The Strategic Importance of Enterprise Data
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
データモデリング 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.
Categories of Data Models
Three basic categories of data models exist, which are based on their level of abstraction:
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- Also called domain models, these are the most abstract type of model.
- They provide a top-level view of the contents, organization, and business rules associated with a system.
- Created when defining business requirements.
- Typically includes entity classes and illustrates their characteristics and relationships with other entities.
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- Begin to fill in the details regarding the concepts and relationships identified in a conceptual model.
- Use formal data modeling notation, with data types and relationships further defined.
- Do not include technical system requirements.
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- The most concrete type of model, representing the schema used to store data in a database.
- Represent the final design, ready for implementation in a relational database.
- Define tables and keys representing relationships between data entities.
Data Modeling Workflow and Its Importance
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
- Assigning 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.
- Reducing complexity: Simplifies the management of data assets by identifying their location and opportunities for consolidation.
- Minimizing data misuse: Eliminates ambiguity around data assets between diverse business stakeholders.
- Saving time: Solidifies business requirements into physical designs and deployable code.
- Managing growth: Documents new data requirements through effective change control.
- Lowering costs: Merges data assets and increases consistency across all business segments.
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 gaining a competitive edge.
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.
How ER/Studio Data Architect Can Help
- Reverse-engineer data from multiple sources
Construct an enterprise data dictionary by reverse-engineering data into logical and physical models. This process reduces redundancy by uncovering opportunities for consolidation, eliminating unnecessary systems, and reducing storage and licensing costs.
- Improve data consistency with naming standards
Apply naming standards automatically to logical and physical models to maintain consistency across the enterprise.
- Enhance data lineage and traceability
Link similar concepts across models and databases to improve データ系統 and traceability, enabling connections to be traced between data sources.
- Foster collaboration with the Model Repository
Facilitate real-time collaboration across multiple data modeling projects. The Model Repository includes controls to eliminate conflicts when checking data in or out.
Why Choose ER/Studio Data Architect?
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.
