Understanding the benefits of data modeling is more important than ever.
Data modeling is the process of creating a data model to communicate data requirements, documenting data structures and entity types.
It serves as a visual guide in designing and deploying databases with high-quality data sources as part of application development.
Data modeling has been used for decades to help organizations define and categorize their data, establishing standards and rules so it can be consumed and then used by information systems. Today, data modeling is a cost-effective and efficient way to manage and govern massive volumes of data, aligning data assets with the business functions they serve.
You can automatically generate data models and database designs to increase efficiency and reduce errors to make the lives or your data modelers – and other stakeholders – much more productive.
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A data model is a visual representation of data elements and the relationships between them. Data models help business and technical resources collaborate in the design of information systems and the databases that power them. They show what data is required and how it needs to be structured to support various business processes.
There are three types of data models: conceptual, logical and physical, and each has its own purpose defined primarily by the level of operational detail. With each stage of data modeling, the data model becomes more information- and context-rich.
A conceptual data model is a rough draft, containing the relevant concepts or entities and the relationships between them.
A logical data model, also referred to as information modeling, is the second stage of data modeling. It is a graphical representation of the information requirements for a given business area.
A physical data model provides the database-specific context, elaborating on the conceptual and logical models produced prior. Accordingly, physical data models are often treated as the blueprint for a proposed database.
Although data modeling isn’t new, it is becoming an increasingly important practice because of the large amount of data organizations are tasked with processing and storing.
A good analogy is that of a house and its architect. The architect designs a house with with the end user/occupant in mind. It has to be constructed with right functionality in the right places.
So think of a table of data as a room in the house. But in the context of data management, the house doesn’t have just 10 rooms – it has 10,000, each with varying degrees of interconnectivity and importance to the organization.
At this scale, oversight can be catastrophic. Therefore, the visual representation provided by a data model gives organizations the confidence to design their proposed systems and take them live.
Data modeling is a critical component of metadata management, data governance and data intelligence. It provides an integrated view of conceptual, logical and physical data models to help business and IT stakeholders understand data structures and their meaning.
Quite simply, you can’t manage what you can’t see.
Data modeling is the first step to ensuring mission-critical information is used, understood and trusted across the enterprise. It has many benefits. Following are the top six benefits of data modeling organizations can realize:
For more information on the benefits of data modeling, click here.
erwin Data Modeler (erwin DM) is an award-winning data modeling tool used by Fortune 500 companies, including some of the world’s leading financial services, healthcare, critical infrastructure and technology firms.
Its history and proven track record enables users to benefit from the primary benefots of data modeling. In addition, erwin DM users have the ability to:
Visualize any data, from anywhere
erwin DM enables organizations to visualize their data whether structured or unstructured, regardless of where its stored – in a relational database, data warehouse or the cloud – within a single interface.
Automate data model and database schema generation
erwin DM users benefit from greater automation capabilities saving them time, increasing efficiency and reducing errors.
Centralize model development and management
erwin DM boasts an integrated view for conceptual, logical and physical data models to help bridge gaps in understanding between business and technical stakeholders.
Encourage data literacy, collaboration and accountability
Improve data intelligence and decision-making across the enterprise by maximizing the ability of stakeholders to use, understand and trust relevant data.
Increase agility in application development
Consolidate and build applications with hybrid architectures, including traditional, Big Data, cloud and on premise.
Reduce risks and costs
Automation and standardization of data definitions and structures reduces risks and costs, plus you can test changes and new applications before they go into production.
Foster successful cloud adoption
Automated schema engineering and deployment accelerates and ensures successful adoption of cloud platforms, like Snowflake, including auto documenting existing schema into reusable models.
See for yourself why erwin DM has been named DBTA’s Readers’ Choice for Best Data Modeling Solution for seven years in a row.