Cloud computing initiatives now dominate the digital landscape, which means businesses from all industries are shifting toward a more technological approach. For business leaders striving to refine their data strategy using cloud solutions, there are two ways to go about it: data modeling and data architecture.
But what are these two approaches and how do they differ? Keep reading to learn more about these important concepts and how they can benefit your business.
Data Modeling vs. Data Architecture
In the past, anything to do with data typically fell under the purview of the IT department, but with the increased awareness of the importance of data to business, an effective data strategy is paramount to growth and competitiveness.
Data modeling and data architecture are similar and they are like two peas in a pod — both work toward the same goal of providing an efficient, high-performing, and seamless database. However, the two functions require different skill sets and are directed at specific objectives to move a data strategy forward.
What is Data Modeling?
Data modeling is the first step when building a database. It’s an abstract representation of data entities that focuses on organizing data, making it a source of information that guides the data’s rules, language, default values, and more. It’s a deep dive into the relationship between data entities, so you can have a solid understanding of your business needs and ensure data accuracy.
There are three types of data models: conceptual, logical and physical. You’ll often see a data model with entity types, attributes, naming conventions, relationships, and rules.
- Conceptual data model. This model shows a high-level view of the data that are used to support business processes. It’s usually unassociated with a specific application or database management system.
- Logical data model. This model captures details of the characteristics, attributes, and relationships among the different entities.
- Physical data model. This model is specific to the application and storage framework used for that data.
In essence, data modeling finds the best ways to represent data and ensure all the information stored is accurate, reliable, realistic, and aligned to your business concepts. What else does data modeling do?
- Assesses the information needs of business users and owners
- Collaborates with application developers and integrates business processes
- Connects different entities and builds entity-relationship diagrams
- Differentiates entities within a model
- Optimizes the model to provide measurable metrics as a reference for improving future performance
What is Data Architecture?
If data modeling is about the representation of your data, then data architecture handles the infrastructure that houses that data. As the moniker suggests, it’s home to all the building blocks you need for a data system, including tools, platforms, and other logistics necessary for storing the data. At its core, data architecture includes managing an organization's end-to-end data lifecycles that inform and drive the operational business processes and analytical decision-making.
Data architecture primarily focuses on areas involving cloud storage, hardware virtualization, hosting plans, encryption, infrastructure, and data processing. Data architecture also focuses on:
- Managing different types of data management frameworks
- Assessing operational demands and adjusting performance expectations, costing, and more;
- Establishing data standards to be implemented across all data systems
Data Modeling and Data Architecture: Complementary Approaches for Your Data Strategy
While there are clear differences between data modeling and data architecture, they do complement each other. Data modeling and data architecture work together to fill in the gaps in your data strategy. One can’t exist without the other, so it’s crucial to understand how to utilize them to build a structurally sound data system.
Data models not only provide the basis for storage, access and protection policies for businesses, they also inform the data architect's selections of platforms, tools and technologies. A solid data architecture can simplify data modeling and work hand-in-hand to create an effective data strategy for your business.
Embracing a digital shift can be daunting, especially if you don’t have the right cloud solutions and app development capabilities to seamlessly advance your business technology. Everestek can bridge the gap between your operations and digital transformation with our cloud platform solutions and other technical services. Contact us today to see how we can help accelerate your digital initiatives.