Challenges in data model tooling in 2023

Introduction

Data modeling is a crucial aspect of modern software development for software engineers, software architects and data engineers alike.

It involves creating visual representations of data structures, relationships, and attributes, which aid in understanding and modeling data effectively.

Data modeling tools play an important role in this process, simplifying the creation and management of data models. However, despite their benefits, data modeling tools come with their own set of challenges that organizations and data professionals must address to harness the full potential of their data assets.

In this article, we will explore some of the common challenges associated with data modeling visualization tools and discuss ways to overcome them when modelling data today building software applications.

Complexity and Learning Curve

Data modeling tools have evolved to accommodate the diverse needs of different industries and organizations. While this variety is a strength, it can also be a source of complexity. Many data modeling tools are feature-rich, offering a multitude of options and customization possibilities. This complexity can lead to a steep learning curve for new users.

One key challenge software teams face is ensuring that their team members are adequately trained to make the most of these tools.

Data professionals must invest time and resources in learning the ins and outs of the tool they are using. This can be particularly daunting for those who are new to data modeling or come from non-technical backgrounds. Overcoming this challenge requires a commitment to training and providing resources to help users become proficient in the chosen tool.

Furthermore, organizations may face issues with tool interoperability. The diversity of data modeling tools in the market means that professionals may need to work with different tools for specific tasks. Understanding the nuances of multiple tools can be overwhelming. Addressing this challenge involves designing clear workflows and best practices to ensure a smooth transition between tools and data modeling phases.

Collaboration

Collaboration is fundamental to the success of data modeling projects. Multiple team members, often from different departments or even external partners, need to work on the same data model simultaneously. However, data modeling tools can sometimes fall short in enabling seamless collaboration and communication.

Version Control

Version control is one more significant challenge in collaborative data modeling. When multiple team members make changes to a data model, it can be difficult to track and manage different versions. This can lead to conflicts, errors, and data inconsistencies. Many data modeling tools lack robust version control mechanisms, making it challenging for teams to keep track of changes and manage concurrent updates.

Especially the integration with Git as the de-facto standard for version control is lacking in many tools available where most data models are now defined in the source code repository.

Process Integration

Data modeling tools are often used in conjunction with various data sources and databases. Ensuring that the tool is compatible with the team's existing data infrastructure can be a significant challenge. The data modeling tool should be able to connect to different databases, support multiple data formats, and integrate seamlessly with the existing technology stack.

To overcome compatibility challenges, organizations need to invest in data integration solutions and ensure that their data modeling tools have the necessary connectors and adaptability to handle diverse data sources and formats.

Documentation

Effective data modeling involves not only creating data models but also documenting them comprehensively. Documentation is essential for understanding the data model's purpose, structure, and relationships, as well as for aiding in data governance and data lineage analysis.

Data modeling tools often provide features for documenting data models, but this can be a challenge if not done consistently and thoroughly. Inadequate documentation can lead to misunderstandings, errors, and inefficiencies when working with data models.

To address documentation challenges, organizations should establish clear guidelines and standards for documenting data models. These guidelines should cover naming conventions, descriptions, and data lineage information. Additionally, they may consider using metadata management tools to centralize and organize metadata for easier access and analysis.

Besides documentation tend to be outdated when lacking version control and integration with the team’s workflow leading to misunderstanding and miscommunication in organizations.

A process or integration for keeping documentation up-to-date is required.

Available visual data modeling tools

There are a number of visual data modeling tools available online from drawing and charting tools to more purpose-build tools turning code into ER-diagrams for example. We have picked 3 that can be used to visualize your data model drafts online:

01. Dbdiagram.io

This web-based database diagram designer is mainly for people with technical skills - developers and data analysts using DBML and other languages to define database schema and visualize them.

Its intuitive code-based interface makes it easy for users to draw Entity-Relationship (ER) diagrams as they code.

02. Draw.io

Draw.io is a versatile online drawing tool tailored for various kinds of modeling and diagrams. It has a wide range of customizable features, it empowers data professionals to effortlessly create intricate data models and collaborative work. It also offers a SQL plugin to turn code into diagrams.

03. Lucidchart

Lucidchart is a powerful online platform that excels in chart visualization including data model drafts. Its robust features can be used to draft data models as a chart and collaborate online.

04. Miro

Miro is one of the most popular whiteboard tools available online to work together in real-time in teams. It can be used to brainstorm and create initial data model drafts together. It focusses more on real-time collaboration and visualization than integration with the actual data model code itself later.

05. Whiteboard (offline)

Many teams today rely on whiteboards/offline session which can be fun and interactive but normally lead to a gap of communication, challenges to digitize and recap actual results from the sessions.

How Hubql solve these challenges

At Hubql we have put a lot of thought and our experience into building a platform that can help your team tackle the challenges or data model visualization and collaboration.

Similar to other tools available you can turn data model schemas into diagrams automatically using code. Automatically generated diagrams solve the problem of outdated documentation and ability to embed/export them for further use.

Beyond converting data model code to ER diagrams for example we have combined the visualization with additional features to ease collaboration and integration by providing comments, suggestions, shareable links and Git integration with GitHub.

If you are struggling with your data modeling process or your data modeling tooling today,
reach out to us to find out how we or other tools can help you.