Catalog
concept#Analytics#Data#Observability#Platform

Analytics

A strategic approach to systematically evaluating data to derive actionable insights and improve decision-making.

Analytics denotes the systematic collection, processing and analysis of data to derive actionable insights for decision‑making.
Established
Medium

Classification

  • Medium
  • Business
  • Architectural
  • Intermediate

Technical context

Databases (SQL/NoSQL)ETL/ELT pipelines and data platformsBI and visualization tools

Principles & goals

Data-driven decisions must be measurable and traceable.Analyses should be context-aware and domain-informed.Transparent metrics and governance ensure trust and auditability.
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Misinterpreting correlations as causation.
  • Privacy breaches due to insufficient governance.
  • Overreliance on predictive models without domain validation.
  • Start with clear hypotheses and iterative experiments
  • Introduce automated data quality checks
  • Establish governance rules for access, retention and metric definitions

I/O & resources

  • Raw data from event, transaction and system sources
  • Domain metrics and business KPIs
  • Access rights and governance definitions
  • Analytical reports, dashboards and alerts
  • Models and predictions for decision support
  • Recommendations for product and process changes

Description

Analytics denotes the systematic collection, processing and analysis of data to derive actionable insights for decision‑making. It spans methods, metrics and tools from descriptive to predictive analytics and links technical infrastructure with business questions. The goal is to improve products, processes and strategic decisions.

✔

  • Improved decision quality through data-based insights.
  • Faster identification of optimization opportunities in products and processes.
  • Increased transparency over business and operational metrics.

✖

  • Insight quality strongly depends on data quality and availability.
  • Complex analyses can incur high infrastructure and operational costs.
  • Wrong metric definitions lead to misleading priorities.

  • Time to Insight

    Time from data availability to actionable insight.

  • Data coverage

    Percentage of relevant data sources included in analyses.

  • Dashboard adoption

    Share of teams regularly using provided dashboards.

E‑commerce conversion optimization

Analyzing usage data to identify drop-off pages and optimize checkout flows.

Reducing incidents in operations

Telemetry analyses enable proactive anomaly detection and reduction of incidents.

Marketing attribution

Linking campaign data with usage metrics to evaluate channel effectiveness.

1

Define objectives and KPIs, align stakeholders

2

Catalog data sources and build integration paths

3

Introduce initial analyses, dashboards and validation loops

⚠️ Technical debt & bottlenecks

  • Legacy data pipelines without test and monitoring mechanisms.
  • Missing data catalog hinders reuse and governance.
  • Ad-hoc scripts for key KPIs instead of reproducible pipelines.
Data integrationQuery execution latencyLack of domain expertise
  • KPIs are manipulated to meet short-term goals.
  • Automated predictions are adopted into production without validation.
  • Personal data is used in analyses without consent.
  • Confusing correlation with causation when deriving actions.
  • Scaling too early before validating assumptions.
  • Unclear ownership leads to outdated or conflicting metrics.
Data modeling and data integrationStatistics and exploratory data analysisDomain knowledge to interpret results
Scalability of data processingData quality and data catalogSecurity and privacy (compliance)
  • • Available infrastructure and operational capacity
  • • Legal requirements for privacy and retention
  • • Heterogeneous data sources and formats