Enterprise data comprises one of an organization’s most valuable resources. The ability to compete with market rivals is directly impacted by how a company makes use of its information assets. Using these resources wisely enables a business to streamline operations, increase productivity, and bump up the bottom line. Failure to make productive use of its data puts an enterprise at a distinct disadvantage with its competitors.
Using Data Models
Data modeling is one method organizations can use to make better use of enterprise information. Data models are visual representations of an information system used to identify connections and relationships hidden in the information. Models are meant to address business needs and should evolve as enterprise requirements change. They support business processes and assist in developing an IT strategy.
Three basic categories of data models exist which are based on their level of abstraction.
- Conceptual data models are also called domain models and are the most abstract type of model. They provide a top-level view of the contents, organization, and business rules associated with a system. This type of model is created when defining business requirements. It usually contains entity classes and illustrates their characteristics and relationships with other entities.
- Logical data models begin to fill in the details regarding the concepts and relationships identified in a conceptual model. Formal data modeling notation is usually employed as data types and relationships are further defined. No technical system requirements are included with a logical data model.
- Physical data models are the most concrete type of model and provide the schema used to store data in a database. A physical model represents the final design and can be implemented in a relational database. Tables and keys representing relationships between data entities are defined in a physical model.
Data models are usually created using an interactive process where stakeholders perform a detailed evaluation of the methods used to process and store information. A typical data modeling workflow includes the following steps.
- Identifying the entities;
- Identifying key properties of each entity;
- Finding relationships between entities;
- Mapping attributes to entities;
- Assign keys to reduce redundancy and improve performance;
- Finalizing and validating the data model.
Data modeling offers organizations a tested method with which to make the most productive use of their information resources.
The Benefits of Data Modeling
Multiple tangible organizational and economic benefits can be obtained through effective data modeling. Simply engaging in the process of developing models focuses attention on how enterprise data resources are being used. Some of the real benefits that come from data modeling are:
- Reducing the complexity of managing data assets by identifying where they are and if there are opportunities for consolidation;
- Minimizing the potential for data misuse by eliminating ambiguity around data assets between diverse business stakeholders;
- Saving time by solidifying business requirements into physical designs and deployable code;
- Managing growth by documenting new data requirements through effective change control; and
- Lowering costs by merging data assets and increasing consistency across all segments of a business.
Data models can be instrumental in helping all parts of a business coordinate data handling procedures. Efficient data modeling can be a deciding factor in identifying innovative ways to use corporate information for the benefit of the organization and gain a competitive edge.
Get the Right Tools for Data Modeling
Effective data modeling requires the process to be iterative, incremental, and collaborative. ER/Studio Data Architect offers teams an effective tool with which to create logical and physical data models to streamline the way information assets are used throughout an organization. You can model data gathered from a wide range of relational, NoSQL, and big data sources.
Here are a few of the ways ER/Studio Data Architect can help your business handle its data resources.
- Reverse-engineer data from multiple sources into logical and physical models to construct an enterprise data dictionary. This process can reduce redundancy by uncovering opportunities for consolidation to eliminate unnecessary systems and reduce storage and licensing costs.
- Improve data consistency across the enterprise with naming standards that can be applied automatically to logical and physical models.
- Enhance data lineage with links to similar concepts across models and databases, allowing connections to be traced between data sources.
- Collaboration is fostered through the tool’s Model Repository. This construct enables real-time collaboration across multiple data modeling projects and includes controls to eliminate conflicts when checking data in or out of the repository.
ER/Studio Data Architect is the right tool for creating effective data models that help an organization use its data assets constructively. Use it to create data models that contribute to your company’s well-being and competitive standing.