The business environment has become very dynamic, uncertain, and volatile. Many companies understand the role of data in getting a competitive advantage. Many business people declare that “data is their company’s asset,” and they need “to get value from it” by “becoming data-driven.” Many established and new data management capabilities should assist in reaching these goals. Knowledge graphs, metadata management, and data lineage are examples of these new capabilities that have been actively discussed and developed. There are some challenges associated with the implementation of these capabilities:
- Definitions of these capabilities are ambiguous and depend on the context.
- These capabilities intersect each other to a great extent and have many dependencies.
- Their implementation is time- and resource-consuming.
This series of articles aim to:
- Illustrate relationships between key concepts that form the core of these capabilities: data, metadata, information, and knowledge
- Analyze and compare knowledge graphs (KG), data lineage (DL), and metadata management (MM) by comparing:
- Definitions and structures of each capability
- Business drivers that motivate companies to establish them
- Architecture and technology
- Use cases
- Demonstrate differences and similarities between these three capabilities