A blog about Product Management, Data Management and Self-Driven Learning
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.
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.
Effective data governance frameworks require executive support, defined roles, simplicity, user involvement, appropriate tools, and regular reviews. Organisations often overlook these aspects, leading to ineffective governance. By addressing common pitfalls, they can establish a robust framework that ensures efficient data management, compliance, and alignment with business objectives.
Many smaller organisations resist data governance due to perceived resource needs. However, effective frameworks can be established with strategic prioritisation and existing tools. Focusing on compliance, security, and quality, along with defined roles, incremental wins, training, and clear success metrics allows even resource-constrained entities to implement valuable data governance practices.
How do we get people on the same page? We need a translator! In the Data Governance world, this is the Business Glossary - read on to learn more.
Nigel D'Souza 