Power BI Visualization Types

Columns divided into segments stacked on top of each other, representing different categories

To show total values and how parts contribute to the total

Total sales per year, broken down by product categories

X-axis (categories), Y-axis (values), Legend (segments)

Use when you need both totals and breakdown visibility

Each column scaled to 100%, with segments showing relative percentages

To focus on proportions rather than absolute values

Percentage share of expenses (salaries, rent, supplies) across departments

X-axis (categories), Y-axis (values), Legend (segments)

Best for comparing relative composition over time or across categories

Columns for different series placed side by side within each category

Compare categories directly across groups. Compare exact values instead of totals or percentages.

Comparing budget vs actual profit for each quarter

X-axis (categories), Y-axis (values), Legend (series)

Limit to 3-4 series for readability, excellent for direct comparisons

Line chart with each data point highlighted with marker (dot or symbol)

Emphasize exact values at each point. Small datasets or when individual points should stand out.

Quarterly profit with visible dots on each quarter

X-axis (time), Y-axis (values), Legend (optional)

Use for smaller datasets, helpful when exact values are important

Display one headline KPI

Want immediate 'at a glance' number

Total Revenue, YTD Operating Margin %, Cash Balance, DSO

Fields → single measure (e.g., Total Sales)

Create semantic measures with formatting baked in, use conditional formatting vs target, add date context in Category label

Redesigned, more flexible card visual with modern style and grouping capabilities

Business/Finance KPIs, Marketing metrics, Operations tracking. Available only in Power BI Desktop (preview).

Revenue, margins, OPEX, budget vs actual grouped in modern card layout

Multiple metrics can be grouped in one container with full design control

Enable in Preview features, restart Power BI Desktop, requires Pro/Premium for sharing

When sharing to Power BI Service (not yet supported), production environments

Data points connected by line, great for showing trends over time

Track continuous data like sales, profit, stock prices over time. Highlight overall trend rather than individual points.

Monthly revenue growth for company

X-axis (time), Y-axis (values), Legend (optional)

Best for continuous time series data, use multiple lines sparingly

List several KPIs together

Want compact KPI panel without creating table

Total Sales, Gross Profit %, OPEX, EBITDA, Free Cash Flow

Multiple measures added to visual

Keep to 3-6 metrics, align units (all £ or %), order by importance, rename measures for display

Shows stage-to-stage drop-off in a process

Sales/marketing pipelines, recruitment funnels, grant application stages

Leads → Qualified → Proposal → Won with Overall Win % card

Group or Category (stage), Values (count/measure)

Enforce stage order using Sort by column, consider 100% stacked column for cross-stage comparisons

Explains how you get from starting amount to ending amount via increases/decreases

Bridging analyses: Budget → Variances → Actual; Opening cash → inflows/outflows → Closing cash

Profit bridge from Prior Year to Current Year: Price, Volume, Mix, FX, Cost Savings, One-offs

Category (step name), Y value (signed measure), Breakdown (optional subcategories)

Enable connector lines, use DAX for signed variance measures, keep to 5-10 bars, mark start/end as Total

Shows parts-of-whole with central space that can carry a KPI

Small number of categories (2-6), stakeholders care about shares not precise magnitudes

Share of total OPEX by category (Payroll, Rent, Utilities, Other)

Legend (category), Values (measure), Tooltips (extra measures)

Sort descending by value, keep labels outside with % values, hide categories <3-5% or group as 'Other'

Many small categories, subtle differences, totals matter more than shares

Shows single KPI performance against min/target/max

Quick 'speedometer' view for one metric (e.g., Utilisation %, On-time close %)

Close Timeliness % (value) vs 95% target, bounds 0-100%

Value (KPI measure), Target value, Minimum and Maximum

Define all with measures so gauge responds to slicers, keep big and lonely, use sparingly

Reveals relationships/correlation between two numeric variables, spots outliers

Test whether 'more X leads to more Y', data points represent entities (customers, SKUs, branches)

Ad Spend (X) vs Weekly Sales (Y) by campaign, colored by channel with trendline for ROI

X axis (measure), Y axis (measure), Details (entity/category), Legend (optional), Size (optional), Play axis (time)

Add trend line to show correlation, standardize axes, label outliers with data labels

Compare three variables: X and Y positions + bubble size

Must show extra quantitative dimension (e.g., market share) without separate chart

X = Revenue, Y = Gross Profit, Size = Market Share, by Region

X axis, Y axis, Size (all measures), Details (entity), Legend (group), Play axis (time)

Use tooltips for exact values, cap max/min bubble size so tiny entities remain visible

Overlay column series (magnitude) with line series (rate/ratio or second measure)

Comparing related measures with different scales (e.g., revenue vs margin %), showing trendline on monthly totals

Monthly Revenue (columns) vs Profit Margin % (line) with target margin constant line

Shared axis (time/category), Column values, Line values, Optional secondary axis

Put rate/percentage on secondary axis, format as % with 0-100% bounds, limit to one line

Total length of each bar fixed at 100%, showing percentage share

To compare proportion of categories across groups. Absolute values less important than percentage contribution.

Percentage share of revenue from product categories across regions

Y-axis (categories), X-axis (values), Legend (segments)

Best for comparing relative composition across different groups

Values expressed as percentage of total (always adds up to 100%)

To compare proportion or share of each category over time, not absolute values. Focus on distribution rather than totals.

Percentage of sales from each product category across years

X-axis (time), Y-axis (values), Legend (categories)

Use when comparing relative performance, not absolute growth

When absolute values are more important than proportions

A line chart with the area under the line filled in

To show changes in values over time, highlight magnitude of trends (e.g., profit growth, sales increase). Best for single series or few series where overlapping is not confusing.

Monthly revenue growth over time

X-axis (time/category), Y-axis (values), Legend (optional)

Use for continuous data, avoid too many overlapping series

Multiple area charts stacked on top of each other. Each category's values build on the previous one

To show how different categories contribute to a total over time. Useful when you want both the overall trend and individual contribution of each series.

Total revenue by region (North, South, East, West) over time

X-axis (time), Y-axis (values), Legend (categories)

Keep categories manageable (3-6 max), ensure clear color distinction

Bars placed on top of each other in a single line. Each segment represents part of the total

To show total values and how different parts contribute

Total expenses by department with segments for salaries, utilities, supplies

Y-axis (categories), X-axis (values), Legend (segments)

Use when you need to compare totals and contributions simultaneously

Shows data as slices of a circle. Each slice represents part of the whole (100%)

To show proportions or percentage share of categories. Best with few categories (2-6 max).

Share of total sales by product category

Legend (categories), Values (measure)

Keep categories small and distinct, use for clear percentage contributions

Too many categories (cluttered), small differences between categories, when totals matter more than shares

Bars for different series placed side by side within same category

To compare values across categories directly. Good for spotting differences between groups.

Comparing actual vs budgeted sales for each year

Y-axis (categories), X-axis (values), Legend (series)

Ideal for direct comparisons, limit to 3-4 series for readability