Despite data’s perceived value, research by Accenture shows that only one in five companies are unlocking the intrinsic value of their data. That’s because the true power of data lies in the insights it provides—not the data itself.
Simply collecting mountains of data won’t make a difference. To transform data into actionable intelligence you need the right resources.
Data modeling tools are an important part of this process, providing a structured approach to organizing your data so you can understand how the data is captured, stored, and used. This enhanced understanding improves data quality and builds trust, allowing you to find and operationalize actionable insights.
But with so many data modeling tools available, how do you know which is the right fit for your business? This guide is your answer.
Table of contents:
1. Integration with other databases and tools
As your organization grows, data flows in from a variety of sources. A data modeling software that supports connectivity with various databases and cloud data platforms will enable you to create comprehensive documentation. Doing so will help you understand the dormant data in your data warehouses and group them for faster data analysis.
2. Collaboration and sharing
Many data modeling tools offer collaboration features, which allow multiple team members to work on the same model simultaneously. You can use sharing and collaboration features to track changes, present the work, or share feedback. This level of transparency helps maintain the integrity and accuracy of the data models.
3. Support for forward and reverse engineering
Forward engineering is the process of transforming a high-level abstract data model into a physical implementation within a database system. On the flip side, reverse engineering means creating a data model from a database or script. Your data modeling tool should support both techniques, which will help your team keep up with evolving business needs.
4. Allows for model validation
Before investing in a data modeling tool, confirm whether it offers validation mechanisms. For example, many modern tools allow you to assess model performance, conduct A/B testing, and create custom visualizations. You should also be able to run checks for potential errors such as missing relationships, inconsistent data types, or incomplete definitions.
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Leading in AI-powered analytics, ThoughtSpot lets you easily organize and analyze vast datasets. Our platform utilizes AI and large language models to make data modeling easier and faster by automatically generating synonyms for all data columns, giving time back to data professionals. With ThoughtSpot’s Data Modeling Studio, you can create governed, reusable, and logical data models that empower everyone to find valuable insights.
Moreover, by leveraging “Worksheets” and “ThoughtSpot Modeling Language” analysts can create a simplified, curated view of their data using an intuitive UI, allowing them to build model complex datasets faster. Whether you want to model your data or build real-time, interactive dashboards, ThoughtSpot is a versatile business intelligence platform that caters to all your business needs.
Core features:
Open and scriptable ThoughtSpot Modeling Language (TML)
Enhanced data modeling and exploration with filter rules
Connectivity with multiple cloud data sources
Write custom SQL to build searchable data
Seamless integration with dbt models
AI-assistance for data modeling
Governed and secured controls
Easy collaboration and sharing
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dbt Labs is a data transformation tool that enables data analysts to transform, model, test, and document data in the cloud data warehouse. Users can model their data using SQL or Python and track code changes using built-in, Git-enabled version control. This data modeling software also allows users to write models once and deploy them across any number of environments, streamlining the process and ensuring consistency.
Core features:
Git-enabled version control
Integration with multiple cloud databases
Supports SQL and Python code
Data testing and code deployments
Sharing and collaboration
Erwin Data Modeler is the data modeling tool that helps analysts and data architects design and optimize database structures. With a graphical interface and version-control system, users can keep track of changes, create and customize entities, and visualize complex information. The platform connects with multiple databases and supports conceptual, logical, and physical data models.
Core features:
Synchronization of models, scripts, and databases
Supports forward and reverse engineering
Integrates with various databases
GitHub integration
ER/Studio is another data modeling software that helps data pros craft complex database structures. Its features include forward and reverse engineering, metadata management, data lineage, and team-based model sharing. The platform integrates with multiple databases and offers version management for flexibility and transparency.
Core features:
Supports logical, physical, and dimensional data modeling
Integration with multiple databases
Visual data lineage
Forward and reverse engineering
Version management
MySQL Workbench is a visual database design tool specifically designed for database architects and developers. This open-source platform provides comprehensive tools for data modeling, SQL development, and database administration. It features a graphical user interface that allows users to create complex ER models and collaborate with teams.
Core features:
Writing SQL queries
Graphical user interface
Model change management
Administer MySQL environments
Database integrations
DBSchema is a database management tool that helps users design, document, and manage their databases. Users can create and edit tables, columns, relationships, and constraints through an intuitive graphical interface. The platform supports conceptual and logical data models and allows users to translate them into physical database schemas.
Core features:
SQL Editor
HTML5 Documentation
Relational Data Explorer
Supports forward and reverse engineering
Visual Query Builder
Looker is Google’s data analytics tool designed for reporting and dashboarding. It utilizes a proprietary modeling language known as LookML, which allows users to define and manage data models. Looker also supports version control, allowing multiple team members to work on the same model simultaneously, ensuring transparency in changes made to data models. The connection with Google Workspace tools, such as Slides and Sheets, enhances collaboration.
Core features:
Propriety modeling language known as LookML
Version control
Integrates with multiple databases
Supports SQL querying
Seamless sharing and collaboration
Instead of hardcoding SQL statements, wading through mountains of data, and manually creating data models, you can now leverage ThoughtSpot to go from insight to action within hours—not months or weeks.
Our AI-powered search experience allows you to run Natural Language Queries (NLQ) on all of your business data, helping you create precise definitions of measures, qualifiers, and groupings. Even better, you don’t have to 10x your data team to do it. With AI-assisted data modeling, analysts can eliminate BI backlogs and automate tedious tasks, helping them focus on value-driven initiatives.
For example, take e Cox 2M. Their data team faced constant challenges with their legacy BI tool, which took over five hours to handle a single ad-hoc request. By introducing ThoughtSpot, the team was able to reduce time to insights by a staggering 76% and save $70,000+ in cost-to-serve.
Simplify your data modeling process with ThoughtSpot. Schedule a demo today.
