-
Be the first to like this
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
Published on
Are you sure you trust the data you just used for that $10 million decision? To trust data authenticity we must first understand its lineage. However, the term "Data Lineage" itself is ambiguous since it is used in different contexts. "Business Lineage" links metadata constructs to specific terms in a business glossary. This approach is used by numerous Data Governance solutions. This approach alone comes up short, since it doesn't trace the real flow of information through an organization. "Technical Lineage" traces data's journey through different systems and data stores, providing an audit trail of the changes along the way. True "Data Lineage" combines both aspects, providing context to fully understand the data life cycle. Every step in data's journey is a potential source for introduction of error that could compromise Data Quality, and hence, business decisions. In this session, Ron Huizenga offers a comprehensive discussion of data lineage and associated Data Quality remediation approaches that are essential to build a foundation for Data Governance.
Be the first to like this
Be the first to comment