Risk
How to combine various signals to build a more complete picture of the local and global risk in a dynamic and scalable manner to inform business decisions? Not just today but planning for tomorrow. Aspects of uncertainty quantification and risk management, both aleatory and epistemic in nature, and from a mathematical perspective, begs important questions around how to address the ranking and reliability of various modeling techniques. Thereby, bringing transparency and accountability to formulate decision-strategies combining classical statistics, probabilistic methods, and deep machine learning.
Spatial Aggregation and Entities (Countries, Cities, Companies, Actors)
Risk Domains, Risk Classes (Sub-Classes), and Risk Indicators (Instance)
Prediction and Planning Horizons.
Some of the foundational challenges facing data and analytical community are:
- The variable volume of data
- Different update frequencies
- Inconsistent data fields
- Standardization and aggregation of data
- Serving and Cataloging
Data mesh, at the core, is founded in decentralization and distribution of responsibility to people who are closest to the data to support continuous change and scalability. The approach is very scalable and also helps in the generation and movement of data across the organization much smoothly. The data products hold data, and the application domains that consume lake data, are interconnected to form the data mesh.
Data Product consists of different layers for collection, processing, and serving of data. A catalog is maintained to understand the data to be fetched by the API and also see the knowledge graph associated with the info across other data products