concept#Data#Analytics#Governance#Observability

Reporting

A structured process for producing reports and dashboards to support decision making.

Reporting describes the systematic collection, aggregation and presentation of data into meaningful reports and dashboards.

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaBusiness
Decision typeOrganizational
Organizational maturityIntermediate

Technical context

Integrations

Data warehouse / lakehouseBI and dashboarding toolsIdentity and access management

Principles & goals

Principles

Establish a single source of truthEnsure data quality before visualizationEnable self-service with governance

Value stream stage

Run

Organizational level

Enterprise, Domain, Team

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Misinterpretation of undefined metrics
Data leaks due to overly broad access rights
Maintenance overhead from many bespoke reports

Best practices

Maintain a central catalog of key metrics
Implement automated tests for metrics
Provide self-service with clear guardrails

I/O & resources

Inputs

Raw data from source systems
Data model and metric definitions
Access and governance rules

Outputs

Interactive dashboards
Scheduled report runs and exports
Archived audit reports

Resources

Description

Reporting describes the systematic collection, aggregation and presentation of data into meaningful reports and dashboards. It combines data integration, metric definitions and visualization to support decisions across organizational levels. Implementations vary by audience, cadence and degree of automation.

✔Benefits

Faster, better-informed decisions
Increased transparency of business metrics
Support for compliance and reporting obligations

✖Limitations

Dependence on data quality and availability
Potential latency with aggregated data
Initial effort for definitions and governance

Trade-offs

Metrics

Report refresh time
Measurement of time from data availability to report update.
User adoption
Share of active users and frequency of report usage.
Metric accuracy
Share of validated metrics without discrepancies or errors.

Examples & implementations

E‑commerce weekly report

Weekly revenue summary with conversion, returns and inventory metrics for the operations team.

Quarterly finance report for investors

Consolidated reporting with EBIT, cash flow and variance analyses for external communication.

IT system health dashboard

Real-time monitoring of technical metrics like latency, error rates and capacity utilization.

Implementation steps

Clarify goals and stakeholders

Define metrics and data sources

Select technical pipeline and reporting tool

Plan governance, tests and rollout

⚠️ Technical debt & bottlenecks

Technical debt

Duplicated calculations across reports
Missing test coverage for metrics
Outdated data pipelines without monitoring

Known bottlenecks

Data integrationData qualityAccess control

Misuse examples

Using inconsistent metrics for cross-department comparisons
Publishing sensitive data without access control
Using reporting as a substitute for root‑cause analysis

Typical traps

Unclear KPI definitions lead to misinterpretation
Too many metrics overwhelm users
Technical debt from delayed automation

Required skills

SQL and data queryingData modeling and BI designVisualization and storytelling

Architectural drivers

Data quality and trustworthinessScalability of aggregation and query pathsSecurity and access models

Constraints

• Legal requirements (privacy, retention)
• Heterogeneous source systems and formats
• Limited resources for integration and maintenance