Data Insights: Common Data Modeling Mistakes to Avoid

Introduction

A solid data model is the backbone of every reliable application, report, or data platform.

It defines how information is structured, stored, and related — influencing everything from system performance to scalability and data integrity.

Yet, even experienced engineers and analysts often fall into subtle traps that lead to long-term pain: inconsistent queries, redundant data, slow joins, and complex migrations.

In this post, we’ll explore the most common data modeling mistakes, why they happen, and how to design models that stand the test of time.

Mistake 1: Ignoring Business Context

A data model isn’t just about tables and keys — it’s a reflection of business processes.

Many teams design schemas focused on technical convenience instead of business meaning.

When you skip stakeholder input or domain analysis, your model might represent data correctly but fail to answer the right business questions.

Avoid it by:

• Conducting a domain discovery session before schema design.
• Using conceptual models or entity relationship diagrams (ERDs) to map real-world processes first.
• Revisiting the model when business logic changes.

Mistake 2: Over-Normalisation or Under-Normalisation

Normalisation helps eliminate redundancy — but taken to the extreme, it can make queries unreadable.

Conversely, skipping normalisation leads to bloated tables and inconsistent data.

Symptoms:

• Too many join operations (over-normalised).
• Duplicate or denormalised columns (under-normalised).

Fix:

• Apply 3rd Normal Form (3NF) as a balanced starting point.
• Denormalise selectively for read-heavy analytical systems (e.g., star schema).
• Always document your design rationale for each denormalisation.

Mistake 3: Poor Naming Conventions

Cryptic table or column names (tbl1, cust_cd, x_data) make the model unreadable and prone to errors.

Your schema should be self-documenting — clear enough that another engineer can understand it instantly.

Best practices:

• Use singular, descriptive names (Customer, Order, Product).
• Maintain consistent case style (e.g., snake_case or camelCase — not both).
• Prefix bridge or mapping tables clearly (order_product_map).
• Reserve abbreviations only for universally known acronyms (e.g., URL, ID).

Mistake 4: Ignoring Data Types and Constraints

A common oversight is choosing generic data types (e.g., storing everything as VARCHAR) or skipping constraints like NOT NULL, UNIQUE, or CHECK.

This causes performance issues, invalid data, and broken relationships over time.

Example:

Storing timestamps as strings prevents efficient sorting or time-based queries.

Avoid it by:

• Selecting the smallest appropriate data type for each field.
• Enforcing constraints at the database level (not just in the application).
• Using foreign keys to maintain referential integrity.

Mistake 5: Lack of Scalability Considerations

Data models built for today’s dataset often crumble tomorrow.

Designs that assume a single region, a small dataset, or simple joins don’t scale well when data volume or concurrency spikes.

Fix:

• Anticipate growth in data size and query complexity.
• Design for partitioning, indexing, and sharding early.
• Use surrogate keys (UUIDs or sequences) for scalability instead of composite keys that slow joins.

Mistake 6: No Version Control or Change Management

A schema isn’t static — it evolves as your product or business grows.

Without structured version control, schema drift and undocumented changes can break downstream systems.

Best practices:

• Manage schema migrations using tools like Alembic, Liquibase, or Flyway.
• Version every DDL change in Git.
• Implement database migrations in CI/CD pipelines.

Mistake 7: Misusing Primary and Foreign Keys

Sometimes developers use natural keys (like email or username) as primary keys — which can change or duplicate — leading to data inconsistency.

Correct approach:

• Always prefer surrogate primary keys (auto-increment or UUID).
• Use foreign keys for relational integrity, not just logical relationships in the app.
• Ensure cascading rules (ON DELETE CASCADE, etc.) are intentional.

Mistake 8: Skipping Index Strategy

Indexes speed up queries but can also slow down inserts and updates if overused.

Many teams create indexes reactively rather than planning them during design.

Fix:

• Identify high-read and high-write tables.
• Use composite indexes only when necessary.
• Periodically review unused or duplicate indexes.
• Monitor performance with EXPLAIN plans.

Mistake 9: Treating Data Warehouse Like a Transactional DB

Operational databases (OLTP) and analytical warehouses (OLAP) serve different purposes.

Applying OLTP design principles (like strict normalization) to OLAP systems causes performance bottlenecks.

Fix:

• For analytics, adopt star or snowflake schemas.
• Pre-aggregate data for frequent reports.
• Use columnar storage (e.g., Redshift, BigQuery, Snowflake) for large-scale reads.

Mistake 10: Not Planning for Auditability and Lineage

When data flows across systems, you need traceability — knowing where a value came from and when it changed.

Without audit fields or metadata tracking, debugging becomes guesswork.

Best practices:

• Add created_at, updated_at, and source_system fields.
• Maintain data lineage through ETL metadata or tools like OpenLineage.
• Store change history in slowly changing dimensions (SCDs) for analytical systems.

Conclusion

Good data modeling isn’t just a design task — it’s a discipline.

Avoiding these mistakes requires a balance of technical rigor, domain understanding, and continuous review.

A well-designed model accelerates development, simplifies reporting, and ensures data trust — the ultimate foundation of every data-driven organization.

References

• The Data Warehouse Toolkit – Ralph Kimball (🔗 Link)
• PostgreSQL Documentation: Constraints and Keys (🔗 Link)
• AWS Redshift: Best Practices for Data Modeling (🔗 Link)