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Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, e.g., using data modeling:
Overview of a data-modeling context: Data model is based on Data, Data relationship, Data semantic and Data constraint. A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision.
The data modelling process. Data modelling is the process of creating a data model by applying formal data model descriptions using data modelling techniques. Data modelling is a technique for defining business requirements for a database. It is sometimes called database modelling because a data model is eventually implemented in a database. [19]
Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.
Datavault or data vault modeling is a database modeling method that is designed to provide long-term historical storage of data coming in from multiple operational systems. It is also a method of looking at historical data that deals with issues such as auditing, tracing of data, loading speed and resilience to change as well as emphasizing the ...
Dimensional models are more denormalized and optimized for data querying, while normalized models seek to eliminate data redundancies and are optimized for transaction loading and updating. The predictable framework of a dimensional model allows the database to make strong assumptions about the data which may have a positive impact on performance.
Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]
Unlike the star schema (dimensional modelling) and the classical relational model (3NF), data vault and anchor modeling are well-suited for capturing changes that occur when a source system is changed or added, but are considered advanced techniques which require experienced data architects. [2]