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Data modeling creates the structure your data will be stored in and exist . It defines how things are labeled and organized, which determines how your data can be utilized and, ultimately, what information or insight it will convey. When you build a database system of any kind, the complexity is driven by the number of tables and columns in the system. This, in turn, drives the price to build and maintain it. A data model helps you to reduce unnecessary columns, by constructing an optimal data structure with the fewest tables and columns and thus helps reduce system complexity and reduce cost.

 

Your data can end up being unreliable, inconsistent, and complicated when you work with numerous types of systems, databases, applications, and NoSQL data, which may result in incorrect insights or conclusions, errors or wrong or poor decision making. Most often, you will come across the need for data modeling when you have data that needs to be stored in RDBMS or NoSql like stores. Both Relational and NoSql stores specify a format in which data can be stored in them. Relational follows a tabular format where data is stored in Row-Column format, each row representing one data record and each column in the row representing a specific attribute of data. NoSql stores may store data as Key-Value pairs, or Column Family format, or as documents.

data modeling service

So, Data modeling basically means you need to analyze your data, identify the attributes it has and how they relate to other attributes, based on these two factors along with the underlying storage engine you would start providing a schematic representation to your data.

We employ mainly four different approaches to our Data modeling service aimed at different levels in the organization: 

data modeling service

 

Contextual data model:

This approach is a one-page context diagram showing all the areas of business interest (or subject areas) and how they are related to each other. These subject areas contain information about the organization data at the strategic level and will probably have senior managers looking after them.

Conceptual data model –

In this approach Business concepts are presented as entities, all named and defined in business terms aimed at tactical-level business users. The conceptual model is aimed at ensuring everything that the business users need is catered to.

Logical data model –

This approach illustrates the specific entities, attributes, and relationships involved in a business function and serves as the basis for the creation of the physical data model. This is aimed at the operational data stewards or subject matter experts (the people who know their subject areas well).

Physical data model –

This approach represents an application and database-specific implementation of a logical data model. This is the model that the database builder uses to create a database from

data modeling service

Data modeling choices and decisions will need to be made early in any software deployment and will have far and wide-reaching impact on the overall success of the project. Our Data modeling service would ensure that all errors or miscalculations are avoided, and would form a data architecture roadmap that is based on proven techniques, best practices, and accepted standards of the industry. We believe that the only way to generate a reliable model is by taking into consideration not only the data itself but the data sources, what comes in contact with the data, the systems, applications, and platforms used to process the data. 7AVP can help you get the most out of your data.

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