- Why data management?
part 1: Thinking Globally, The meaning of data to people
- What is Information?
- How do we know what we know in data modelling
- Patterns of misunderstanding
- Identity and persistence
- Models of meaning
part 2: Thinking Globally, The representation of data to machines
- Simulating meaning and understanding
- Equivalence of representation
- Syntax and its role in data management
- Models and meta models
- Model transformations and mapping
- Mapping versus deriving existence
- Generating machine readable artifacts
part 3: Thinking Globally, Managing Data and Knowledge at scale
- Data architecture, solution architecture and business architecture
- The consequence of decentralization on ownership, mandates and cooperation
- How to make a controlled vocabulary for an organization
- How to make a knowledge graph based on ontologies for an organization
- Planting data seeds
part 4: Acting Locally, how Teams Work within a Data Architecture
- Introduction
- Tackling Usecases
part4 : expert knowledge
- A closer look at meta-models
- A closer look at transformations
Papers
How do we know what we know in data modelling
There are many frameworks for Data Architecture, like DM-BOK, FAIR and many others. The prerequisite to making any of them work for your organization is to understand your organizations data. In order to do this, we use semantic models see here. But how can we be sure that the terms we define in them actually represent real, meaningful things?
Lets start by setting an important context for the field of data architecture: There is no definitive way to be certain about correctness, the ‘true-ness’, of the semantic models and ontologies that we make.
Papers
Our Perspective On Data Achitecture
We would argue that perhaps the most important reason existence of the capability of data architecture, is to insure that the data and information that we use to base our decisions on is first and foremost there and then actually represents reality.
Whether you are publishing scientific- or open data or just trying to get more value out of your organizations confidential data, we believe the FAIR principles are the most concrete proposal for solving the most common data issues.