Big Data Analytics Governance aims to implement policies, processes, and technologies to protect sensitive data, maintain data integrity, and comply with regulatory requirements.

COBIT 5 (Control Objectives for Information and Related Technologies) offers a comprehensive framework for Big Data Governance, addressing data security, risk management, and compliance.

Data lineage provides a historical record of data movement, allowing organizations to trace the origin, transformation, and usage of data assets.

The General Data Protection Regulation (GDPR) mandates organizations to protect personal data, provide individuals with control over their data, and ensure compliance with data privacy laws.

Data retention policies establish guidelines for determining how long data should be retained, based on factors such as legal requirements, business value, and data sensitivity.

Tokenization replaces sensitive data with unique identifiers (tokens), preserving data integrity while protecting the confidentiality of the original data.

The CDO is responsible for driving data-driven decision-making, establishing data governance policies, and ensuring compliance with regulatory requirements.

Basel II is an international regulatory framework that sets forth requirements for data governance and risk management in the banking sector.

Data classification involves categorizing data into different levels of sensitivity, such as public, internal, confidential, or highly confidential, to determine appropriate security measures and access controls.

Advanced Encryption Standard (AES) is a widely adopted symmetric encryption algorithm used to protect data at rest and in transit.

Metadata provides descriptive information about data, enabling organizations to understand the context, usage, and relationships between data assets.

The Health Insurance Portability and Accountability Act (HIPAA) sets forth data privacy and security standards for healthcare organizations to protect patient health information.

Data governance councils are responsible for setting the strategic direction, reviewing data governance policies, and monitoring compliance with data governance requirements.

Pseudonymization involves replacing personal identifiers with artificial identifiers, allowing data to be used for analysis while protecting the identity of individuals.

Data quality management aims to ensure that data is reliable, trustworthy, and fit for its intended purpose, enabling organizations to make informed decisions based on accurate information.