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What to Consider When Choosing a Data Modeling Tool

Nov 16, 2021 by Kathy Meara

data modeling tool

Many organizations are searching for a data modeling tool as they undertake application modernization initiatives to move from legacy infrastructure and migrate to the cloud. If only migrating was as easy as just reinstalling the app, like we do when switching laptops! But of course, business applications have complex databases behind them, and those databases need to go along for the ride. When an organization needs to design or redesign a database, the data modeling process comes into play.

In the white paper, Top 10 Considerations for Choosing a Data Modeling Solution, the analysts at IT Central Station looked at what actual customers were saying about what led them to select erwin® Data Modeler by Quest® as the tool they relied on as the foundation of their application modernization lifecycle.

Several of the key considerations focus on alignment – to standards, to business objectives and among team members. Having a data modeling tool that’s used by application development and database design teams helps ensure that everyone follows best practices for data normalization across consistent structures.

Another important consideration for selecting a data modeling tool is how well it supports collaboration between IT and the business as the data models are being built. Elements like visualization are critical for conveying data schemas and relationships to business users so they understand how data is flowing.

Technical considerations include the ability to support multiple platforms and database types, particularly NoSQL databases. Application modernization inevitably means reevaluating current databases and potentially selecting new ones that better meet the needs of the data and application. One key feature of erwin Data Modeler is the ability to generate code from a data model – this is a huge timesaver.

Organizations are also adopting non-relational databases, such as Cassandra and MongoDB for the volumes of unstructured data that businesses generate. Many are using Snowflake for its analytics capabilities and need a data modeling tool that will handle these platforms as easily as it does relational databases.
Support for data modeling standards should be part of every tool. These standards encompass data modeling notation and best practices and may be required by certain industries or government bodies.

Finally, a data modeling tool needs to be easy to use. Not all users will be data architects or technical experts, so the tool has to be straightforward for all levels of expertise. This includes quick installation and setup, automation of common tasks, and the ability to make changes quickly.

The push to modernize applications and migrate them to the cloud has brought data modeling tools to the forefront of must-haves for digital transformation. As you evaluate a data modeling tool for your organization, it’s helpful to know what to look for in a solution.

Download this white paper to learn what erwin Data Modeler users are saying about how the solution is meeting their needs and what they find most valuable.