concept#Data#Analytics#Decision Making

Data-Driven Decision Making

Data-driven decision making uses analytical approaches to make informed decisions.

Data-driven decision making enables organizations to make decisions based on accurate data analyses and statistical methods.

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaBusiness
Decision typeDesign
Organizational maturityAdvanced

Technical context

Integrations

CRM SystemsData Visualization ToolsBusiness Intelligence Platforms

Principles & goals

Principles

Data must be accurate and relevant.Decisions should be based on quantitative analyses.Transparency in decision-making processes is important.

Value stream stage

Build

Organizational level

Enterprise, Domain, Team

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Privacy risks may arise.
Inadequate analysis can lead to incorrect conclusions.
Technical challenges in data processing.

Best practices

Regular Review of Data Quality
Ensure Data Security
Involve Stakeholders in the Decision-Making Process

I/O & resources

Inputs

Market Research Data
Customer Data
Sales History

Outputs

Data-Driven Decisions
Improved Customer Engagement
Higher Conversion Rates

Resources

Description

Data-driven decision making enables organizations to make decisions based on accurate data analyses and statistical methods. It promotes efficiency and precision in decision-making.

✔Benefits

Improves the accuracy of decisions.
Increases efficiency in the decision-making process.
Enables proactive rather than reactive decisions.

✖Limitations

Data can be flawed or incomplete.
Lack of data literacy can hinder application.
Over-reliance on data can lead to poor decisions.

Trade-offs

Metrics

Time-to-Decision
The time taken to make an informed decision.
Customer Satisfaction Index
A measure of customer satisfaction with the services offered.
ROI of Projects
The Return on Investment for projects that use data-driven decisions.

Examples & implementations

Launch of a New Product Line

A company conducted extensive market and customer needs analysis before launching a new product line.

Optimization of Customer Service

By analyzing customer feedback, a company significantly improved its customer service.

Improvement of Sales Effectiveness

A company used data analysis to improve the effectiveness of its sales teams.

Implementation steps

Select Appropriate Data Analysis Tools

Train the Team in Data Analysis

Collect and Analyze Data

⚠️ Technical debt & bottlenecks

Technical debt

Outdated IT Systems
Lack of Documentation for Data Analyses
Inadequate Data Integration Strategies

Known bottlenecks

Data Integration IssuesLack of Qualified AnalystsSlow Processing of Large Data Sets

Misuse examples

Decision Making Without Data Analysis
Reliance on a Single Data Source
Neglecting Data Security

Typical traps

Using Poor Data Quality
Making Decisions Too Quickly
Lack of Communication within the Team

Required skills

Data Analysis and InterpretationKnowledge of StatisticsUnderstanding of Business Objectives

Architectural drivers

Integration of Data Analysis ToolsCompliance with Data Privacy RegulationsSustainability of Data Quality

Constraints

• Data Access and Availability
• Technological Infrastructure
• Data Usage Policies