Data Governance: Strategies for Data Quality and Compliance

所在平台: Udemy

课程主页: https://www.udemy.com/course/data-governance/

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课程名称:数据治理:数据质量与合规的策略 课程概述:欢迎参加Uplatz的“数据治理:数据质量与合规的策略”课程。数据治理是一个框架,规定了在组织内如何管理、访问和使用数据,以确保其准确性、安全性和合规性。它涉及设定政策、角色和责任,以规范数据在整个生命周期中的处理方式,包括创建、存储、使用和删除。其主要目标是确保数据的可靠性、一致性、安全性以及在法规要求和商业需求中的适当使用。数据治理对于确保组织能够信任并有效使用其数据,同时维护合规性并降低风险至关重要。它通过定义清晰的流程、角色和技术来管理数据的质量、安全性和合规性。 课程内容: 1. 数据治理的运作机制: - **建立治理政策**:创建定义数据管理和保护方式的治理政策。 - **指派角色与责任**:识别负责不同数据治理方面的利益相关者。 - **实施数据治理工具**:采用数据目录、元数据管理和数据质量工具等技术。 - **监控与执行合规性**:持续监控数据流程以确保遵循治理政策。 - **迭代与改进**:定期评估治理实践,调整政策以适应新的商业、法律或技术发展。 2. 数据治理的关键特性: - 数据质量管理 - 数据管理责任 - 数据安全与隐私 - 数据血统与元数据管理 - 访问控制 - 合规与监管对齐 - 数据目录管理 - 关键绩效指标与度量 - 数据生命周期管理 - 变更管理 3. 课程大纲包括: - 理解数据治理的定义和目的 - 关键组成部分 - 参与的利益相关者 - 数据治理框架及实施步骤 - 数据治理的挑战与收益 - 各种数据治理框架(如DAMA-DMBOK、CMMI、IBM数据治理框架) - 数据治理结构的建立、角色与责任 - 数据隐私和合规性 - 主要法规 - 数据治理工具与最近趋势 通过本课程,您将获得有关如何有效实施数据治理策略以确保数据质量和合规性的深入理解。

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A warm welcome to the Data Governance: Strategies for Data Quality and Compliance course by Uplatz.Data governance is the framework that outlines how data is managed, accessed, and used within an organization to ensure its accuracy, security, and compliance. It involves setting policies, roles, and responsibilities that dictate how data is handled throughout its lifecycle, from creation and storage to usage and deletion. The primary goal is to ensure that data is reliable, consistent, secure, and used appropriately, especially in the context of regulatory requirements and business needs.Data governance is crucial for ensuring that organizations can trust and effectively use their data while maintaining compliance and minimizing risk. It works by defining clear processes, roles, and technologies that manage data's quality, security, and compliance throughout its lifecycle.How Data Governance WorksEstablish Governance Policies: The first step is creating governance policies that define how data should be managed and protected. These policies outline rules for data quality, data access, data usage, and data security.Assign Roles and Responsibilities: Organizations must identify stakeholders responsible for various aspects of data governance. These roles include Data Stewards (responsible for day-to-day data management), Data Owners (accountable for specific data sets), and Data Governance Committees (which set policies and oversee the program).Implement Data Governance Tools: Technologies like data cataloging, metadata management, and data quality tools are implemented to track data lineage, maintain data consistency, and monitor data usage.Monitor and Enforce Compliance: Organizations continuously monitor data processes to ensure adherence to governance policies. They use metrics and key performance indicators (KPIs) to evaluate data quality, compliance, and security.Iterate and Improve: Data governance is an ongoing process. Organizations must regularly assess their governance practices, adjust policies, and ensure they adapt to new business, legal, or technological developments.Key Features of Data GovernanceData Quality Management: Ensuring the accuracy, completeness, and consistency of data across the organization.Data Stewardship: Assigning individuals or teams to manage and ensure the quality and security of data throughout its lifecycle.Data Security and Privacy: Protecting sensitive data and ensuring that data usage complies with privacy laws (e.g., GDPR, CCPA).Data Lineage and Metadata Management: Tracking data's origin, transformations, and its journey across systems to ensure transparency and traceability.Access Control: Implementing role-based access policies to ensure only authorized users can access or manipulate certain data sets.Compliance and Regulatory Alignment: Ensuring that data governance practices adhere to industry regulations and standards (e.g., HIPAA, SOX, Basel III).Data Cataloging: Creating a centralized repository of data assets that provides an inventory of available data, its location, and its governance rules.KPIs and Metrics: Establishing performance indicators to measure the success of governance initiatives, such as data quality scores, policy compliance rates, and data security metrics.Data Lifecycle Management: Defining processes for how data is stored, maintained, archived, or deleted at the end of its useful life.Change Management: Managing changes in data policies, technologies, or business processes while ensuring minimal disruption and consistent governance.Data Governance - Course CurriculumUnderstanding Data Governance Definition and PurposeKey componentsStakeholders InvolvedData Governance FrameworksImplementation StepsChallenges and BenefitsWhy Data Governance is requiredData Governance frameworksDefinition and PurposeCore componentsPolicies, standards and processesTools and TechnologiesDAMA-DMBOK FrameworkCMMI FrameworkIBM Data Governance FrameworkEstablishing DG structures, Roles & responsibilitiesData StewardsData OwnersData UsersOrganizational Levels of data GovernanceData Governance CouncilData ArchitectData Quality AnalystCompliance officerBusiness usersManagement TeamData Governance PlanComponentsData Stewardship and OwnershipRelationship between Data Stewardship and OwnershipData privacy and ComplianceKey RegulationsTools and TechnologiesRecent TrendsData CatalogData quality toolsGovernance PlatformsData Security and privacy toolsMaster data managementLineage toolsCollaboration and workflow management toolsImplementing DG Policies and proceduresMetrics and KPIs

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