Analyzing, understanding and clarifying your data requirements are the underpinnings of good data modeling. Our experts analyze and ensure multiple source systems, databases from various Lines Of Business (LOBs) are communicating accurately, effortlessly, and efficiently. 7AVP designs data architecture roadmaps based on proven techniques, best practices, industry standards and your contributions and defined objectives. By considering the data, its sources, controls (business, compliance & technical) and the multitude of variables that come in contact with the data, systems, applications, 3rd party products and platforms, we will make sure your data modeling is efficient, accurate and complete.
Data modelling creates the structure your data will be stored and exist. It defines how things are labelled and organized, which determines how your data can be utilized and 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. As a result, the cost of developing and sustaining it rises. A data model helps you 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 be 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, Column Family format, or documents.
So, Data modelling means you need to analyze your data and identify the attributes it has and how they relate to other attributes; based on these two factors and the underlying storage engine, you would start providing a schematic representation of your data.
We employ mainly four different approaches to our Data modeling service aimed at different levels in the organization:
This approach is a one-page context diagram showing all the areas of business interest (or subject areas) and how they are related. These subject areas contain information about the organization data at the strategic level and will probably have senior managers looking after them.
Business concepts are presented as entities, with all names and definitions intended for tactical-level business users. The conceptual model aims to meet all of the requirements of the business users.
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).
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 modelling choices and decisions will need to be made early in any software deployment and will have a far and wide-reaching impact on the project’s overall success. Our Data modelling service would ensure that all errors or miscalculations are avoided and form a data architecture roadmap 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 considering the data itself and the data sources, what comes in contact with the data, and the systems, applications, and platforms used to process the data. 7AVP can help you get the most out of your data.