ATechReview

Introduction Data governance frameworks that address storage and retention but ignore master data quality are missing the factor that most directly determines enterprise AI success. Master data — customers, products, suppliers, locations, employees — is the foundational reference against which all other enterprise data is interpreted. When master data is inconsistent, duplicate, or conflicted across […]

Introduction Data governance frameworks that ignore shadow IT are governance frameworks that ignore the majority of enterprise data risk. Shadow IT — the unauthorized applications, databases, spreadsheets, and cloud services that employees use to get work done outside official channels — has grown dramatically as the pace of business has outrun the capacity of central […]

Introduction Data governance frameworks without a data catalog are like laws without a legal registry — nobody can find what they need, enforcement is impossible, and the system collapses under its own complexity. Enterprise AI programs are pushing data catalog adoption into the mainstream, because AI teams cannot build reliable models on data they cannot […]

Introduction Data governance frameworks are only as effective as their implementation — and most enterprise implementations fail not because the framework is wrong, but because it is disconnected from operational reality. Governance models that look elegant on paper become shelfware within months when they lack executive sponsorship, automated enforcement, and integration with the tools data […]