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Data Modeling

Empower Data-Driven Decisions: Data Modeling by Sanpurple

In this information era, proper data management has been the key determinant of the total success of every organized venture. Sanpurple is your trusted companion that provides exemplary data modeling solutions to assist you in arranging, structuring, and comprehending your data to enable informed decision making towards the achievement of your strategic goals.


Why go for Sanpurple if you need a data
modeling solution?


Experienced Data Modeling Team

Our team of data-savvy professionals has the required expertise in building and establishing data models revolving about your specific business objectives.


Agile Data Modeling Approach

We implement an agile methodology which allows us to maintain flexibility and continuous improvement throughout the modeling process all the time.


Enhanced Data Governance

Our data models focus on data integrity and consistency while forming a strong foundation to which data governance practices can be built.


Improved Data Quality

The optimized data structures not only minimize errors and redundancies but also provides the best quality data for enhanced decision making.


Actionable Business Insights

We assist you in transforming the raw data into actionable insights that give you an ability to optimize operations, find trends, and open up new opportunities for growth.

Our Data Modeling Process:

We’ll set you up with an initial meeting where we can go over your business goals, data sources and data usage milestones.

Within the proposed work, we will support you in a co-creative process where a conceptual model of the central entities, attributes and the corresponding relationships will be crafted.

The conceptual model would be translated into a more detailed logical model which specifies a data structure and constraints.

We can propose and bring into life a structured data model that logically maps the logical concept to a particular database server type.

We will validate the data model to make sure it corresponds to your requirements and refine it if necessary.

We provide continuous assistance to keep your data model updated and also enhance the data architecture in response to evolving business requirements.

Benefits of Data Modeling with Sanpurple:Benefits of Data Modeling with Sanpurple:

Improved Data Accessibility & Usability:

An adequately built data model will make your information more accessible to be utilized for your data analytics and reporting.

Enhanced Data Security:

Data modeling safeguards data security through formulation of clear data ownership and data access rules.

Reduced Data Silos:

Data models get rid of data silos through a centralized view of your data, making it easy for data integration.

Optimized Database Performance:

Good data modeling helps you build a database that generates the required information rapidly because of structure efficiency.

Data-Driven Decision Making:

Data models empower you to use data insights to make informed decisions all over your organization.

Ready to take your power of data and make sound management decisions?

Contact Sanpurple today. Through our collaboration, we will build a data model that covers you needs and at the same time boosts your business.



The data modeling services involve the creation and maintenance of data models which are the structures that represent the relationships, rules as well as the structures governing the data of the organization. They are used to build, tune, and analyze databases according to the organizational business needs and goals.

The primary objective of data modeling is to consequently provide a detailed skeleton for the comprehensive organization and interpretation of the data resources of an organization. Through providing logical and physical symbols, data modeling aids stakeholders' communication, data consistency checking, decision-making, and database optimization processes.

The four main types of data modeling are:
  • Conceptual Data Modeling: Develops business logic concepts and relationships on a high level of abstraction, bypassing technical details of implementation.
  • Logical Data Modeling: Defines entities, attributes, and relationships using formal conceptual models that make databases building and programming easier.
  • Physical Data Modeling: Indicates how data models are achieved by the specific DBMS through tables, columns, indexes, and constraints.
  • Dimensional Data Modeling: Tailored to data warehouses and analytics focusing on organizing data into facts (measurable events) and dimensions (describable properties).

The data modeling life cycle comprises several stages, including:
  • Requirements Gathering: Knowing which business applications will use the data and what kind of information is needed.
  • Conceptual Modeling: Modeling data to the upper levels and to represent business concepts.
  • Logical Modeling: Designing high-dimensional databases and relationships.
  • Physical Modeling: The process of data modeling within a specific database ecosystem.
  • Validation and Iteration: Evaluating and restructuring data models through feedback and testing.
  • Maintenance: Managing changes and updates to data models, which are subject to change, as and when business requirements are changing.

SQL Data Modeling entails designing a relational structure of a database using SQL commands which are used to define tables with columns, specify constraints and relationships. SQL based data modeling enables developers to create and modify database objects, carry out data integrity rules and tune database performance so that data storage and retrieval can be done fast.