If information should be treated as a product, then it should be managed accordingly. All information should be assigned to a product manager. Information should be managed over its product life cycle in the same way as a successful physical
Is there a connection with Artificial Intelligence / Machine Learning and Data Governance? Of course, since without well-governed and accurate data, there is no possibility for an AI or ML project to succeed What Is Data Governance? Data governance is
To enable information quality in any environment, it is essential to identify the collectors, consumers, and custodians of each item of information and define their responsibilities clearly. If organizations want to treat information as a product, then they must look
Data governance has 10 key components that exist to meet the enterprise’s data management requirements. All are essential to success Data governance is the foundation of all data management programs. It is an essential discipline that supports all other data
Having an organizational understanding of the essential data used across the enterprise, through the development of a Subject Area Model and a foundational Conceptual Data Model, can improve the effectiveness of a Data Governance program Increasingly, enterprises are recognizing the
So many organizations do not have a basic component for success in the information age: an enterprise data strategy. The risks of not having and implementing a data strategy are immense. The topic of a data strategy is almost as
Although data strategy involves many best practices, some aspects to the conventional wisdom may be more effective in real life Most data management professionals have read one or a few books and articles on devising a data strategy and may
A key principle to start and sustain any successful business process is to “treat data and information as a product.” There are many issues related to information quality and information management practices that affect information quality in organizations. Every organization
Business analysis efforts should start with a conceptual data model based on use case content Successful business analysis requires understanding of business. One of the essential tools for understanding a business is a conceptual data model. Even though conceptual models
