Make better tables and matrixes in Power BI reports: a comprehensive guide

Key takeaways

Tables are easy to create and use. A default Power BI matrix gives the reader detailed numbers but asks them to do all the work to interpret and derive meaning from the data. Deliberate choices about what to show, how to format it, and what to emphasize make the difference.
The hard part is making the choices, not applying them. Deciding what matters most, using conditional formatting to highlight it, removing noise, sorting with intent, and adding context like sparklines; none of these are technically difficult, but each requires a judgment call that depends on the reader’s question.
AI can help with the implementation, but not the decisions. AI Agents can speed up report creation by removing friction, but they still need someone to define what matters, for whom, and why.

This summary is produced by the author, and not by AI.

Tables: the most popular visuals of all time

To understand our attachment to the table, let’s first take a very brief detour to ±3100 BCE. In ancient Mesopotamia, scribes divided wet clay into cases and columns to track deliveries and distributions of barley and wheat, with pictographs identifying the product and circles to record quantity. It’s arguably one of the earliest recorded uses of a table, and it illustrates our tight bond: we go way back.

V006 Figure 2 - Image of an ancient Sumerian clay tablet from approximately 3100 BCE, showing two sides (obverse and reverse) divided into columns and cases with pictographs and circular impressions used to record quantities of barley and wheat

In the year 2026 CE, Power BI reports make tables and matrixes easy to create. Drop one onto the canvas, add some fields, and you have a grid of numbers. But “easy to create” and “easy to read” are different things. A table that works for the reader requires deliberate choices about what to show, how to format it, and what to emphasize - choices that are subjective, because they depend on who’s reading and what question they’re trying to answer.

Why you should design and build better tables

The human brain hasn’t evolved to process large heaps of information simultaneously; it needs to put in effort and is prone to error. It is, however, pretty good at visual perception. We can display information in ways that play into the strengths of the human brain (e.g. rapid and effortless pattern recognition) and minimize reliance on its weaknesses (e.g. short working memory and poor attention span). That’s what data viz is all about.

To illustrate, let’s take a look at how humans read tables.

V006 Figure 3 - Diagram of an eye and many arrows laid over a table visual, indicating eye movements. Even a simple table of 12 cells requires quite a bit of eye movements to read and some calculations by the brain before it can be understood

Furrowing a critical brow with a caveman grunt of “table bad!” isn’t the point; it’s not helpful. Visual charts leverage what humans are built for: spotting trends and deviations at a glance. But tables aren’t going anywhere – they’re the right choice when readers need precise values, comparisons across many dimensions, or the ability to look up specific rows. The question isn’t whether to use tables, but how to make them work harder.

These principles are well-established, so why do we still see so many unformatted tables in the wild? Because applying the principles takes critical thinking and deliberate effort, which are tempting to skip when the deadline is in a few hours and stakeholders “just want the numbers”. It’s a fair shortcut in the moment, but it snowballs: one unformatted table becomes ten, and before long the organization’s reporting culture defaults to raw data dumps that no one reads carefully. Let’s look at what that effort actually involves, because even AI tools that can generate charts from prompts shouldn’t skip it.

Going from Power BI defaults to deliberate choices

Suppose you have a product table with performance against targets. Out of the box, the Power BI matrix visual gives you a grid of numbers with alternating row shading and some nice blue accent bars. It's accurate, but it asks the reader to do all the work.

V006 Figure 4 - Image of a table as default Power BI matrix visual, with hallmark gray row shading and blue grid accent lines

V006 Figure 5 - Image of a table with only the relevant metrics, conditional formatting with color bars on gap vs. target. Gridlines are minimal and rows have whitespace to breathe. The table is sorted by Orders MTD Value, and a sparkline column shows the trend of gap vs. target

A few deliberate changes can make the table work for the reader:

Decide what matters most. Not every number in the table deserves equal attention. If the reader’s first question is "which product types are behind target?", then the gap between order value and target is the signal, everything else is context. That decision comes before any formatting. Keep only the metric and the gap.

V006 Figure 6 - Image of a before-and-after comparison of a Power BI table. The top table has four columns including a redundant target column, with a callout highlighting that the focus should be on the gap between actual and target. The bottom table is simplified to three columns: Type, Orders MTD, and vs. Target, removing the redundant column

Use conditional formatting to guide the eye. Highlight the signal once you know it. Data bars on important metrics and a color scale on the variance column let the reader spot trouble without scanning every cell. We’re offloading work from short-term memory to visual perception: exactly the trade-off between memory and perception.

V006 Figure 7 - Image of a before-and-after comparison of a Power BI table. The top table has no conditional formatting. The bottom table adds data bars on Orders MTD and color-coded values with triangle symbols on the vs. Target column, using red for negative and blue for positive values to draw attention to deviations

Remove what doesn't help. Gridlines, banded rows, and borders are not always helpful. They add visual noise that competes with the signal. Strip them back and let whitespace do the separation.

V006 Figure 8 - Image of a before-and-after comparison of a Power BI table. The top table has gridlines and banded row shading. The bottom table removes gridlines, banded rows, and borders, relying on whitespace to separate rows for a cleaner and less cluttered appearance

Sort with intent. Alphabetical order rarely answers a useful question. Sort by the metric that matters most, so the reader's eye lands on the answer immediately.

V006 Figure 9 - Image of a before-and-after comparison of a Power BI table. The top table is sorted alphabetically by product type. The bottom table is sorted by vs. Target value, placing the most significant deviations at the top so the reader immediately sees which product types are furthest from target

Add context where relevant. A single metric to indicate gap vs. target tells you the situation today but not whether its performance has been improving or declining, often an important distinction that drives which actions to take. A sparkline column can help answer that question, adding a compact trend line that can paint a different picture than the single gap value.

V006 Figure 10 - Image of a before-and-after comparison of a Power BI table. The top table shows Type, Orders MTD, and vs. Target columns sorted by gap. The bottom table adds a Trend column with sparklines showing each product type's performance over time, providing context on whether the gap has been improving or declining

None of these steps are technically difficult. The hard part is making the choices—and that's exactly the part that shouldn’t be skipped.

TIP

Sparklines are Power BI’s only default microvisual for tables. While useful, sometimes you may want to go a step further. SVG-based microvisuals are highly flexible but make the report harder to maintain and change over time. It’s up to you – the author – to decide whether the overhead is worth the gain for the readers.

V006 Figure 11 - Image of a Power BI table with an additional SVG Dumbbell column, where each row displays a dumbbell microvisual that shows both scale and gap simultaneously using dots connected by a line, offering richer context than a simple sparkline at the cost of added development and maintenance overhead

Can’t AI do it for us yet?

AI agents can now generate visuals and even entire reports from natural language prompts. This can be useful for removing unnecessary friction: handling tedious UI interactions, generating boilerplate DAX, or exploring technical possibilities you might not know exist due to gaps in Power BI knowledge. Just as agents can accelerate semantic model development, they can assist with report creation... but they still need clear direction and context. The Power BI report format is complex, and without proper understanding of what makes a report "good" (as we've discussed), agents will happily generate technically correct but ultimately unhelpful tables and dashboards.

AI is a powerful tool for execution, but human judgment remains essential for defining what to build and why. More specifically, the Large Language Models (LLMs) powering coding agents were trained on and in generating text. When an LLM builds a Power BI report, it’s manipulating a textual presentation of the report format, not “seeing” the result the way humans do. Vision models exist that can interpret and generate images, but their model of vision has very little in common with how we humans actually process what we see. Our brains rely on perceptual heuristics – e.g. grouping, contrast and pattern recognition – that let us near-instantly judge whether a visual “works” for the data it’s displaying. No generative AI model currently replicates that. So while an agent can assemble a report, AI can’t yet truly evaluate whether the report communicates the message effectively to a human audience. That judgment is still yours.

Further recommended reading

Design Better Data Tables by Matt Ström-Awn. Concise breakdown of the design fundamentals behind readable tables – alignment, typography, and minimalism – that provides the rationale behind the formatting choices discussed in this article.
Three Things You Can Do to Make Your Data Tables More Visual by Jon Schwabish. Practical advice on integrating visual elements like heatmaps and sparklines into tables to reduce cognitive load.
SVG in Power BI – Part 1: SVG Basics by Laura GB. Hands-on starting point for building SVG-based microvisuals in Power BI tables from scratch.

In conclusion

Tables aren’t going anywhere, nor should they, so formatting them with just a few deliberate actions so they’re better suited for human perception just makes sense. Also, AI can speed up the execution of building and formatting tables. But deciding what matters most, for whom, and why – that’s still on you. The steps above aren’t a checklist to memorize; they’re a way of thinking that applies to every table you build. Start with the reader’s question and format the table to answer it.