High-level architecture diagrams offer crucial insights into data flow and connectivity within organisations. They enhance clarity, facilitate collaboration, and promote scalability. Adopting standards like BPMN can streamline processes, while tools such as Microsoft Visio aid in creating these diagrams. Regular updates ensure accuracy, supporting effective data governance strategies.
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How Should We Classify Data – A Quick Introduction to Data Classification
This post emphasises the importance of data classification within Data Governance, highlighting four potential classification levels: Public, Internal, Confidential, and Restricted. It stresses contextualising classification based on industry standards, steps to classify data, and the necessity of inventorying assets. Automation tools like Microsoft Purview facilitate consistent data management throughout its lifecycle.
Metadata: Unlocking the Power of Data Context
Metadata, or "data about data," is essential for managing organisational information, categorised into technical, business, and operational types. It enhances data discoverability, usability, and compliance, while ensuring alignment with business objectives. Effective management involves tools and stakeholder engagement, promoting transparency and efficiency across data systems and governance frameworks.
A Data Dictionary: The Backbone of Field Standards
A data dictionary is essential for ensuring consistency and accuracy in data management by providing a structured inventory of data elements, including their definitions and formats. It prevents misunderstandings across teams and complements codebooks, which focus on coding systems for statistical analysis. Together, they enhance data clarity and collaboration within and between organisations.
Aligning Data Governance with Privacy Laws: A Compliance Checklist
In a data-driven environment, aligning data governance with privacy laws is essential to mitigate risks and protect user privacy. Organizations must understand applicable regulations, classify data by its sensitivity, practice data minimisation, strengthen security measures, create transparent sharing policies, conduct regular audits, and train employees to ensure compliance and safeguard sensitive information.