Catalog
concept#Data#Analytics#Data Analysis#Insight Discovery

Explorative Data Analysis (EDA)

Explorative Data Analysis is an important process for discovering patterns and relationships in data before conducting formal analyses.

Exploratory Data Analysis (EDA) is used to visually and statistically explore data to generate hypotheses and gain key insights.
Established
Medium

Classification

  • Medium
  • Business
  • Design
  • Intermediate

Technical context

Databases like MySQL or PostgreSQL.Analysis tools like Python or R.Visualization tools like Tableau or Power BI.

Principles & goals

Data should come from various sources.Anomalies must be identified and analyzed.Visualization plays a crucial role.
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Irrelevant data can skew results.
  • Lack of standardization can lead to inconsistencies.
  • Misinterpretation of results is possible.
  • Always look at data from different perspectives.
  • Pay attention to consistency in the data.
  • Offer regular training for users.

I/O & resources

  • Raw data from various sources.
  • Set analysis goals.
  • Technical resources for data processing.
  • Report on analysis results.
  • Visual representations of the data.
  • Identified patterns and anomalies.

Description

Exploratory Data Analysis (EDA) is used to visually and statistically explore data to generate hypotheses and gain key insights. EDA is critical for data-driven decisions and helps analysts identify central trends and anomalies.

✔

  • Improves data quality through hypothesis generation.
  • Develops a better understanding of the data.
  • Supports data-driven decisions.

✖

  • EDA can be time-consuming.
  • Often requires specific knowledge.
  • Can lead to false conclusions if not done carefully.

  • Data Quality

    Measure of accuracy and completeness of data.

  • Analysis Time

    Time required to perform the data analysis.

  • User Satisfaction

    Assessment of user satisfaction post-analysis.

Customer Analysis at Company X

Company X uses EDA to analyze customer behavior and adjust offerings accordingly.

Health Data Analysis

Analyzing health data helps identify trends and risk factors in the population.

Sales Trend Analysis

Using EDA, a company can track the performance of its products over various time periods.

1

Compile data sources.

2

Define analysis goals.

3

Analyze and visualize data.

⚠️ Technical debt & bottlenecks

  • Using outdated analysis tools.
  • Regularly addressing insufficient data quality.
  • Lack of standards in data processing.
Insufficient data quality.Lack of user competence.Limited tools for analysis.
  • Conducting analyses based on flawed data.
  • Neglecting anomalies in the data.
  • Not establishing clear hypotheses before analysis.
  • Misinterpretation of visualizations.
  • Lack of documentation of analysis results.
  • Excessive reliance on tools.
Basic knowledge of statistics.Ability to visualize data.Familiarity with data analysis tools.
Technological advancements in data processing.Availability of data analysis tools.Increasing competition in the market requiring innovation.
  • • Data must comply with legal requirements.
  • • Technological limitations must be considered.
  • • Resource allocation may vary.