user identifier
  • Same order, case, account or business-object value
  • Same application, environment and error signature
  • Same message or categorized failure
  • Events occurring within a configured time range and sharing selected attributes
This method is useful when related sources do not propagate a dedicated end-to-end trace ID but contain values that can reliably identify the same activity.

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Example

A checkout error, payment-provider log and order record may be correlated using:
  • Order ID
  • Customer or session identifier
  • Payment operation
  • Application environment
  • Compatible timestamps
The resulting view connects the user-facing failure with the related business and technology evidence.

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Correlation Precision

Correlation based on a unique shared value can provide strong evidence. Correlation based only on approximate timestamps or common values can produce ambiguous matches. Rules should therefore use the most selective attributes available and define appropriate time windows, constraints and source relationships. Learn more about Exact-Match Correlation.

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ID-Based Correlation

ID-Based Correlation connects events and transactions through identifiers propagated across applications or system components. Common identifiers include:
  • Trace ID
  • Correlation ID
  • Request ID
  • Session ID
  • Transaction ID
  • Order or case ID
  • Parent and child span IDs
  • Organization-specific business identifiers
This approach is particularly useful for distributed systems because each participating component can attach the same correlation identifier—or a related parent/child identifier—to its telemetry.

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Example

A user action initiates an HTTP request. The request ID is propagated through an integration service, application method and database operation. GermainUX uses the identifier to assemble the related events into one end-to-end trace. When identifiers are unique, consistently propagated and correctly captured, ID-based correlation usually provides the strongest deterministic relationship among distributed events. Learn more about ID-Based Correlation.

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Stream Correlation

Stream Correlation analyzes continuously arriving events in near real time and detects relationships according to configured sequence, timing and attribute rules. It can identify patterns such as:
  • Event A followed by Event B within a defined period
  • A workflow start without a corresponding completion
  • Repeated failures preceding abandonment
  • A resource spike followed by transaction degradation
  • A specific sequence of user, application and integration events
  • Multiple related conditions occurring across different data streams
Stream correlation is useful when the relationship is defined by behavior over time rather than one shared identifier.

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Example

A stream rule could identify sessions where:
1

A user starts checkout

2

A payment request fails within two minutes

3

The user repeats the payment action

4

No purchase-completion event occurs

5

The session ends

The correlated pattern represents a payment-related checkout abandonment even if no single record contains the complete story. Learn more about Stream Correlation.

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Correlation Versus Causation

Correlation shows that events are related according to identifiers, matching attributes, time or sequence. It does not always prove that one event caused another. Root-cause conclusions should consider:
  • Strength and uniqueness of the correlation evidence
  • Event order and timing
  • Parent and child transaction relationships
  • Application architecture and known dependencies
  • Repeated occurrence across similar instances
  • Supporting code, log, network and Session Replay evidence
  • Whether alternative causes have been excluded
GermainUX helps assemble and analyze this evidence, while teams retain the ability to validate the conclusion against the monitored system.

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Custom Correlation Rules

Correlation can be tailored to the organization’s data model, architecture and business processes. Custom configuration can define:
  • Source and target data types
  • Correlation direction
  • Identifiers and matching fields
  • Field transformations or normalized values
  • Time windows and ordering
  • Parent and child relationships
  • Required and optional conditions
  • Relationship strength or confidence
  • Constraints by application, environment or business context
  • How the relationship appears in dashboards and traces
This flexibility allows teams to correlate standard telemetry with organization-specific transactions and business events.

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Cross-Application Journeys and Workflows

Correlation is not limited to technical transactions. GermainUX can connect events across a customer journey or employee workflow. Examples include:
  • Campaign → Website visit → Product view → Cart → Checkout → Fulfillment
  • Customer search → Chatbot conversation → Agent handoff → Case resolution
  • CRM click → HTTP request → Integration → Apex or application code → Database
  • Employee action → Approval workflow → Backend job → Confirmation
  • Identity submission → Verification services → Manual review → KYC decision
Journey correlation depends on the identifiers and events available across each participating channel and system.

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Session Replay and User Context

When Real User Monitoring is deployed, a correlated transaction can be connected with the corresponding Session Replay. This allows teams to see:
  • What the user attempted
  • Which page, screen or workflow was involved
  • What happened before and after the transaction
  • Whether the user saw an error or slowdown
  • How the user responded to the issue
  • Whether the journey completed, recovered or was abandoned
Session Replay provides user-experience context that backend tracing alone cannot show.

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Transaction Timing and Performance

Correlated traces can break transaction time down across observable layers, such as:
  • Browser or native client
  • Network
  • Web server
  • Application server
  • Integrations
  • Database
  • Infrastructure
The trace timeline helps identify the critical path, slow operations, parallel work, waits, gaps and dependency latency. Correlated code, query and infrastructure evidence can then reveal why a particular layer was slow.

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Errors and Failure Analysis

GermainUX can correlate errors with:
  • The affected user and session
  • The triggering transaction
  • Related logs and exceptions
  • Application methods and stack traces
  • HTTP requests and integrations
  • Database operations
  • Infrastructure conditions
  • The business process or journey outcome
This helps teams determine whether an error was the root cause, a downstream symptom or an unrelated event occurring at the same time.

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Visualization Options

Correlated data can be visualized in several ways according to the analytical objective. Custom visualizations and relationships can be configured for specific data sources and use cases.

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AI-Assisted Analysis

AI Explore can help teams ask questions about the available correlated evidence, including:
  • What happened before and after this event?
  • Which component contributed most to the delay?
  • Which related errors or KPIs changed at the same time?
  • Which users and business processes were affected?
  • Is this pattern new or recurring?
  • What is the likely root cause?
  • What should be investigated next?
AI-generated conclusions should be validated against the underlying trace, identifiers, timings and system architecture.

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Alerts and Automation

Correlated conditions can trigger:
  • Real-time alerts
  • SLA notifications
  • Scheduled reports
  • Dynamic investigation actions
  • Creation or enrichment of an incident
  • Approved remediation workflows
For example, GermainUX can alert only when a technical error is correlated with a failed user journey, reducing noise from errors with no observed user or business impact.

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Validate Resolution

After remediation, GermainUX can verify whether:
  • The correlated failure pattern stopped occurring
  • Transaction duration improved
  • Errors and retries declined
  • User journeys recovered
  • The affected-user count decreased
  • The issue remained resolved across applications, versions and environments

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Security and Privacy

Correlation can combine user, business and technical data from multiple systems. Configuration should follow the organization’s privacy, security, retention and access requirements. Sensitive identifiers and values can be masked, anonymized or excluded where appropriate. Access to traces, Session Replay and correlated business records should be limited to authorized users.

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Additional Resources

Outlier Detection

Read more

AI

Insights & Recommendations

Analytics

More Analytics Features

Automation

More Automation Features

Dashboards

Dashboards and reports overview

Reports

Automatic and scheduled reports

Management

More Management Features

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The content of this document is proprietary to GERMAIN SOFTWARE.