Data Modeling
We understand the power of data. In today’s fast-paced business landscape, data is the driving force behind informed decision-making, process optimization and strategic growth. Our mission is to help organizations discover the full potential of their data through advanced data modeling solutions.
Our Data Modeling Services
Our data modeling solutions are not just about creating diagrams and schemas. They’re about empowering your organization with data-driven insights, streamlining processes, reducing costs, and driving growth. Whether you’re in finance, healthcare, e-commerce, or any other industry, we have the expertise to transform your data into a strategic asset.
Conceptual Data Modeling
We help you define the data structures needed to support your business objectives, ensuring alignment with your goals.
Logical Data Modelin
Our experts design data models that are independent of any specific database management system, allowing for greater flexibility and scalability.
Physical Data Modeling
We translate logical models into physical databases, optimizing performance, and ensuring data integrity.
Dimensional Modeling
For those seeking to build data warehouses and analytical solutions, our dimensional modeling expertise can help you gain valuable insights.
Data Model Optimization
We assess and enhance existing data models to improve efficiency and accuracy.
Physical Data Modeling
We translate logical models into physical databases, optimizing performance, and ensuring data integrity.
How can you benefit from Data Modelling?
Improved Understanding of Data
- Data modeling helps stakeholders gain a clear understanding of the structure, meaning, and relationships within the data. This understanding facilitates better decision-making and problem-solving.
Enhanced Data Quality
- By defining entities, attributes, and relationships, data modeling helps ensure data accuracy, consistency, and completeness. It enables data validation and verification processes to maintain high-quality data.
Facilitates Database Design
- Data modeling serves as a blueprint for database design and development. It provides a visual representation of the database structure, making it easier for database administrators and developers to implement and maintain databases.
Supports System Integration
- Data modeling helps identify common data elements and standardizes data definitions across different systems and applications. This facilitates data integration efforts and enables seamless data exchange between systems.
How it Works
01. Understanding Requirements
- Gather requirements from stakeholders
- Identify the purpose and scope of the data model
- Determine what data needs to be captured and how it will be used
03. Choosing Data Model Type
- Consider the requirements and complexity of the system
- Evaluate different data model types (e.g., relational, hierarchical, network, object-oriented).
- Choose the most suitable data model type for the project.
05. Validation and Verification
- Validate the data model to ensure it accurately represents the requirements.
- Verify that the data model meets the intended purpose and functionality
- Perform quality checks to ensure the integrity and consistency of the data model.
02. Establishing Relationships
- Identify how entities are connected or related to each other
- Define the cardinality of relationships (one-to-one, one-to-many, many-to-many)
- Determine the nature of relationships (e.g., aggregation, composition).
04. Creating the Data Model
- Use a modeling language such as Entity-Relationship Diagrams (ERD) or Unified Modeling Language (UML).
- Create visual representations of entities, attributes, and relationships
- Ensure the data model accurately reflects the requirements and relationships
01. Strategy
During this phase we will explore an existing ecosystem including:
- Clarification of the stakeholders’ vision and objectives
- Reviewing the environment and existing systems
- Measuring current capability and scalability
- Creating a risk management framework.
02. Discovery phase
We offer a Discovery Phase as a service to help you validate your idea, choose a tech stack, estimate ROI, and build a feasible prototype.
- Defining client’s business needs
- Analysis of existing reports and ML models
- Review and documentation of existing data sources, and existing data connectors
- Estimation of the budget for the project and team composition.
- Data quality analysis
- Detailed analysis of metrics
- Logical design of data warehouse
- Logical design of ETL architecture
- Proposing several solutions with different tech stacks
- Building a prototype.
03. Development
Based on your needs and chosen tech stack, our experts will build a robust data warehouse. Some of the steps will include:
- Physical design of databases and schemas
- Integration of data sources
- Development of ETL routines
- Data profiling
- Loading historical data into data warehouse
- Implementing data quality checks
- Data automation tuning
- Achieving DWH stability.
04. Ongoing support
We will help you build a dedicated team for ongoing support of the data warehouse. Overall, the support will cover:
- Fixing issues within the SLA
- Lowering storage and processing costs
- Small enhancement
- Supervision of systems
- Ongoing cost optimization
- Product support and fault elimination.
Ready to Get Started
Ready to optimize your data warehouse strategy? Contact us today to discuss your specific data warehouse consulting needs. Let us help you build and manage a data warehouse that empowers your organization with data-driven insights, efficiency, and competitiveness. Your data warehouse, our expertise.
