Catalog
concept#Data#Analytics#Platform

Data Analysis

Data analysis describes processes for processing, evaluating, and interpreting raw data to derive business-relevant insights.

Data analysis is the systematic process of inspecting, cleaning, transforming, and modeling data to extract meaningful insights and support decision-making.
Established
Medium

Classification

  • Medium
  • Business
  • Design
  • Intermediate

Technical context

Databases (e.g., PostgreSQL, Snowflake)ETL/ELT pipelines (e.g., Airflow, dbt)Visualization tools (e.g., Superset, Tableau)

Principles & goals

Data quality first: valid, complete, and consistent data are prerequisites.Transparent method choice: assumptions and steps must be documented.Iterative approach: exploratory analysis before final models.
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Wrong decisions due to misinterpreted results.
  • Biases from unrepresentative samples.
  • Excessive complexity leads to poor traceability.
  • Embed automated data validation in pipelines.
  • Document and version results reproducibly.
  • Include interdisciplinary teams for interpretation.

I/O & resources

  • Raw data from systems and sensors
  • Metadata and data catalogs
  • Business questions and objectives
  • Analytical reports and dashboards
  • Models, hypothesis tests and KPIs
  • Recommendations for actions and changes

Description

Data analysis is the systematic process of inspecting, cleaning, transforming, and modeling data to extract meaningful insights and support decision-making. It encompasses descriptive, exploratory, and inferential techniques across quantitative and qualitative data. Proper analysis reveals patterns, validates hypotheses, and informs strategic actions.

✔

  • Better decision basis through data-driven insights.
  • Early identification of opportunities and risks.
  • Efficiency gains through targeted action identification.

✖

  • Limited validity with poor data quality.
  • Correlation is not causation; interpretation risks exist.
  • Privacy and compliance constraints may limit analyses.

  • Time-to-Insight

    Time from data availability to actionable insight.

  • Data quality score

    Measurement of completeness, accuracy, and consistency.

  • Adoption rate of analysis outcomes

    Share of recommendations that feed into decisions.

Sales data analysis for assortment optimization

A retailer uses transaction and inventory data to adjust assortment and order quantities.

Usage analysis of a SaaS platform

Product team analyzes feature adoption and derives product priorities.

Operational monitoring and anomaly reporting

A manufacturer detects machine failures early through time-series analysis.

1

Define objectives and select relevant metrics.

2

Identify, collect, and clean data sources.

3

Perform exploratory analysis and form hypotheses.

4

Validate models, operationalize results, and monitor.

⚠️ Technical debt & bottlenecks

  • Poorly documented transformation logic in ETL
  • Manual steps in pipelines that block automation
  • Missing versioning of datasets and models
Fragmented data sourcesInsufficient data integrationLack of qualified personnel
  • Deriving causal claims from purely correlative findings.
  • Basing decisions on undocumented assumptions.
  • Using confidential data without anonymization for exploration.
  • Overfitting from overly complex models on small datasets.
  • Selection bias from unsuitable sample selection.
  • Confusing data quality symptoms with business problems.
Basic statistics skillsData cleaning and SQL skillsDomain knowledge for meaningful interpretation
Data quality and consistencyAvailability and access speedScalability of the analysis infrastructure
  • • Privacy regulations (e.g., GDPR)
  • • Limited compute resources in infrastructure
  • • Data access rights and silos