Power Query best practices and performance

As marketing datasets grow, Power Query projects can quickly become hard to read, slow to refresh, or fragile. Most of the time, these issues don’t come from Power BI itself, but from small habits that accumulate over time.

The good news is that a few simple best practices are enough to keep your Power Query workflows clean, reliable, and performant. This article focuses on the principles that matter most for marketing teams: structure, naming, data quality, and avoiding common performance traps.

Make queries maintainable with a clear structure

Use staging queries to separate concerns

A very effective habit is to separate your work into different types of queries:

• A raw or staging query that only loads the data as it comes from the source
• A cleaning query where you handle formatting, renaming, filtering, and transformations
• A final query that is used in your model and reports

This approach makes it much easier to understand what each query is responsible for. It also simplifies debugging, reuse, and future changes.

Adopt naming conventions that scale

Clear naming saves a lot of time as projects grow.

business names instead of generic ones

Prevent data issues early with proper cleaning

Always validate data types

Data types have a major impact on both correctness and performance. Dates imported as text, numbers parsed incorrectly because of separators, or percentages treated as strings can all break visuals and calculations.

A good habit is to:

• Check data types early in the query
• Fix them explicitly rather than relying on automatic detection

This avoids subtle issues later in reports.

Trim and clean text fields

Marketing data often contains extra spaces or invisible characters, especially when coming from .CSV files or multiple platforms.

• Trim removes extra spaces from text values.
• Clean removes non-printable or invisible characters that often come from exports or copy-paste operations.

Using Trim and Clean helps:

• avoid unexpected duplicates
• make joins and filters more reliable
• improve overall data quality

These steps are lightweight but very effective.

Avoid slow transformations and fragile workflows

Filter and remove columns as early as possible

One of the simplest performance wins is to:

• remove unused columns early
• filter rows as soon as you know what you don’t need

This reduces the amount of data Power Query has to process in every subsequent step.

Be careful with complex row-by-row logic

Custom columns and complex conditional logic can be powerful, but they can also slow down refresh when overused. Whenever possible:

• keep transformations simple
• avoid unnecessary calculations at the row level
• prefer clear, explicit steps over clever but opaque logic

Merge only what you need

Merging queries is often necessary, but it’s also one of the heaviest operations in Power Query. To keep things efficient:

• reduce datasets before merging
• merge only the columns you actually need
• avoid chaining many merges in a single query

This keeps refresh times predictable.

Parameters: making queries more flexible

Parameters allow you to define reusable values such as file paths, date ranges, or environments. For marketing teams, they can be useful for:

• switching folder paths without editing steps
• adjusting date ranges centrally
• managing test vs production setups

That said, parameters are optional. If they add complexity without a clear benefit, it’s better to skip them until the project really needs them.

Conclusion

Good Power Query performance doesn’t come from advanced tricks. It comes from clear structure, consistent naming, validated data types, and simple transformations applied intentionally. By adopting these best practices early, marketing teams can build Power BI reports that refresh faster, break less often, and remain easy to understand as reporting needs evolve.

A clean Power Query foundation makes every dashboard more reliable and every analysis more trustworthy.