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. One of the techniques used to obtain deeper knowledge related to the information contained in corporate databases is data modeling.
A data model is created by using a multi-step process. Generally, the creation of a data model uses the following framework.
- Identify the entities that are represented in the modeled data;
- Determine a differentiating property for each entity;
- Develop a rough draft of entity relationships;
- Identify the data attributes that need to be part of the model;
- Map attributes to entities reflecting how the organization uses the data;
- Finalize and validate the data model.
A data model may need to be updated and modified as the 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 Relying on an outdated model can result in unreliable business intelligence and poor decisions based on the information it provides.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.
The primary tasks assigned to data modelers include:
- Developing data dictionaries to define where enterprise data assets are located and determining their value to the organization;
- Creating procedures that help control how data moves between different departments;
- Standardizing data naming conventions;
- Implementing procedures to secure and protect data resources.
Data analysts are primarily involved in these tasks:
- Creating algorithms with developers to facilitate access to data resources;
- Locating specific data elements using database queries;
- Maintaining the consistency of databases by removing irrelevant data;
- Using the data productively through the use of graphs, reports, and other visualizations.
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
One of the most important reasons that data models are developed is to assist non-technical decision-makers in understanding the complexities of the systems powering their business. Toward that end, it is necessary to make the model accessible to the associated stakeholders. Sometimes it is more beneficial to provide a simpler model that directly addresses the needs of the enterprise. The following are some points to keep in mind when developing a data model.
- Engage non-technical teams – The way you discuss the model with other teams can have a big impact on its acceptance. Details concerning the creation of the model may be irrelevant to your audience and emphasizing them can hinder it from being used productively.
- Focus on the model’s results – The underlying technology or techniques used to create the model may be extremely interesting to the technical members of your audience. This may not be the case with the individuals who will be using the model. When discussing the model with non-technical stakeholders, stress the results demonstrated by the model instead of the devices used in its creation.
- Use the model as a guide – Avoid presenting the model as a fully-developed answer to a business problem. Emphasize its usefulness as a guide to help the organization make viable decisions.
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 Business Architect – The focus of this application is the creation of process and conceptual models that align with business goals. You can model and map the relationships between the people, processes, and data in your organization. You can identify sensitive data elements in your environment to help comply with regulatory standards.
ER/Studio Data Architect – This tool helps your team build data models as part of a development cycle by creating entity-relationship diagrams (ERDs). The models can be used to design new databases, migrate and consolidate existing data resources, and create enterprise data warehouses.
ER/Studio Enterprise Team Edition – Collaboration is a key component of data-driven enterprise management. This version of ER/Studio allows the creation of data models and glossaries of business terms that can be used throughout the organization to keep everyone on the same page and using data resources in the same way.
Whatever the goal of your data modeling, ER/Studio offers an advanced solution that streamlines the process of model development. These tools will help your organization squeeze the maximum value out of its data assets which is what everyone is striving to achieve.