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concept#Data#Analytics#Data Analysis

Business Intelligence (BI)

Business Intelligence (BI) includes technologies, applications, and practices for analyzing data and providing actionable insights.

Business Intelligence refers to the processes and technologies that companies use to collect, analyze, and visualize data.
Established
Medium

Classification

  • Medium
  • Business
  • Design
  • Advanced

Technical context

CRM systemsDatabasesReporting tools

Principles & goals

Ensure data integrityMake data-driven decisionsSimple visualization of complex data
Build
Enterprise

Use cases & scenarios

Compromises

  • Misinterpretation of data
  • Data privacy risks
  • Dependency on technologies
  • Regular data reviews
  • Training for staff
  • Use of visualization techniques

I/O & resources

  • Access to relevant data sources
  • Training in using BI tools
  • Data management policies
  • Actionable business insights
  • Reports and dashboards
  • Graphical data analyses

Description

Business Intelligence refers to the processes and technologies that companies use to collect, analyze, and visualize data. This enables informed decision-making and strategic planning by leveraging real-time data analytics.

  • Increased efficiency
  • Better decision-making
  • Faster access to data

  • High implementation costs
  • Complexity of tools
  • Data manipulation possible

  • ROI of BI Solutions

    Measurement of the return on investment of BI applications.

  • Processing Time of Data Requests

    The time needed to process data requests.

  • User Satisfaction

    Assessment of user satisfaction with BI tools.

Sales Analysis at Company X

Company X used BI tools to analyze sales data and make strategic decisions.

Customer Satisfaction Study at Company Y

Company Y evaluated customer feedback to improve products.

Market Research by Company Z

Company Z conducted market analyses to identify new business opportunities.

1

Identify and integrate data sources

2

Select and set up BI tools

3

Create report templates

⚠️ Technical debt & bottlenecks

  • Outdated BI software
  • Insufficient hardware resources
  • Lack of documentation
Data QualityDelays in data processingTechnological dependencies
  • Misinterpretation of BI data
  • Data leaks due to insecure practices
  • Overload from too much data
  • Too high expectations for BI tools
  • Neglecting data security
  • Lack of user acceptance
Data analysis skillsKnowledge in BI toolsCommunication skills
Integration of data sourcesReal-time data processingUser-friendly interface
  • Compliance with data protection regulations
  • Budget constraints
  • Technical infrastructure