Why Data Modeling is an Important Skill

There are many diverse skills that database administrators and developers can bring to the table. They may be expert SQL coders and end up in high demand throughout the department when optimization is required. Maybe that new DBA is very well-versed in cloud migrations and can help the team successfully relocate their database environment. It could be that they are a data storage wizard and can find ways to maximize how disk resources are used that never occurred to the team before.

A skill that may not immediately spring to mind that can benefit anyone in the field of database support is data modeling. There are several goals of developing a data model when designing or attempting to understand a database system. They include:

  • Providing an accurate representation of all data objects contained in a database;
  • Assisting in the conceptual, logical, and physical design of a database;
  • Defining the database’s stored procedures and relational tables;
  • Understanding the relevance of data objects with an aim toward identifying missing data and eliminating redundancy.

Cloud computing has raised the importance of data modeling as the pace of migration to and from the cloud has increased. Another aspect of modern database solutions that make data modeling skills more valuable is the rise of multi-platform environments. When called upon to move a database from an on-premises data center to the cloud or from a specific platform to a different one, effective data modeling is critical to the project’s success.

Types of Database Models

Database models describe the system’s logical design and structure as well as how the data will be stored, modified,  and accessed by users. There are four general categories of data models used by database professionals.

Hierarchical model - This type of model organizes data in a tree structure with a single root linked to all other elements. Child nodes on the tree only have one parent node in a hierarchical model. These models are useful for describing situations where a one-to-many relationship exists between different data types.

Network model - A network model is a graph-like extension of a hierarchical model in which data elements can have more than one parent node. It is a useful model for mapping many-to-many data relationships.

Entity-relationship model - The defining aspect of E-R models is the division of data elements into objects and their characteristics. Data objects are referred to as entities that have specific attributes. Entities are related by relationships. One of the uses of entity-relationship models is to pictorially represent relationships, making them easier for non-technical stakeholders to understand. These models are often used in database design in conjunction with relational models.

Relational model - Two-dimensional tables are used to organize information in the relational model with relationships represented by storing a common data field. It is a widely-used contract in the database field that lends itself to being used with structured data.

Data Modeling Concepts

Here are some concepts that need to be understood to effectively work with data models.

  • Entity types represent collections of similar physical or abstract objects. An entity type simply represents the data with no regard to the object’s behavior.

  • Attributes are associated with entities and each entity must have at least one attribute. Developers usually represent attributes as columns in tables and multiple columns are often used to provide greater detail and control over the data resources.

  • Naming conventions are essential for understanding and modifying code. Logical and physical data models often have different naming conventions that are in line with the purpose of the respective model.

  • The relationships between entities are an essential component of data models and understanding them is a critical skill for modelers.

  • Normalization organizes the data in a model to develop more cohesion between entity types. Reducing data redundancy is an important step in the normalization process.

Tools for Data Modeling

IDERA’s ER/Studio Data Architect provides your team with a tool for developing and managing enterprise data models. The application supports a wide range of database platforms and can be instrumental in performing tasks such as standardizing data dictionaries and developing naming standards. Graphical models can be constructed for forward and reverse database engineering. The tool provides the capability to automate model validation to identify issues such as circular relationships or missing object definitions.

An IDERA whitepaper is available that provides more information about improving your data modeling skills. DBAs who wish to add to their knowledge of data modeling should give it a read. They’re likely to learn something that will contribute to more efficient database design and help them cope with the challenges of working in multi-platform environments.