Data Governance: Building Trust in Your Data
Data governance decides whether data can be trusted. That in turn decides whether reports, automated processes and AI models can be trusted too. In this category, we treat data governance as a business capability rather than a compliance exercise. The articles cover defining ownership and responsibilities, documenting data with a data catalog and business glossary, making data lineage transparent, and meeting regulatory requirements such as the EU AI Act, BCBS 239 and GDPR.
Data governance with SoftProject
With dataspot. and MyDataCatalogue, SoftProject offers business-oriented data governance solutions that bring business departments and IT together in one place. The data catalog, business glossary, data lineage and responsibilities are managed collaboratively, so everyone knows what data means, where it comes from and who is accountable for it. MyDataCatalogue complements the portfolio for organizations focused on cataloging their data assets. Combined with X4 BPMS and Phoenix, governance rules don’t stay on paper; they are built directly into integration flows and business processes.
Single Customer View: How to Build One Golden Record for Every Customer
Most organisations do not have a customer data problem. They have a customer data agreement problem. The CRM holds one version of the account, the e-commerce platform another, the service...
Data Hub Models for Master Data Management (MDM): One Architecture for Every Organisation
Master data creates real technical and organisational challenges. Choosing the right data hub is how you solve them — here are the four models and how to pick one. Building...
Different types of data hub for MDM (Master Data Management)
Far from being a trivial matter for a business, implementing a single master data repository poses significant technical and organisational challenges. The most common of these include: The difficulty in...
Frequently asked questions
What is data governance?
Data governance is the framework of roles, responsibilities, rules and processes that ensures data is reliable, understood and used correctly throughout an organization. It defines who owns which data, what business terms mean and which quality and compliance requirements apply. Good data governance turns data into a trusted asset for reporting, automation and AI.
What is the difference between data governance and data management?
Data governance sets the rules: responsibilities, definitions, policies and quality standards. Data management covers the operational work of storing, integrating, processing and maintaining data according to those rules. Put simply, governance decides what should happen with data and who is accountable, while data management makes it happen in practice.
Why do organizations need a data catalog?
A data catalog provides a central inventory of an organization’s data assets, including descriptions, owners, sources and usage context. Employees can find the data they need, understand what it means and judge whether they can trust it. Without a catalog, knowledge about data stays spread across individual people and departments, which slows down projects and leads to inconsistent results.
What is data lineage and why does it matter?
Data lineage shows where data comes from, which systems and transformations it passes through, and where it is used. This transparency is essential for troubleshooting, impact analysis before system changes, and regulatory requirements such as BCBS 239 or the EU AI Act. In dataspot., lineage is documented from a business perspective, so it is understandable beyond the IT department.
How do you get started with data governance?
Start small and focus on value. Pick a few business-critical data domains, define clear owners and document the most important terms in a business glossary. Then expand step by step to more domains, quality rules and lineage. Trying to govern all data at once rarely works. A pragmatic approach makes early results visible and builds acceptance across the organization.