As the digital transformation continues to change how business is done day to day, many organizations are searching for data modeling tools that meet their new needs. Public sector organizations and federal agencies, much like their private sector partners, must move from legacy infrastructures and migrate to the cloud, however, this is not as easy as moving between laptops!
Most 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 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 data modeling tools 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 data and application needs. 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, data modeling tools need 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.