u/Think_Beautiful_1192

Hi everyone,

I'm working on creating a standard Power BI theme that my team can use across all of our dashboards. The goal is to provide a consistent experience for users so that reports feel familiar, professional, and easy to understand regardless of who built them.

My biggest challenge right now is color selection.

Our primary brand color is a bright red (#C91245), but when I use it heavily throughout a dashboard it feels overwhelming and visually harsh. We also have several supporting brand colors available, and I'm trying to determine which colors should serve as primary, secondary, and accent colors within the theme.

I'm also debating between:

• A complementary color palette
• A gradient-based palette
• Some combination of both

One complication is that many of our reports contain 20+ reason code categories that need to be displayed in charts and visuals. My current palette doesn't provide enough distinct colors, so Power BI starts assigning default colors, which makes the dashboards feel inconsistent and cluttered.

My goals are to:

• Create a modern, clean, professional look
• Support both internal and external audiences
• Ensure charts with many categories remain readable
• Build a scalable theme that works across multiple dashboards

These are the brand colors I have available:

• #C91245 (Red)
• #F36C21 (Orange)
• #FFBF3D (Yellow)
• #00A661 (Green)
• #004F38 (Dark Green)
• #4F8CFF (Light Blue)
• #003DA5 (Dark Blue)

If you were building a dashboard theme around these colors, how would you approach it? Which color would you use as the primary color, and what strategy would you recommend for handling visuals with 20+ categories?

I'd love feedback from anyone with a strong eye for dashboard design, data visualization, or color theory.

I’m pretty sure I’m overthinking this and have gone in circles long enough, so I’m hoping to get some perspective from folks here.

I have a fairly large Power BI model sourced from a single SQL query. Most of the heavy lifting is done upstream in SQL Server, and the result is one wide table with basically everything I could possibly need for analysis. It’s 100+ columns and a little over 3 million rows.

Once it hits Power BI, I’m stuck on whether I should just leave this as a flat table or reshape it into a star schema for better performance and model design. The end goal is to have a single semantic model that can support multiple reports/dashboards.

The data itself is SAP financial accounting data, each row represents a line item on an accounting document. Columns include things like document numbers, posting keys, reason codes, customer attributes, etc. I also have a number of indicator/flag columns (Payment Document Flag, Post Audit Flag, Invalid Flag, all 0/1).

I can clearly identify some obvious dimensions (Date, Processor, Customer), but where I start to question myself is around the smaller, repetitive attributes. For example, we only have about 10–15 reason codes, each with rollups that are already calculated in SQL. Same question for things like age groups and sort columns, these are also pre-calculated upstream.

So, my main questions are:

• Would you keep this as one large flat table, or break it into a star schema?
• If breaking it up, how far would you go? Only obvious dimensions, or anything repetitive?
• Do small, low-cardinality attributes like reason codes or age groups belong in the fact table, or spun out into dimensions?
• Does it matter if those attributes are already fully derived in SQL?

For context, the original dashboards were built by contractors and everything was left as one massive table. It works, but I know this isn’t great from a modeling perspective and I’m concerned about performance and long-term maintainability at this scale.

I suspect I’m overcomplicating things, so I’d really appreciate hearing how others would think about this.

What are your thoughts?