A well-structured data model is the foundation of any successful data-driven system, given that it ensures efficiency, consistency, and scalability. However, even with all the experience in the world, it’s still possible to fall into common data modelling traps that lead to performance issues, data inconsistencies, and increased maintenance costs.

In this article, we’ll explore some of the most frequent mistakes made in data modelling and provide practical solutions to help you avoid them. By understanding these pitfalls and implementing specific best practices to avoid them, you can ensure your data models remain flexible, efficient, and aligned with business needs.

Poor Normalisation (Over- or Under-Normalisation)

Striking the right balance for normalisation is critical. Over-normalisation can introduce unnecessary complexity, resulting in excessive joins that slow down queries. Conversely, under-normalisation leads to data redundancy, which in turn increases storage costs and the risk of inconsistencies.

The best starting point to work around this is to follow the third normal form (3NF) as a guideline and then adjust from there based on your use case. Make sure to optimise performance specifically based on what you’re doing – e.g. OLTP systems benefit from normalisation, whereas OLAP systems often require a lower level of normalisation. It’s also important to ensure you regularly review your data structures to ensure they remain aligned with business requirements as they evolve over time.

Inflexible Schema Design

A rigid schema can create significant issues when business requirements evolve. If a model lacks adaptability, simple changes, such as adding a new attribute, can result in complex migrations and downtime.

The solution here is to plan for future scalability by using schema versioning from the outset. You should also consider whether you might be able to reap the benefits of JSON columns or flexible NoSQL solutions when appropriate. Finally, as with the first point, you should also ensure that you regularly review your schema with stakeholders to ensure it continues to align with business needs.

Ignoring Business Needs

A technically sound data model is as good as pointless if it doesn’t actually reflect business logic. Misalignment between data structure and real-world use cases can lead to inefficient queries, data misinterpretation, and ultimately require a costly rework.

To get around this, ensure you collaborate with domain experts early in the design phase. By utilising their insights, you ensure that the model reflects actual business processes and requirements, reducing the risk of costly redesigns later on. You should also make sure you use domain-driven design (DDD) principles to ensure your models reflect real-world business rules. Finally, you once again want to continuously validate your model against actual business operations to ensure alignment there.

Lack of Clear Data Governance

Without well-defined governance policies, inconsistencies arise in data definitions, naming conventions, and access control, which will inevitably compound and lead to both misinterpretations and compliance risks.

With this problem, you want to establish clear governance policies from the outset, covering ownership, access control, and data definitions. You should also make sure to maintain comprehensive documentation and enforce naming conventions as well as standardised terminology throughout the project to ensure consistency from start to finish.

Overcomplicating Relationships

Excessive foreign keys, deeply nested relationships, and unnecessary complexity can make queries inefficient and slow. This often arises when every real-world entity is modelled with strict one-to-one or one-to-many mappings, even when they are not necessary. This not only increases query times but also makes debugging and future modifications more cumbersome. Additionally, overly complex relationships can introduce integrity constraints that slow down inserts, updates, and deletions, making the system harder to scale.

To work around this issue, make sure to keep relationships as simple as possible to minimise query overhead. You also want to use denormalisation selectively when performance outweighs storage concerns. Finally, make sure to implement indexing and caching strategies to optimise performance.

Failing to Optimise for Performance

Even if a data model performs well in development, it may well fail under real-world loads if you fail to optimise for performance, leading to slow queries and increased operational costs.

To overcome this issue, use indexing, partitioning, and query optimisation from the outset. You also want to conduct regular performance testing to identify any potential bottlenecks before deployment. The final thing on this checklist is to make sure you monitor query execution plans and optimise accordingly.

Not Considering NoSQL or Polyglot Persistence

Many teams default to relational databases without taking the time to evaluate alternative solutions, which can lead to inefficiencies for certain workloads.

You should always assess whether NoSQL (e.g., document stores, graph databases) could potentially be more suitable for your needs, and should also consider polyglot persistence, whereby different databases serve different use cases within the same architecture. Ultimately, you want to ensure you’re always choosing the right tool for the job rather than forcing all data into a single model.

Conclusion

Avoiding these common data modelling pitfalls requires careful planning, collaboration, and continuous evaluation. By focusing on normalisation balance, schema flexibility, business alignment, governance, and performance, you can build robust and scalable data models that support long-term success.

At Vertex Agility, we specialise in helping organisations design efficient, scalable, and future-proof data models. Whether you’re starting from scratch or optimising an existing system, our experts are here to guide you.

📧 Get in touch today to discuss how we can help you build data models that drive better insights and long-term business value.