Data rules the business world of the 21st Century. Having the right information has always been an important characteristic of successful companies and merchants, but the rise of digital data has changed the game in many ways. At one time, commerce was conducted in face-to-face exchanges with shopkeepers keeping track of customer preferences through the power of their memory and interpersonal skills. Except for very rare cases, that is no longer the way things are done.
The enormous amounts of digital information available to modern corporations demand new methods with which to handle it. This wealth of data can be used by companies to better serve their customers and identify new income streams. The trick is to use the information wisely for innovative processes such as predictive analytics and data governance. A fundamental component of proper data usage involves the creation of data models.
What is a Data Model?
Simply put, data modeling is the practice of abstractly exploring data-oriented structures. The constructed models serve a variety of purposes in facilitating database and application design and development. Entity types are identified and have attributes assigned to them. Defining relationships between entities is one of the primary goals of data modeling.
There are three types of data model. They work together sequentially and introduce increased complexity as they turn entities and their relationships into a form that can be implemented in a database. The types of models are:
Conceptual data models – In this type of data model, the high-level relationships between the different entities under examination are defined. No data attribute or primary keys are specified in a conceptual model. It simply looks at data entity relationships abstractly as the first step in the process of constructing a completed model that can be used by a database or application team.
Logical data models – The role of a logical model is to provide a detailed description of the data without any concern as to how it will be physically implemented in a database. It defines all entities and their relationships. It also specifies the attributes and primary keys for each entity.
The logical model is more detailed than the conceptual model on which it is based. Primary keys and attributes are included in this model. Additionally, the relationships between entities are specified using primary and foreign keys rather than simply being stated in the conceptual model.
Physical data models – The rubber meets the road in a physical data model. It provides the most detailed description of the entities that will inhabit the database and represents how they will be built. Tables and columns are explicitly named in the physical model as well as data types and column constraints. Primary and foreign keys are used to identify relationships between tables.
A physical model may be substantially different from the logical model it was built on due to physical considerations. The model will also vary depending on the RDBMS where it will be implemented.
The complexity of the three types of data models increases as you progress from the original conceptual model to the completed physical one. Along the way the details that are required to construct the database come into view, making for a more holistic development process.
How Are Data Models Used?
The use of data models offers many benefits that directly impact a business. Among them are:
- Higher quality software products can be developed through the use of data modeling. The time spent constructing data models will repay the development team with a more well-defined scope of the problem and enable different approaches to be considered before implementation. A related advantage is the reduced development cost that accompanies this streamlined development paradigm.
- Data models are also extremely useful in understanding current data assets or those recently introduced to the environment through a merger. Knowledge of the data structures and their relationships in diverse databases may bring to light ways to integrate them that were not readily apparent without a data model.
- Another important use of data models is as the building blocks of a data governance program. The focus on regulatory compliance has made data governance a popular method with which an enterprise develops a common language around the data used across the organization. Robust data models form the foundation of a viable data governance initiative.
A Versatile Tool for Data Modeling
IDERA’s ER/Studio family of software tools provides a flexible and efficient data modeling platform. The application enables you to document the relationships between the people, data, and processes that drive your company. ER/Studio allows data resources to be cataloged and uncovers the sensitive data objects that may need more protection than they currently receive. It is a collaborative tool that enables data models to easily be shared throughout the enterprise, which is critical when implementing the common data conventions required by a governance initiative.
There are three versions of ER/Studio, ensuring the right fit for the scope of your organization’s data modeling needs. See which one is right for your team and start enjoying the advantages of a data modeling tool that will help your enterprise handle the complexities of today’s data-centric business world.