Data governance is the practice of establishing and enforcing policies, standards, roles, and responsibilities for the quality, security, and usage of data across an organization1. Data governance helps ensure that data is consistent, reliable, and trustworthy, and that it aligns with the organization’s strategic goals and objectives. Data governance also facilitates collaboration and communication among different stakeholders, such as business analysts, data owners, data stewards, and data consumers2. By implementing data governance, the analyst can influence how the organization strategically plans for business analytics, as data governance can help address the issues of data quality, data integration, data access, data ethics, and data value3.

Data integration, data management, and data warehousing are related but distinct concepts from data governance. Data integration is the process of combining data from different sources into a unified view4. Data management is the process of collecting, storing, organizing, and maintaining data throughout its lifecycle5. Data warehousing is the process of creating and maintaining a centralized repository of data for analytical purposes. While these practices can support business analytics, they do not necessarily influence how the organization strategically plans for business analytics, as they are more focused on the technical aspects of data rather than the organizational aspects of data.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 392: Introduction to Business Data Analytics: An Organizational View, IIBA, 2019, p. 143: Data Governance: The Definitive Guide, Tableau, 4: Data Integration: The Definitive Guide, Tableau, 5: Data Management: The Definitive Guide, Tableau, . : Data Warehousing: The Definitive Guide, Tableau, .