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Data governance

As explained in Architecture Modernization: Socio-technical alignment of software, strategy, and structure

Data governance is the system of policies, responsibilities, and controls for managing data quality, security, consistency, and appropriate use.

Data governance helps an organization handle data consistently while responding to business needs and regulatory obligations. It must remain adaptable: privacy requirements and expectations about financial-data retention differ across countries and can change. section 14.1.2

Autonomy with sensitive payroll data

As PayFit grew, it sought to give teams more autonomy and reduce dependencies by aligning them with business domains. Payroll data made that shift more delicate: decentralized decisions still needed a holistic approach to governance and consistency. section 17.1.1

Domain discovery offers a starting point for making consistency concerns visible. Teams can map domain concepts and boundaries, then collect the different meanings attached to important terms before trying to align on a model. Such workshops expose misunderstandings; they do not, by themselves, settle every governance decision. section 17.1.1

How Architecture Modernization: Socio-technical alignment of software, strategy, and structure covers it

How deep it goes
Frames governance as holistic and adaptable, without specifying policies, responsibilities, or technical controls.
Examples
Uses national financial-data retention differences and a growing HR-tech company's sensitive payroll data.
What you can do after
Evaluate/understand why greater team autonomy calls for attention to data consistency and governance.
How it connects
Connects governance to privacy regulations, domain modeling, organizational autonomy, and decentralized decision-making.

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This explainer was generated by Manning's knowledge graph from the text of Architecture Modernization: Socio-technical alignment of software, strategy, and structure. The section links point to the parts of the book it draws on.