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Creating a data dictionary – our 6 stage process

Do you have a data dictionary internally? We find it’s a super useful document for governance, data consistency and continuity.

With all the confusing metrics in GA4, internal acronyms and data silos we highly recommended creating one! Here at Fabric we can appreciate this isn’t the ‘coolest’ piece of work, but it’ll save so much time in the long term!

Don’t have one? We’ve put together a bespoke 6 point process to follow!

We have a bespoke 6 stage process when creating a data dictionary for any business.

Stage 1: Define the Purpose and Scope

Clearly articulate the purpose and scope of the data dictionary. Determine the specific objectives and requirements that the data dictionary should fulfil. This includes identifying the intended audience, the types of data to be documented, and the level of detail required.

Stage 2: Identify Key Stakeholders

Identify the key stakeholders who will be involved in the creation and maintenance of the data dictionary. These stakeholders may include data analysts, database administrators, data owners, and subject matter experts. Engage with them to understand their needs and perspectives to ensure the data dictionary meets their requirements.

Stage 3: Inventory Data Elements

Conduct a thorough inventory of the data elements within the organisation or the specific project. Identify all relevant data sources, databases, tables, columns, and any other components that store or manipulate data. It is crucial to document the data elements accurately, including their names, descriptions, data types, and any constraints or relationships with other elements.

Stage 4: Define Data Attributes and Relationships

For each data element identified in the previous stage, define its attributes and relationships. Attributes may include things like data length, format, allowed values, and any other relevant characteristics. Identify relationships between data elements, such as primary keys, foreign keys, and dependencies, to capture the interconnectivity of the data.

Stage 5: Document Metadata and Business Rules

Enhance the data dictionary by documenting metadata and business rules associated with each data element. Metadata includes information such as the source of the data, data quality requirements, and data usage guidelines. Business rules define the validation and transformation rules that govern data elements. Documenting these aspects ensures a comprehensive understanding of the data and promotes consistency and accuracy in data management.

Stage 6: Establish Maintenance Procedures

Develop procedures for maintaining the data dictionary over time. Assign responsibilities to individuals or teams for updating and reviewing the dictionary regularly. Establish guidelines for incorporating changes, additions, and retirements of data elements. Ensure that the data dictionary remains up to date and relevant by incorporating it as part of the organization’s data governance framework. Remember, this bespoke process can be tailored to the specific needs and requirements of your organisation or project.