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BABOK Guide
BABOK Guide
10. Techniques
Introduction 10.1 Acceptance and Evaluation Criteria 10.2 Backlog Management 10.3 Balanced Scorecard 10.4 Benchmarking and Market Analysis 10.5 Brainstorming 10.6 Business Capability Analysis 10.7 Business Cases 10.8 Business Model Canvas 10.9 Business Rules Analysis 10.10 Collaborative Games 10.11 Concept Modelling 10.12 Data Dictionary 10.13 Data Flow Diagrams 10.14 Data Mining 10.15 Data Modelling 10.16 Decision Analysis 10.17 Decision Modelling 10.18 Document Analysis 10.19 Estimation 10.20 Financial Analysis 10.21 Focus Groups 10.22 Functional Decomposition 10.23 Glossary 10.24 Interface Analysis 10.25 Interviews 10.26 Item Tracking 10.27 Lessons Learned 10.28 Metrics and Key Performance Indicators (KPIs) 10.29 Mind Mapping 10.30 Non-Functional Requirements Analysis 10.31 Observation 10.32 Organizational Modelling 10.33 Prioritization 10.34 Process Analysis 10.35 Process Modelling 10.36 Prototyping 10.37 Reviews 10.38 Risk Analysis and Management 10.39 Roles and Permissions Matrix 10.40 Root Cause Analysis 10.41 Scope Modelling 10.42 Sequence Diagrams 10.43 Stakeholder List, Map, or Personas 10.44 State Modelling 10.45 Survey or Questionnaire 10.46 SWOT Analysis 10.47 Use Cases and Scenarios 10.48 User Stories 10.49 Vendor Assessment 10.50 Workshops

3. Techniques

3.5 Data Flow Diagrams

Guide to Business Data Analytics

3.5.1 Purpose

Data flow diagrams show where data comes from, which activities process the data, and if the output results are stored or utilized by another activity or external entity.

For more information, see BABOK® Guide v3, chapter 10.13.
3.5.2 Business Data Analytics Perspective

Data flow diagrams illustrate the movement and transformation of data between entities and processes. They are used to depict and identify data that is relevant to the business data analytics initiative.

.1    Identify the Research Question

Various types of data flow diagrams are used to understand organizational data. Logical data flow diagrams are used to assess the current state of data and help depict the future state. Physical data flow diagrams identify data sources involved and how data flows between them. This knowledge helps the data analytics team frame better quality research questions.

.2    Source Data

When sourcing data, data flow diagrams are beneficial in identifying the required data sources, the data dependencies, and the best ways to bring together the data into the target repository.

.3    Analyze Data

Data flow diagrams are beneficial during data analysis, particularly when trying to identify the source of any data discrepancies. Data issues can be introduced during data migration or transformation, or even due to incorrect data mapping. These models are used to understand data anomalies and help the team identify how best to improve data quality.

.4    Interpret and Report Results

Data Flow Diagrams does not have a significant role in the Interpret and Report Results domain. However, it can be used to support the narrative being constructed.

.5    Use Results to Influence Business Decision-Making

The results of the data analytics work can be depicted or illustrated using data flow diagrams, where appropriate. They aid understanding by visualizing data flows that may be difficult to understand. For example, future state physical data flow diagrams help implement any new data sources or data processes required for the future state.

.6    Guide Organization-Level Strategy for Business Analytics

Although Data Flow Diagrams is not specifically used to guide organization- level strategy, they are used to describe the flow of data across processes that are used by the data analytics team.