3. Techniques
3.4 Data Dictionary
Guide to Business Data Analytics
3.4.1 Purpose
A data dictionary is used to standardize a definition of a data element and enable a common interpretation of data elements within a single data source or across multiple data sources.
For more information, see BABOK® Guide v3, chapter 10.12.
A data dictionary is used to standardize a definition of a data element and enable a common interpretation of data elements within a single data source or across multiple data sources.
For more information, see BABOK® Guide v3, chapter 10.12.
3.4.2 Business Data Analytics Perspective
The data dictionary is used to collate and standardize references to data elements across initiatives or at an organizational level. In a business data analytics initiative, the data dictionary can also be expanded to identify and locate data elements across organizational systems. To achieve this, the primitive elements in a data dictionary including name, aliases, value or meaning, and description can be expanded to also track business rules and data validation, both for derived data and transformed elements.
A comprehensive data dictionary serves as a source of truth for the team in determining the business data analytics approach and subsequent tasks.
.1 Identify the Research Question
When planning the business data analytics approach, it is important to determine what relevant data currently exists. The data dictionary is used to understand and select key data elements, either to frame good quality questions or to identify data to be explored through the questions. The data dictionary provides all stakeholders with a shared understanding of data elements, their use, and their application.
.2 Source Data
When sourcing data, the data dictionary helps to identify the required data sources, study key data elements, and perform data mapping, migrations, and transformations. For example, queries used to source data may use data elements as conditional filters, and their data type, format, and size are essential to understand. The data dictionary provides a deeper understanding of data elements and how they may be related to other data elements, and this helps identify a comprehensive set of data sources.
.3 Analyze Data
The data dictionary can be used in conjunction with other models, such as entity relationship diagrams, to understand how to join data between different data sources and perform the necessary analysis.
.4 Interpret and Report Results
Data Dictionary does not have a significant role in the Interpret and Report Results domain. However, important outcomes of the analytics effort may result in new elements being defined and these can be added to the data dictionary.
.5 Use Results to Influence Business Decision-Making
Data Dictionary does not have a significant role in the Use Results to Influence Business Decision-Making domain. However, the team may reference the existing data dictionary to ensure results are being communicated in a standardized and meaningful way.
.6 Guide Organization-Level Strategy for Business Analytics
Data Dictionary does not have a significant role in the Guide Organization- Level Strategy for Business Analytics domain. However, the analytics team can refer to the data dictionary and help maintain its relevance as new terms are developed.
The data dictionary is used to collate and standardize references to data elements across initiatives or at an organizational level. In a business data analytics initiative, the data dictionary can also be expanded to identify and locate data elements across organizational systems. To achieve this, the primitive elements in a data dictionary including name, aliases, value or meaning, and description can be expanded to also track business rules and data validation, both for derived data and transformed elements.
A comprehensive data dictionary serves as a source of truth for the team in determining the business data analytics approach and subsequent tasks.
.1 Identify the Research Question
When planning the business data analytics approach, it is important to determine what relevant data currently exists. The data dictionary is used to understand and select key data elements, either to frame good quality questions or to identify data to be explored through the questions. The data dictionary provides all stakeholders with a shared understanding of data elements, their use, and their application.
.2 Source Data
When sourcing data, the data dictionary helps to identify the required data sources, study key data elements, and perform data mapping, migrations, and transformations. For example, queries used to source data may use data elements as conditional filters, and their data type, format, and size are essential to understand. The data dictionary provides a deeper understanding of data elements and how they may be related to other data elements, and this helps identify a comprehensive set of data sources.
.3 Analyze Data
The data dictionary can be used in conjunction with other models, such as entity relationship diagrams, to understand how to join data between different data sources and perform the necessary analysis.
.4 Interpret and Report Results
Data Dictionary does not have a significant role in the Interpret and Report Results domain. However, important outcomes of the analytics effort may result in new elements being defined and these can be added to the data dictionary.
.5 Use Results to Influence Business Decision-Making
Data Dictionary does not have a significant role in the Use Results to Influence Business Decision-Making domain. However, the team may reference the existing data dictionary to ensure results are being communicated in a standardized and meaningful way.
.6 Guide Organization-Level Strategy for Business Analytics
Data Dictionary does not have a significant role in the Guide Organization- Level Strategy for Business Analytics domain. However, the analytics team can refer to the data dictionary and help maintain its relevance as new terms are developed.
