Tag: Data Visualization

Analytics, Business Intelligence, Data Visualization

Glossary – 100 “Data Visualization” Terms

Below is a glossary that includes 100 common “Data Visualization” terms and phrases in alphabetical order. Enjoy!

TermDefinition & Example
 AccessibilityDesigning for all users. Example: Colorblind-friendly palette.
 AggregationSummarizing data. Example: Sum of sales.
 AlignmentProper positioning of elements. Example: Grid layout.
 AnnotationExplanatory text on a visual. Example: Highlighting a spike.
 Area ChartLine chart with filled area. Example: Cumulative sales.
 AxisReference line for measurement. Example: X and Y axes.
 Bar ChartUses bars to compare categories. Example: Sales by product.
 BaselineReference starting point. Example: Zero line.
 Best PracticeRecommended visualization approach. Example: Avoid 3D charts.
 BinningGrouping continuous values. Example: Age ranges.
 Box PlotDisplays data distribution and outliers. Example: Salary ranges.
 Bubble ChartScatter plot with size dimension. Example: Profit by region and size.
 CardDisplays a single value. Example: Total customers.
 Categorical ScaleDiscrete category scale. Example: Product names.
 ChartVisual representation of data values. Example: Bar chart of revenue by region.
 Chart JunkUnnecessary visual elements. Example: Excessive shadows.
 Choropleth MapMap colored by value. Example: Sales by state.
 Cognitive LoadMental effort required to interpret. Example: Overly complex charts.
 Color EncodingUsing color to represent data. Example: Red for losses.
 Color PaletteSelected set of colors. Example: Brand colors.
 Column ChartVertical bar chart. Example: Revenue by year.
 Comparative AnalysisComparing values. Example: Year-over-year sales.
 Conditional FormattingFormatting based on values. Example: Red for negative.
 ContextSupporting information for visuals. Example: Benchmarks.
 Continuous ScaleNumeric scale without breaks. Example: Temperature.
 CorrelationRelationship between variables. Example: Scatter plot trend.
 DashboardCollection of visualizations on one screen. Example: Executive KPI dashboard.
 Dashboard LayoutArrangement of visuals. Example: Top-down flow.
 Data DensityAmount of data per visual area. Example: Dense scatter plot.
 Data Ink RatioProportion of ink used for data. Example: Minimal chart clutter.
 Data RefreshUpdating visualized data. Example: Daily refresh.
 Data StoryStructured insight narrative. Example: Executive presentation.
 Data VisualizationGraphical representation of data. Example: Sales trends shown in a line chart.
 Data-to-Ink RatioProportion of ink showing data. Example: Minimalist charts.
 Density PlotSmoothed distribution visualization. Example: Probability density.
 DistributionSpread of data values. Example: Histogram shape.
 Diverging ChartShows deviation from a baseline. Example: Profit vs target.
 Diverging PaletteColors diverging from midpoint. Example: Profit/loss.
 Donut ChartPie chart with a center hole. Example: Expense breakdown.
 Drill DownNavigating to more detail. Example: Year → month → day.
 Drill ThroughNavigating to a detailed report. Example: Customer detail page.
 Dual Axis ChartTwo measures on different axes. Example: Sales and margin.
 EmphasisDrawing attention to key data. Example: Bold colors.
 Explanatory VisualizationUsed to communicate findings. Example: Board presentation.
 Exploratory VisualizationUsed to discover insights. Example: Ad-hoc analysis.
 FacetingSplitting data into subplots. Example: One chart per category.
 FilteringLimiting displayed data. Example: Filter by year.
 FootnoteAdditional explanation text. Example: Data source note.
 ForecastPredicted future values. Example: Next quarter sales.
 Funnel ChartShows process stages. Example: Sales pipeline.
 GaugeDisplays progress toward a target. Example: KPI completion.
 Geospatial VisualizationData mapped to geography. Example: Customer density map.
 GranularityLevel of data detail. Example: Daily vs monthly.
 GraphDiagram showing relationships between variables. Example: Scatter plot of height vs weight.
 GroupingCombining similar values. Example: Products by category.
 HeatmapUses color to show intensity. Example: Sales by day and hour.
 HierarchyParent-child relationships. Example: Country → State → City.
 HighlightingEmphasizing specific data. Example: Selected bar.
 HistogramDistribution of numerical data. Example: Customer age distribution.
 InsightMeaningful takeaway from data. Example: Sales decline identified.
 InteractivityUser-driven exploration. Example: Click to filter.
 KPI VisualHighlights key performance metrics. Example: Total revenue card.
 LabelText identifying data points. Example: Value labels on bars.
 LegendExplains colors or symbols. Example: Product categories.
 Legend PlacementPosition of legend. Example: Right side.
 Line ChartShows trends over time. Example: Daily website traffic.
 MatrixTable with grouped dimensions. Example: Sales by region and year.
 OutlierValue far from others. Example: Extremely high sales.
 PanMove across a visual. Example: Map navigation.
 Pie ChartDisplays parts of a whole. Example: Market share.
 ProportionPart-to-whole relationship. Example: Market share.
 RankingDisplaying relative position. Example: Top 10 customers.
 Real-Time VisualizationLive data display. Example: Streaming metrics.
 Reference LineBenchmark line on chart. Example: Target line.
 ReportStructured set of visuals and text. Example: Monthly performance report.
 Responsive DesignAdjusts to screen size. Example: Mobile dashboards.
 Scatter PlotShows relationship between two variables. Example: Ad spend vs revenue.
 Sequential PaletteGradual color progression. Example: Low to high values.
 Shape EncodingUsing shapes to distinguish categories. Example: Circles vs triangles.
 Size EncodingUsing size to represent values. Example: Bubble size.
 SlicerInteractive filter control. Example: Dropdown region selector.
 Small MultiplesSeries of similar charts. Example: Sales by region panels.
 SortingOrdering data values. Example: Top-selling products.
 StorytellingCommunicating insights visually. Example: Narrative dashboard.
To learn more, check out this article on Data Storytelling.
 SubtitleSupporting chart description. Example: Fiscal year context.
 Symbol MapMap using symbols. Example: Store locations.
 TableData displayed in rows and columns. Example: Transaction list.
 TitleDescriptive chart heading. Example: “Monthly Sales Trend.”
 TooltipHover text showing details. Example: Exact value on hover.
 TreemapHierarchical data using rectangles. Example: Revenue by category.
 TrendlineShows overall direction. Example: Sales trend.
 Visual ClutterOvercrowded visuals. Example: Too many labels.
 Visual ConsistencyUniform styling across visuals. Example: Same fonts/colors.
 Visual EncodingMapping data to visuals. Example: Color = category.
 Visual HierarchyOrdering elements by importance. Example: Large KPI at top.
 Waterfall ChartShows cumulative effect of changes. Example: Profit bridge analysis.
 White SpaceEmpty space improving readability. Example: Padding between charts.
 X-AxisHorizontal axis. Example: Time dimension.
 Y-AxisVertical axis. Example: Sales amount.
 ZoomFocus on specific area. Example: Map zoom.
Analytics, Business Intelligence, Data Visualization, Microsoft Certification, PL-300, Power BI

Enable Personalized Visuals in a Report (PL-300 Exam Prep)

 
This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Visualize and analyze the data (25–30%)
   --> Enhance reports for usability and storytelling
      --> Enable Personalized Visuals in a Report

Note that there are 10 practice questions (with answers and explanations) at the end of each topic. Also, there are 2 practice tests with 60 questions each available on the hub below all the exam topics.

Overview

Enabling personalized visuals allows report consumers to customize how visuals appear and behave without modifying the underlying report design. This capability improves self-service analytics, increases user engagement, and supports storytelling flexibility, all while maintaining governance and data integrity.

This topic appears in the PL-300 exam under:

Visualize and analyze the data (25–30%) → Enhance reports for usability and storytelling

For the exam, candidates must understand what personalized visuals are, how to enable or disable them, what users can customize, and how personalization impacts the saved report experience.

What Are Personalized Visuals?

Personalized visuals allow report viewers (not authors) to:

  • Change the visual type
  • Add or remove fields
  • Modify measures or dimensions
  • Adjust filters and slicers
  • Change sorting
  • Save their customized version of a visual

These changes apply only to the user’s personal view, not the original report.

Key Characteristics

  • Personalization is user-specific
  • The original report remains unchanged
  • Users can reset visuals to the report author’s default
  • Requires edit permissions on visuals, but not dataset ownership

How to Enable Personalized Visuals

Personalized visuals are controlled at the report level in Power BI Service.

Steps (High-Level):

  1. Open the report in Power BI Service
  2. Select File → Settings
  3. Enable Allow users to personalize visuals
  4. Save the report

Once enabled, users see a “Personalize this visual” option in the visual’s menu.

What Users Can Personalize

When enabled, users may:

  • Switch between supported visual types
  • Add/remove fields from a visual
  • Change aggregations (Sum, Average, Count, etc.)
  • Apply filters and sorting
  • Create ad hoc analysis without editing the report itself

What Users Cannot Change

Personalized visuals do not allow users to:

  • Change the data model
  • Create or edit DAX measures
  • Modify report-level settings
  • Affect other users’ views
  • Save changes back to the dataset

This ensures data governance and consistency.

Personalized Visuals vs Editing Reports

FeaturePersonalized VisualsEdit Report
Requires edit accessNoYes
Affects original reportNoYes
User-specificYesNo
Data model changesNoYes

For PL-300, remember: personalized visuals are for consumers, not authors.

Resetting and Saving Personalizations

  • Users can save their personalized visuals
  • Saved changes persist across sessions
  • Users can select Reset to default to revert to the author’s design
  • Reset affects only the current user

Governance and Best Practices

When to Enable Personalized Visuals

  • Executive dashboards with varied analysis needs
  • Self-service BI environments
  • Reports consumed by analysts and power users

When to Disable

  • Highly curated executive reports
  • Regulatory or compliance-driven reporting
  • Scenarios where visual consistency is required

Exam-Relevant Scenarios

You may see PL-300 questions that involve:

  • Users wanting to adjust visuals without editing the report
  • Ensuring user changes don’t affect others
  • Improving report usability without redesigning pages
  • Choosing between personalization, bookmarks, or edit access

Key Exam Takeaways

  • Personalized visuals are enabled at the report level
  • Changes are user-specific
  • Original report design is not modified
  • Supports self-service analytics
  • Can be reset to the default view

Exam Tip

If a question states:

  • “Users want to modify visuals without changing the report”
  • “Each user should have their own customized view”
  • “Avoid giving edit permissions”

👉 The correct solution is often Enable personalized visuals.

Summary

Enabling personalized visuals enhances report usability by empowering users to explore data in ways that best suit their needs—without compromising governance or design standards. For the PL-300 exam, focus on when to enable this feature, what it allows, and how it differs from editing reports or using bookmarks.

Practice Questions

Go to the Practice Questions for this topic.

Data Visualization, Microsoft Certification, PL-300, Power BI

Design Reports for Mobile Devices (PL-300 Exam Prep)

 
This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Visualize and analyze the data (25–30%)
   --> Enhance reports for usability and storytelling
      --> Design Reports for Mobile Devices

Note that there are 10 practice questions (with answers and explanations) at the end of each topic. Also, there are 2 practice tests with 60 questions each available on the hub below all the exam topics.

Overview

Designing reports for mobile devices is a critical skill assessed in the PL-300: Microsoft Power BI Data Analyst certification exam. As more business users consume reports on phones and tablets, Power BI provides dedicated tools to ensure reports remain readable, performant, and user-friendly on smaller screens.

For the exam, you are expected to understand when and how to design mobile-optimized report layouts, how they differ from standard report pages, and best practices for usability.

Why Mobile Report Design Matters

Desktop reports often contain:

  • Multiple visuals per page
  • Wide layouts
  • Dense detail

On mobile devices, these designs can become:

  • Hard to read
  • Difficult to interact with
  • Slow to load

Power BI solves this by allowing authors to create dedicated mobile layouts that optimize:

  • Screen space
  • Touch interactions
  • Visual clarity

Power BI Mobile Layouts

Mobile Layout Feature

Power BI Desktop includes a Mobile layout view, which allows you to design a separate layout specifically for phones.

Key points:

  • Mobile layouts do not replace desktop layouts
  • They are optional but recommended
  • They apply when users view reports in the Power BI mobile app

To access:

View → Mobile layout

How Mobile Layouts Work

  • The mobile canvas is narrow and vertical
  • You manually select and place visuals
  • Visuals not added to the mobile layout won’t appear on mobile
  • Each report page can have its own mobile design

This gives report authors full control over:

  • Visual order
  • Size
  • Priority of information

Best Practices for Mobile Report Design

1. Prioritize Key Insights

Mobile screens support fewer visuals. Focus on:

  • KPIs
  • Summary metrics
  • High-level trends

Avoid overcrowding the page.

2. Use Single-Column Layouts

Vertical scrolling works best on mobile devices.

  • Stack visuals vertically
  • Avoid side-by-side layouts

3. Optimize Visual Types

Mobile-friendly visuals include:

  • KPI cards
  • Line charts
  • Bar/column charts
  • Simple tables

Avoid:

  • Large matrices
  • Highly detailed visuals
  • Small text-heavy charts

4. Increase Font and Element Size

Touch-based interaction requires:

  • Larger fonts
  • Bigger buttons
  • More spacing between visuals

5. Limit Slicers

Too many slicers reduce usability.
Recommended:

  • Use dropdown slicers
  • Place slicers at the top of the page
  • Consider using sync slicers for consistency

Interactions and Navigation on Mobile

  • Visual interactions (cross-filtering/highlighting) still apply
  • Drill-through works but should be clearly indicated
  • Bookmarks and buttons can be used but must be large enough for touch
  • Tooltips are supported but should be concise

Performance Considerations

Mobile devices often have:

  • Less processing power
  • Slower network connections

To improve performance:

  • Reduce the number of visuals per page
  • Avoid complex DAX calculations where possible
  • Limit high-cardinality visuals

Publishing and Testing Mobile Reports

After publishing:

  • Test reports using the Power BI mobile app
  • Verify layout consistency across devices
  • Confirm slicers, filters, and interactions behave as expected

Power BI Desktop does not emulate device-specific behavior, so real testing is essential.

Key Exam Concepts to Remember

For PL-300, be prepared to answer questions about:

  • When to use mobile layouts
  • Differences between desktop and mobile report views
  • Best practices for mobile usability
  • How visuals are added to the mobile layout
  • What happens when no mobile layout is defined

Exam Tip

If a question mentions:

  • Phones
  • Small screens
  • Executives on the go
  • Power BI mobile app

👉 The correct solution often involves designing or modifying a mobile layout, not changing the desktop report.

Summary

Designing reports for mobile devices ensures that Power BI content is:

  • Accessible
  • Actionable
  • Optimized for modern consumption patterns

For the PL-300 exam, focus on intentional layout design, usability principles, and understanding how Power BI separates desktop and mobile experiences.

Practice Questions

Go to the practice questions for this topic.

AI, Analytics, Artificial Intelligence (AI), Business Intelligence, Business Intelligence (BI) Development, Business Intelligence Platform, Data Analysis, Data Visualization, Power BI, Reporting

Choosing the Right Chart to display your data in Power BI or any other analytics tool

Data visualization is at the heart of analytics. Choosing the right chart or visual can make the difference between insights that are clear and actionable, and insights that remain hidden. There are many visualization types available for showcasing your data, and choosing the right ones for your use cases is important. Below, we’ll walk through some common scenarios and share information on the charts best suited for them, and will also touch on some Power BI–specific visuals you should know about.

1. Showing Trends Over Time

When to use: To track how a measure changes over days, months, or years.

Best charts:

  • Line Chart: The classic choice for time series data. Best when you want to show continuous change. In Power BI, the line chart visual can also be used for forecasting trends.
  • Area Chart: Like a line chart but emphasizes volume under the curve—great for cumulative values or when you want to highlight magnitude.
  • Sparklines (Power BI): Miniature line charts embedded in tables or matrices. Ideal for giving quick context without taking up space.

2. Comparing Categories

When to use: To compare values across distinct groups (e.g., sales by region, revenue by product).

Best charts:

  • Column Chart: Vertical bars for category comparisons. Good when categories are on the horizontal axis.
  • Bar Chart: Horizontal bars—useful when category names are long or when ranking items. Is usually a better choice than the column chart when there are many values.
  • Stacked Column/Bar Chart: Show category totals and subcategories in one view. Works for proportional breakdowns, but can get hard to compare across categories.

3. Understanding Relationships

When to use: To see whether two measures are related (e.g., advertising spend vs. sales revenue).

Best charts:

  • Scatter Chart: Plots data points across two axes. Useful for correlation analysis. Add a third variable with bubble size or color to generate more insights. This chart can also be useful for identifying anomalies/outliers in the data.
  • Line & Scatter Combination: Power BI lets you overlay a line for trend direction while keeping the scatter points.
  • Line & Bar/Column Chart Combination: Power BI offers some of these combination charts also to allow you to relate your comparison measures to your trend measures.

4. Highlighting Key Metrics

Sometimes you don’t need a chart—you just want a single number to stand out. These types of visuals are great for high-level executive dashboards, or for the summary page of dashboards in general.

Best visuals in Power BI:

  • Card Visual: Displays one value clearly, like Total Sales.
  • KPI Visual: Adds target context and status indicator (e.g., actual vs. goal).
  • Gauge Visual: Circular representation of progress toward a goal—best for showing percentages or progress to target. For example, Performance Rating score shown on the scale of the goal.

5. Distribution Analysis

When to use: To see how data is spread across categories or ranges.

Best charts:

  • Column/Bar Chart with bins: Useful for creating histograms in Power BI.
  • Box-and-Whisker Chart (custom visual): Shows median, quartiles, and outliers.
  • Pie/Donut Charts: While often overused, they can be effective for showing composition when categories are few (ideally 3–5). For example, show the number and percentage of employees in each department.

6. Spotting Problem Areas

When to use: To identify anomalies or areas needing attention across a large dataset.

Best charts:

  • Heatmap: A table where color intensity represents value magnitude. Excellent for finding hot spots or gaps. This can be implemented in Power BI by using a Matrix visual with conditional formatting in Power BI.
  • Treemap: Breaks data into rectangles sized by value—helpful for hierarchical comparisons and for easily identifying the major components of the whole.

7. Detail-Level Exploration

When to use: To dive into raw data while keeping formatting and hierarchy.

Best visuals:

  • Table: Shows granular row-level data. Best for detail reporting.
  • Matrix: Adds pivot-table–like functionality with rows, columns, and drill-down. Often combined with conditional formatting and sparklines for added insight.

8. Part-to-Whole Analysis

When to use: To see how individual parts contribute to a total.

Best charts:

  • Stacked Charts: Show both totals and category breakdowns.
  • 100% Stacked Charts: Normalize totals so comparisons are by percentage share.
  • Treemap: Visualizes hierarchical data contributions in space-efficient blocks.

Quick Reference: Which Chart to Use?

ScenarioBest Visuals
Tracking trends, forecasting trendsLine, Area, Sparklines
Comparing categoriesColumn, Bar, Stacked
Showing relationshipsScatter, Line + Scatter, Line + Column/Bar
Highlighting metricsCard, KPI, Gauge
Analyzing distributionsHistogram (columns with bins), Box & Whisker, Pie/Donut (for few categories)
Identifying problem areasHeatmap (Matrix with colors), Treemap, Scatter
Exploring detail dataTable, Matrix
Showing part-to-wholeStacked Column/Bar, 100% Stacked, Treemap, Pie/Donut

The below graphic shows the visualization types available in Power BI. You can also import additional visuals by clicking the “3-dots” (get more visuals) at the bottom of the visualization icons.

Summary

Power BI, and other BI/analytics tools, offers a rich set of visuals, each designed to represent data in a way that suits a specific set of analytical needs. The key is to match the chart type with the story you want the data to tell. Whether you’re showing a simple KPI, uncovering trends, or surfacing problem areas, choosing the right chart ensures your insights are clear, actionable, and impactful. In addition, based on your scenario, it can also be beneficial to get feedback from the user population on what other visuals they might find useful or what other ways they would they like to see the data.

Thanks for reading! And good luck on your data journey!