Data modeling service provider company | Data Management

Data Management

Data Modelling

Data Modelling has become a crucial strategy in the digital market. It is the process of creating a visual representation of data based on the business needs. These models act as blueprints to structure an optimized database for any organization.

At Atreya, we implement the best Data Modelling techniques to map out the connections and workflows in data for your business. We ensure proper data governance and higher application quality relevant to core business rules. Our methods will negate the risks in your data and secure smooth data management. We aim to align with your business goals by detecting trends and patterns and making predictions to help your business navigate through opportunities and challenges.

Conceptual Data Modelling
Logical Data Modelling
Physical Data Modelling
Entity-Relationship Data Modelling
Object-Oriented Data Modelling

Our Work Flow

Identify the entities and their key properties

The identification of the items, events, or concepts represented in the data set to be modelled is the first step in the data modelling process. Each entity should be logically distinct from the others while being cohesive. Each entity type can be distinguished from the others by one or more distinct qualities, known as attributes.

Identify the entities and their key properties.

After identifying the entities and their key properties, the next step is to define relationships among different entities to provide a structural modeling of data. The earliest draft of a data model will specify the nature of the relationships each entity has with the other. These relationships are usually documented via unified modeling language (UML).

Mapping

Once the entities are represented, the next step is to completely outline all the attributes of the entities. This is crucial as the mapping process will devise a model which would cite how a business can utilize the data. There are several methods of mapping the attributes, usually in form of data modeling patterns such as analysis patterns or design patterns which will depend on the goal that is intended by the stakeholders.

Normalization

Normalization is a method of structuring data models (and the databases they represent) in which numerical identifiers, known as keys, are allocated to groupings of data in order to indicate relationships between them without having to repeat the data. Normalization reduces the amount of storage space required by a database, but at the expense of query performance.

Finalize and validate the data model.

After representing the relationship between data groups, the data model can be structured. However, the process is repetitive which will need several modifications. Data Models should be repeated, processed, and refined according to the changing nature of business and what it requires. Once a concrete data model can be devised which will portray all aspects of business needs, it can be validated.