Importance of Data Modeling in Business
In today’s data-centric business environment, an organization’s information stores are one of its most important assets. Attaining a more complete understanding of the scope and organization of data resources can provide business intelligence and assist the decision-makers who are guiding the enterprise.
Role of Data Modeling
One of the techniques used to obtain deeper knowledge related to the information contained in corporate databases is data modeling.
Data Modeling Process
- Identify Entities
Identify the entities that are represented in the modeled data.
- Define Entity Properties
Determine a differentiating property for each entity.
- Draft Relationships
Develop a rough draft of entity relationships.
- Identify Data Attributes
Identify the data attributes that need to be part of the model.
- Map Attributes to Entities
Map attributes to entities, reflecting how the organization uses the data.
- Finalize and Validate
Finalize and validate the data model.
Maintaining Data Models
A data model may need to be updated and modified as elements and business requirements change. Relying on an outdated model can result in unreliable business intelligence and poor decisions based on the information it provides.
Data Modeling Versus Data Analysis
Data modeling is sometimes confused with the related technique of data analysis. Though both processes focus on maximizing the value of enterprise data resources, there are some important differences between modeling and analysis. This can be seen by looking at the main responsibilities involved with each discipline.
Primary Responsibilities of Data Modelers
Data modelers are typically responsible for the following tasks:
- Developing Data Dictionaries
Define where enterprise data assets are located and determine their value to the organization.
- Creating Data Control Procedures
Establish procedures to control how data moves between different departments.
- Standardizing Data Naming Conventions
Ensure consistent data naming conventions across the organization.
Implementing Data Security Procedures
Develop and enforce procedures to secure and protect data resources.
Role of Data Analysts in Data Management
Key Responsibilities of Data Analysts
- Collaborating with Developers
Creating algorithms with developers to facilitate access to data resources.
- Data Element Identification
Locating specific data elements using database queries.
- Database Maintenance
Maintaining the consistency of databases by removing irrelevant data.
- Data Visualization and Reporting
Using the data productively through the use of graphs, reports, and other visualizations.
Demand for Data Modelers and Data Analysts in Business
Currently, both data modelers and data analysts are in demand in the business world. You can expect a slightly higher salary if you focus on the modeling aspects of enterprise data.
Developing Data Models That Get Used
To ensure that data models are effective, they must be accessible and understandable to non-technical stakeholders who need to make informed decisions.
The Importance of Accessible Data Models
One of the primary reasons data models are created is to help non-technical decision-makers understand complex systems driving their business. Simplifying models can often make them more usable for enterprise needs.
Key Considerations for Effective Data Models
- Engage Non-Technical Teams
- The way a model is presented impacts its acceptance. Avoid overwhelming your audience with technical creation details that may not be relevant to them, as this can limit the model’s practical use.
- Focus on Results, Not Techniques
- Technical elements of model creation are often of interest to technical teams, but non-technical stakeholders are typically more concerned with the model’s demonstrated results. Emphasize outcomes rather than the methods used to build the model.
- Position the Model as a Decision-Making Guide
- Rather than presenting the model as a definitive solution, showcase it as a tool to guide viable business decisions. This approach makes it clear that the model is a flexible support, rather than an absolute answer to a problem.
Creating Data Models
Using the right software tools can go a long way toward producing informative data models that are used by the enterprise. IDERA’s ER/Studio data modeling suite of tools offers users several distinct platforms for developing data models. The focus of the individual tools may make one of them more appropriate for your business needs. Here’s a brief overview of the ER/Studio offerings.
ER/Studio Offerings
ER/Studio Business Architect
- Purpose: Focuses on creating process and conceptual models that align with business goals.
- Functionality: Models and maps relationships between people, processes, and data within the organization.
- Key Feature: Identifies sensitive data elements to assist with regulatory compliance.
ER/Studio Data Architect
- Purpose: Supports data model creation as part of a development cycle.
- Functionality: Allows the creation of entity-relationship diagrams (ERDs).
- Applications:
- Designing new databases
- Migrating and consolidating existing data resources
- Building enterprise data warehouses
ER/Studio Enterprise Team Edition
- Purpose: Facilitates collaboration in data-driven enterprise management.
- Functionality: Enables creation of data models and business glossaries.
- Key Benefit: Standardizes data usage and terminology across the organization.
Maximizing Data Value with ER/Studio
Whatever the goal of your data modeling, ER/Studio offers an advanced solution that streamlines the process of model development. These tools help organizations extract maximum value from their data assets—a critical objective for any enterprise.
