Catalog
concept#Data#Platform#Data Analysis#Self-Service Analytics

Self-Service Analytics

Self-Service Analytics enables users to independently create analyses and reports without IT support.

Self-Service Analytics provides a platform that allows users to carry out data analyses independently.
Established
Medium

Classification

  • Medium
  • Technical
  • Organizational
  • Intermediate

Technical context

CRM SystemsDatabasesAnalysis Tools

Principles & goals

User-Centered DesignReal-Time Data AvailabilitySimple Navigation
Build
Team, Domain

Use cases & scenarios

Compromises

  • Misjudgments by Untrained Users
  • Data Misuse by Unauthorized Access
  • Incompatibility with Existing Systems
  • Offer Regular Training
  • Gather User Feedback
  • Review Data Regularly

I/O & resources

  • Access Permissions to Data Sources
  • Available Analysis Tools
  • User Training
  • Analyzed Data
  • Generated Reports
  • Data Visualizations

Description

Self-Service Analytics provides a platform that allows users to carry out data analyses independently. This solution reduces dependency on IT resources and enhances decision-making by providing direct access to current data.

  • Increased Efficiency
  • Faster Decision-Making
  • Less Dependence on IT

  • Requires Technical Know-How
  • Data Quality May Vary
  • Security Risks in Data Access

  • User Satisfaction

    Measurement of user satisfaction with the tool.

  • Data Integrity

    Proportion of accurate and consistent data.

  • Saved IT Resources

    Measurement of the reduced resource burden in IT.

Application in Product Development

Self-Service Analytics was successfully used to analyze market research results.

Optimization of Marketing Campaigns

The marketing team uses Self-Service Analytics to analyze campaign data in real-time.

Data Analyses for Management

Management uses Self-Service Analytics to create presentations and analyses.

1

Conduct User Training

2

Set Up Access Rights

3

Deploy the Tool

⚠️ Technical debt & bottlenecks

  • Outdated Software Versions
  • Insufficient Documentation
  • Missing Security Updates
Lack of Knowledge among UsersOutdated Data SourcesTechnological Backlog
  • Insufficient Data Review
  • Using Analyses Without Context
  • Ignore User Errors
  • Assuming All Users Are the Same
  • Focus Only on Technical Aspects
  • Lack of Communication in the Team
Data Analysis SkillsFamiliarity with Analysis ToolsBasic Knowledge in Data Management
Integration of Existing SystemsData AvailabilityUsability
  • Resource Fluctuations
  • Technological Limitations
  • Training Requirements