Popular charts for storytelling in Power BI

Data storytelling is at the intersection of three components - visuals, data, and narrative.

• Visuals: These are charts or graphics that illustrate the data and the story, helping the audience to see and grasp the information quickly.
• Data: This is the information that supports the story, providing evidence and insights.
• Narrative: This is the story itself, which ties the visuals and data together, giving them context and meaning.

This intersection is crucial for effective storytelling, as it allows users to convey their message in a clear, concise, and memorable manner. By combining these elements, users create a powerful and engaging story that the audience can easily connect with and remember. This concept is discussed extensively in Brent Dykes' book, "Effective Data Storytelling."

Data and storytelling

When it comes to data and storytelling, data is truly the foundation of effective storytelling. During the process of data analysis, we typically clean the data, analyze it, and uncover insights. This process is often iterative; as we discover insights, we dive back into the data for deeper exploration and further insights.

This iterative process allows us to delve deeper and refine our insights, ensuring that the stories we tell are rooted in data.

Narrative and storytelling

Humans are natural storytellers, a trait that dates back to ancient times when we gathered around campfires to share tales. You might wonder what narrative structure has to do with data, but it is crucial in organizing and presenting our data stories effectively. 

Narrative structure, a concept from literary analysis, breaks down stories into their component parts.  It involves the sequence of events, the relationships between characters, and the way the story unfolds. Effective narrative structure is crucial for engaging the audience and conveying the message or theme of the story.

Key elements of narrative structure

One classic example of narrative structure is Freytag’s Pyramid, which outlines a series of events leading to a climax and resolution. This structure has the following elements.

• Exposition: The exposition sets the scene and introduces the main characters and setting.
• Rising Action: The rising action builds tension and conflict, leading to a climax.
• Climax: The climax is the most intense and dramatic moment in the story, where the conflict reaches its peak.
• Falling Action: The falling action resolves the conflict and brings the story to a close.
• Resolution: The resolution ties up loose ends and provides a sense of closure.

For instance, in the story of Hansel and Gretel, the siblings encounter various events culminating in a climax where they escape from the witch, followed by a resolution where they find their way home with treasure.

Data storytelling pyramid

Applying narrative structure to data involves organizing and presenting data in a way that tells a story. Brent Dykes’ data storytelling pyramid, adapted from Freytag’s Pyramid, is an effective framework for this purpose.

This is one framework that is used for creating effective data stories. It involves five main components:

• Setting: This provides context and background, enabling the audience to understand the data and its relevance. It sets the stage for the story.
• Hook: A notable observation or turning point that captures the audience’s attention. This element introduces the main narrative and engages the audience.
• Rising Insights: These are the supporting details that lead up to the central insight. They build the narrative, providing evidence and depth to the story.
• Central Insight: The main takeaway or the most significant finding in your data. This is the climax of your data story, the key message you want your audience to remember.
• Resolution: This part addresses the actions to be taken next. It provides a conclusion and suggests steps based on the central insight.

Storyboard vs. Sequential Slides

Several techniques have been developed for presenting stories.  Two common forms of presentation are storyboards and sequential slides.

Storyboard

A storyboard presents the key insight along with supporting details on a single canvas. This method allows the audience to see all the information at once, providing a comprehensive view of the story. However, this can sometimes overwhelm the audience as they may not know where to focus.

Sequential Slides

Many experts prefer sequential slides, where the story is broken down into manageable points across multiple slides. This approach helps the audience absorb and understand the story better, as they can focus on one part at a time. Sequential slides guide the audience through the narrative step-by-step, enhancing engagement and comprehension.

By using sequential slides, storytellers can control the flow of information and highlight the most important points, ensuring that the audience stays engaged and retains the key insights.

Visuals and storytelling

Visuals are a powerful tool in storytelling, allowing complex data to be communicated quickly and effectively. By transforming raw data into compelling visuals, we can create narratives that resonate with audiences, making the information more accessible and memorable. Throughout this ebook, we will delve deeper into the intersection of visuals and storytelling, exploring how various chart types and visualization techniques can be used to craft engaging and insightful data stories.

Storytelling tends to be confused with data visualization, but simply creating a chart is not enough to tell a story! There are various differences between storytelling and data visualization. Let's explore their unique roles and how they complement each other.

Storytelling is always explanatory

The purpose of storytelling is always explanatory, meaning it aims to explain a message or convey information to the audience. The focus is usually on one central insight that the audience needs to understand.

On the other hand, data visualization can serve both exploratory and explanatory purposes. Exploratory data visualization involves using visual elements to analyze and discover multiple insights from data, while explanatory data visualization is used to present and explain specific insights to others. For example, during the data exploration phase we might discover that a competitor's new campaign led to a 30% drop in sales among many other patterns and trends. Explanatory analysis involves taking this insight, explaining its significance and convincing our audience of its importance.

What value does storytelling add?

Storytelling enhances data analysis in three key ways:

1. Context: Storytelling provides context for data and insights, helping the audience understand the significance and relevance of the information being presented. By setting the scene and providing background, storytelling makes the data more relatable to the audience.
   
   
2. Causal relationships: Storytelling helps to illustrate causal relationships between different data points and insights. By presenting data in a narrative format, storytelling can show how one event or factor leads to another, helping the audience understand the underlying causes and effects.
   
   
3. Emotional value: Storytelling adds emotional value to data by making it more engaging and memorable. By tapping into the audience's emotions and creating a connection between the data and the audience's experiences, storytelling makes the information more impactful and meaningful.

When do we step beyond visualization?

According to Brent Dykes’ "Effective Data Storytelling," the need for storytelling occurs at the "Story Zone," which is characterized by two key conditions:

1. Relatively high insight complexity: Insight complexity can be understood as the difficulty of understanding or accepting the insight. When insights are intricate or hard to grasp, storytelling becomes essential to explain and contextualize them effectively. Additionally, Dykes discusses that insights can also be "hard" when they bring about significant change. Decision-makers can be resistant to change, making these insights challenging to accept, even if they are easy to understand.
   
   
2. Relatively high potential payoff: The payoff is the potential value or impact of the insight. If the insight has significant potential value or impact, storytelling can help emphasize its importance and relevance to the audience.

In simpler cases where insights are easy to understand or have low potential value, storytelling may not be necessary, and visualization alone may suffice.

Understanding the components of a good story is crucial for effective data storytelling. These components can be applied to any chart used in storytelling to enhance its impact and clarity.

Focus attention using pre-attentive attributes

Pre-attentive attributes are visual elements that can be used to draw the audience's attention to specific parts of the story. These visual cues—length, size, width, shape, curvature, and beyond—serve as the storyteller's toolkit.

A good story leverages these attributes to focus attention on the right message, guiding the audience to the intended interpretation of the data.

Consider an employee evaluation chart for John Smith. At first glance, it may be challenging to discern the story within the data from the below chart.

However, in the visual below, attention is drawn to one metric through the use of colour. This can influence the message of the story. Here, is the story that John Smith met all his deadlines, as shown below?

Or is it that his poor communication skills negatively impacted team performance?

By strategically leveraging pre-attentive attributes such as color, a narrative gains the power to guide the audience's gaze, emphasize key points, and evoke the desired emotional responses, laying the groundwork for an immersive and impactful storytelling experience.

Use visual hierarchy to your advantage

Visual hierarchy involves the deliberate arrangement of elements within a storyboard, prioritizing certain elements over others to create a sense of order and importance.

Consider the image below:

Most people would likely read the text in the order presented. This example demonstrates the concept of visual hierarchy. In storytelling, especially in storyboards, understanding and implementing visual hierarchy can significantly enhance the impact of your narrative.

Storyboard structures often follow a pattern where viewers tend to scan either in the form of a "Z" or a reverse "N." By strategically placing the most important elements where viewers look first and arranging secondary elements afterward, you can effectively guide the audience's attention and ensure that your story is conveyed in a clear and compelling manner.

Understanding and leveraging visual hierarchy can make your storytelling more engaging and impactful, ensuring that your audience absorbs the key messages you intend to convey

The single sentence rule

One of the most important components is the ability to summarize the conclusion of the story in a single sentence. This directs the story towards a single, important insight. If the conclusion cannot be summarized in a single sentence, it means that the story has not been fully developed yet.

Actionable insight is a key finding from data that provides a clear path to decision-making or specific actions. When we have such central insight, it is crucial to gear the story towards action. This actionable insight should be clear and concise, providing the audience with a clear understanding of what they can do or how they can apply the information being presented.

For example, the visual below succinctly conveys the narrative, indicating that the business ranks second lowest in customer service satisfaction, underscoring the need for improved staff training.

This section will focus on the intersection between storytelling and data visualization. We will explore the simplicity and effectiveness of popular storytelling charts and their practical applications.

Line charts

Line charts are powerful tools for storytelling, especially when conveying changes over time or across continuous variables.  Here are some insights that line charts can effectively convey:

• Change over time: Line charts are used to show how a variable changes over time, providing a clear visual representation of trends and patterns.
  
  
• Overtaking values: Line charts can be used to compare the values of different variables over time, highlighting when one variable overtakes another.
  
  
• Comparing rate of change (slope): Line charts can be used to compare the rate of change (slope) between different variables, providing insights into the acceleration or deceleration of trends.
  
  
• Change in continuous variables: Line charts can effectively display changes in continuous variables such as distance, stock price, or currency value, offering a clear visual of trends and patterns.

By understanding these key points, users can effectively use line charts to tell a story and convey meaningful insights to their audience. Consider the chart below showing the trend in the share of the female population in South Asian countries – Nepal being the first and Maldives dropping to last in recent decades due to the influx of male migrant workers.

Bar charts

Utilize the straightforward nature of bar charts to convey narrative insights effectively, presenting data in a visually engaging and easily understandable format.

Bar charts can be used to examine trends over time or compare different categories. However, they are most effective when highlighting a relatively small number of metrics. This is because their discrete form allows for accurate comparison of these metrics.

 Bar charts are effective visuals for visualizing the following insights:

• Draw attention to a specific metric: Bar charts are used to draw attention to a specific metric, such as the highest or lowest value in a dataset.
  
  
• Trend over time: Column charts can be used to show a trend over time, providing a clear visual representation of changes in a variable over time.
  
  
• Comparison between categories: Bar charts can be used to compare categories, such as comparing the sales of various products or the performance of different teams.
  
  
• Showcase relatively few metrics: Bar charts are well-suited for highlighting a few metrics, such as comparing the performance of a few key metrics.

Stacked bar charts

Stacked bar charts offer a comprehensive visual depiction of data by stacking segments atop one another, allowing for easy comparison of individual and total values within categories.  They can be used to visualize the following observations:

• Impact on total of major contributors: Stacked bar charts excel in illustrating how major contributors influence the total, offering a visual breakdown of each component's contribution to the overarching total, facilitating a clear understanding of their significance. The chart below is an example of comparing categories.

• Sharp change in contribution: Stacked bar charts are effective in highlighting sharp changes in contribution, whether it be sudden spikes or declines in the contribution of specific components. The chart below shows the sharp increase in the number of deaths in conflicts between 1992 and 1995 due to one-sided violence.

• Change over time in totals/contribution: Stacked bar charts provide insights into changes over time in totals or contributions, offering a visual narrative of how these metrics evolve over different time periods. The example below provides a narrative of the increasing usage of digital devices in the US over the years.

• Nominal comparisons: Stacked bar charts allow for nominal comparisons between different components and can for example enable easy visualization of the contributions of various teams or departments.

For instance, the chart below offers a nominal comparison of the cause of deaths in America, their frequency in Google searches, and their coverage in media reports.

You can see that the red bar representing terrorism dominates in media coverage. However, when we look at the actual causes of death, terrorism is barely visible. This stark contrast highlights the discrepancy between media coverage and actual data.

Stacked bar charts like this can be used effectively to make nominal comparisons, revealing how different factors contribute to a whole and highlighting disparities in contribution across different metrics.

Waterfall charts

Waterfall charts provide a clear and concise visual representation of cumulative changes in data over time or across categories, making complex trends and transitions easily comprehensible.

Waterfall charts serve as valuable tools for pinpointing the drivers behind trends in data, shedding light on the specific factors influencing changes over time.

Simple Waterfall Charts

The simplest form of a waterfall chart shows individual contributions along with the total. The chart below illustrates the degree of customer churn and the diverse factors influencing fluctuations in customer numbers.

• Visualizing cumulative change over time: By visually depicting cumulative changes over time, waterfall charts provide a clear narrative of how a variable evolves, facilitating easy interpretation of trends and patterns.

Nested Waterfall Chart

The nested waterfall chart emphasizes hierarchical totals and subtotals. For example, H1 revenue can be divided into the first two quarters, with subtotals shown in blue. This can further be divided into different markets, such as domestic and international.

This type of chart shows cumulative change over time, as we see totals for Q1 and Q2 and then the overall total.

Combination Waterfall Chart

A more advanced type of waterfall chart is the combination waterfall, which allows for performance evaluation using both variance values and absolute values. For instance, this chart can compare the number of downloads in terms of the plan and actual values, providing insights into performance metrics.

• Assessing performance using variance values: Waterfall charts are instrumental in evaluating performance by visualizing variance values, enabling stakeholders to assess deviations and identify areas for improvement or optimization.

The chart below illustrates the number of downloads, comparing the actual and planned values, and highlighting the variance between them. This offers a narrative insight into the performance of the download metrics.

• Highlighting deviations between categories: Waterfall charts highlight deviations between categories, allowing for a clear visual comparison of how different components contribute to the total, thus facilitating insightful analysis and decision-making.

Breakdown Waterfall Chart

A breakdown waterfall chart breaks down totals into contributors and their subtotals. For example, yearly revenue can be broken down into quarters and the contributing income streams for each quarter. 

Waterfall charts, with their ability to break down and visualize data changes and hierarchies, are powerful tools for data storytelling.

The chart below offers a visual narrative, demonstrating that if losses from existing accounts were eliminated, revenues would exceed $300 million. 

Stacked Breakdown Waterfall Chart

The stacked breakdown waterfall chart is a variation that provides a segmented breakdown of totals. For example, when looking at revenue, the bars can be segmented into current customers and new customers. Through segmented breakdowns, waterfall charts offer a comprehensive view of how distinct components contribute to the total, aiding in understanding the distribution of data across various categories. 

This allows for a more detailed analysis, showing both the breakdown of totals and the segmentation within those totals. The chart below depicts the revenue breakdown from various sources, distinguishing between new and existing customers, thus providing us with a narrative.

Slope graph

Slope graphs, introduced by data visualization expert Edward Tufte, are particularly useful for telling before and after stories.

Here are some insights that slope graphs can effectively convey:

• Before/After stories: Slope graphs are ideal for illustrating before/after stories, clearly showing how a variable has changed over a specific period, providing a straightforward visual comparison.
  
  
• Deviations from the trend: Slope graphs are useful for identifying deviations from the trend, such as outliers or anomalies in a dataset, allowing for easy recognition of points that diverge from the expected pattern.

The chart below illustrates that Seating was the only category with a decrease in sales when comparing Q1 with Q2.

• Change in rankings: Slope graphs effectively depict changes in rankings, such as shifts in a company's market share over time, making it easy to visualize and track these transitions.

 The chart below presents a narrative of individuals' rankings of the importance of 18 traits they look for in a spouse, comparing the years 1939 with 2008.

• Rate of change comparisons: Slope graphs facilitate comparisons of the rate of change between different variables, such as sales and revenue, highlighting which variables are increasing or decreasing more rapidly. The steeper the slope of the line, the greater the rate of change.

Treemap

Here are the types of stories that treemaps can tell:

• Identifying key players: Treemaps are excellent for pinpointing the key players in a dataset, such as the largest contributors to a total. This allows for a quick visual identification of the most significant elements.
  
  
• Hierarchical subtotals: Treemaps effectively illustrate hierarchical subtotals, clearly depicting how various components and their subcomponents contribute to the overall total, facilitating an understanding of the data's structure.
  
  
• Contribution to the whole: Treemaps provide a clear visual representation of how different components contribute to the whole, making it easy to see the relative size and importance of each component within the total dataset.

The chart below shows global CO2 emissions by region. The US, represented in green within North America, stands out as a significant contributor to global emissions. Treemaps also allow us to see hierarchical subtotals, such as the total for each continent as well as the contribution of the individual countries.

Additionally, treemaps enable us to compare contributions to the whole, providing a clear visual representation of the data's structure and the relative size of each element. This makes treemaps an effective tool for analyzing and communicating complex data in an intuitive format.

Marimekko charts

Marimekko charts, also known as mosaic plots, visually represent data composition and relationships by varying both the height and width of bars according to numerical values. Combining aspects of bar and stacked bar charts, Marimekko charts illustrate how components contribute to the entirety of a dataset.

 Here are the key insights that Marimekko charts can convey:

• Identifying key players: Marimekko charts are effective for identifying the key players in a dataset, highlighting the largest contributors to the total. This makes it easy to visually spot the most significant elements.

For example, the chart below highlights the top 5 tech companies in the market, comparing the number of employees to the amount of revenue they generate, providing a narrative for storytelling. Amazon stands out with the highest revenue and number of employees, while Apple leads in revenue per employee, as indicated.

• Composition of the total: Marimekko charts can illustrate how various parts add up to the total, offering a clear visual breakdown of the composition. This helps in understanding the relative proportions of each component.
  
  
• Cost-benefit analysis: Marimekko charts are useful for performing cost-benefit analyses, providing a visual comparison of the costs and benefits of diverse options. This aids in making informed decisions by clearly displaying the trade-offs.

Marimekko charts are often used in the fields of sustainability and climate change as Marginal Abatement Cost (MAC) Curves. A MAC Curve shows the cost-effectiveness of various carbon reduction strategies, each represented as a bar, with bar heights representing marginal costs and bar widths indicating the potential for abatement. This is, in fact, a type of Marimekko chart called a variable-width bar chart.

In the chart below, both 'Switch to LEDs' and 'Waste Heat Recovery System' show negative values in their marginal abatement costs, indicating cost savings and providing a strategy for the company to implement sustainability measures. This sorting distinctly emphasizes the economic and environmental benefits of the cost-saving strategies.

100% Marimekko Chart or Mosaic Chart

Another type of Marimekko chart is the 100% Marimekko chart, also known as the mosaic chart.  For instance, a 100% Marimekko chart can show market capitalization by brand and region, providing insights into our brand's performance relative to competitors. This type of chart is useful for understanding the proportionate contributions of different components to the overall total, offering a clear visualization of how each part stacks up against the whole.

Scatter plots

A scatter plot is a type of chart that shows the relationship between two variables plotted on the horizontal and vertical axes using points on the plane. The position of each datapoint in relation to the axis determines its value with respect to each variable. Scatter plots can highlight simple linear relationships, complex interactions, or even the absence of any correlation, providing a clear and intuitive way to analyse and interpret data.

Scatter plots can be used to tell multiple types of stories:

• Linear relationship between two variables: Scatter plots are ideal for displaying the relationship between two variables, such as illustrating the correlation between sales and revenue. This allows for easy identification of patterns and trends such as with linear relationships where an increase or decrease in one variable corresponds to a proportional change in the second variable.
  
  
• Scatter plots can also depict more complex relationships between two variables, including non-linear relationships or interactions. This helps in understanding multifaceted connections within the data.
  
  
• Non-relationships: Scatter plots are useful for showing the absence of a relationship between two variables, providing a clear visual representation when there is no correlation. This aids in confirming or refuting hypotheses about the data.

For example, the scatter plot below illustrates the relationship between population growth rate and median age for countries around the world. Each point represents a single country, where countries with higher median ages tend to have lower population growth rates, and vice versa.

Bubble Chart

Similar to a scatter plot, a bubble chart expands on visualizing relationships between two variables by incorporating a third dimension—represented by the size of each data point.

For example, consider examining median age versus births per woman across different countries, where each bubble's size corresponds to the population of the country as shown in the chart below.

This additional variable enhances the depth of analysis possible with the chart, offering insights into multiple dimensions simultaneously.

Bubble charts are valuable tools in data visualization, allowing for the exploration of complex datasets and the identification of patterns that may not be as apparent in traditional two-dimensional plots.

Bullet charts

The bullet chart, pioneered by data visualization expert Stephen Few, is tailored for comparing performance against a target. It consists of three main parts: a bar representing the performance metric, a marker indicating the target benchmark, and qualitative bands behind the bar. This composition provides a clear visual representation of whether goals have been met, how metrics have performed (good, average, or bad), and which metrics need urgent attention. Bullet charts provide valuable insights into data:

• Meeting or missing targets: Bullet charts effectively display whether targets have been met or missed, offering a straightforward visual representation of performance against specific goals.
  
  
• Performance assessment: Bullet charts are useful for assessing the performance of a metric, indicating whether it is good, average, or bad based on predefined thresholds. This allows for a quick evaluation of current standings.
  
  
• Urgent attention metrics: Bullet charts help identify which metrics require urgent attention, particularly those that fall significantly below target, enabling immediate focus on critical areas.

For example, in the South region below, the target of 55 was exceeded with a value of 70. In contrast, the North region fell short by 20.  

IBCS Style Bullet

The IBCS style bullet chart, following the International Business Communication Standards, normalizes target markers across metrics, i.e., sets the target markers at the same level and scales the bar proportionately for each metric. This normalization allows for a clear comparison of performance deviations as percentages from the target.  This style enhances clarity in assessing performance against targets, facilitating more informed decision-making and strategic planning based on precise performance insights.

Integrated Variance Column

The integrated variance column in a bullet chart enhances clarity by integrating the performance and the target value into a single bar and showing their variance as a color-coded segment, making it immediately clear whether the target has been exceeded, met, or missed. By visually emphasizing these differences, the integrated variance column provides deeper insights into performance metrics, helping stakeholders quickly assess performance success or shortfall against set targets.

Box and whisker plot

Box and whisker plots are effective tools for visualizing the distribution and variability of a dataset. By presenting key summary statistics such as the median, quartiles, and outliers, they offer insights into the central tendency and spread of the data. Boxplots can be used to visualize the following:

• Reading degree of dispersion:  The dispersion of datapoints is reflected in a box plot by the range between the minimum and maximum values, indicated by the whiskers. A longer Inter-Quartile Range (IQR) or a larger range suggests a wider spread in the values, while a shorter IQR or range indicates datapoints that are more closely clustered together. 
  
  
• Skewness of data: Box plots indicate if data is symmetric or skewed, where symmetric data has a balanced distribution around the median and skewed data shows a longer tail on one side of the median, highlighting any asymmetry in the distribution.
  
  
• Locality of data: Box plots reveal where data points lie, providing insight into central tendencies like median and quartiles.
  
  
• High-level overview and comparison of data:  Box plots offer a high-level overview and facilitate the comparison of multiple series by providing a visual summary of key statistical measures. Analysts can compare multiple box plots side by side to identify differences in central tendency, spread, and variability between datasets. The box plot's simplicity and clarity make it easy to compare distributions, detect outliers, and understand the overall shape of the data.

For example, the boxplot below shows the distribution of customer ages across four products. We gather that Product 2 attracts a younger demographic, while Product 4 appeals to an older demographic among the four products, covering a demographic range from 18 to 65.

Range, dumbbell, and arrow charts

Range, dumbbell, and arrow charts are effective tools for visualizing and comparing two measures. Range charts depict the spread of data between minimum and maximum values, providing insight into variability.

Dumbbell charts display the comparison between two data points, often highlighting changes or differences over time or between categories.

Arrow charts visualize the direction and magnitude of change between two data points, making trends and comparisons easy to interpret. Each chart type offers a clear and concise way to convey information and insights to the audience.

Range Charts

Range charts provide a visual representation of the magnitude and direction of change in data over time, highlighting trends and movements in values with clarity.

Dumbbell Charts

Dumbbell charts excel in comparing data with a reference value, allowing for easy assessment of performance relative to the reference point, whether it's a target or benchmark.

Arrow Chart

Arrow charts are effective at emphasizing the movement of values over time, highlighting changes in the data and providing a clear visual representation of trends and directional shifts.

Funnel charts

Funnel charts are visually compelling tools for illustrating the progression of data through various stages of a process. Typically, the chart is wider at the top to represent the initial stage with the highest volume of data or prospects, and then narrows down towards the bottom to signify the final stage with the lowest volume or conversion rate.

This format allows for easy visualization of the funnelling effect, making it simple to identify areas of potential improvement or optimization within the process.

• Progression through funnel stages: Funnel charts effectively visualize the progression through funnel stages, displaying the number of customers transitioning from one stage to the next, facilitating a clear understanding of the conversion process.
  
  
• Identifying drop-off points: Funnel charts are valuable for pinpointing where drop-off occurs, highlighting which stage in the funnel experiences the highest rate of customer attrition, aiding in the identification of potential bottlenecks or areas for improvement.
  
  
• Opportunities for retention rate improvement: This allows for targeted strategies aimed at optimizing the conversion process and retaining more customers.

Traditional Funnel Chart

The traditional funnel chart, commonly used in sales and marketing analytics, visualizes the progression of stages in a process, such as a sales funnel. In this vertical funnel chart, each stage represents the conversion rate from one step to the next—like converting email subscribers into product purchasers. This chart helps identify where drop-offs occur in the conversion process, highlighting areas for improvement in conversion rates and customer retention strategies.

Executive Funnel Chart

Another variation is the executive funnel chart, shown horizontally. This version allows for a detailed and precise analysis of progression through funnel stages in a linear format. For instance, in a sales funnel context, it enables a clear view of how prospects move from initial interest to final purchase or drop-off. Understanding these dynamics helps businesses pinpoint opportunities to optimize conversion rates and enhance overall sales effectiveness.

Waffle charts

Waffle charts offer a visually appealing way to represent data by using a grid of small squares or rectangles to visualize the proportions or percentages of a whole. Each square or rectangle represents a unit of the data, allowing for a quick and intuitive understanding of the distribution or composition of the data set. Waffle charts can effectively visualize many messages:

• Parts-to-whole visualization: Waffle charts visually depict the parts-to-whole relationship between various categories, offering a clear representation of how each part contributes to the whole.
  
  
• Showcase notable proportions: Waffle charts highlight notable proportions, allowing for the emphasis of significant contributors to a total, making it easy to identify key components within the dataset.
  
  
• Alternative to funnel charts: Waffle charts serve as an alternative to funnel charts for visualizing progress through the funnel, providing a different yet effective visual representation of the data flow.
  
  
• Category value versus target value visualization: Waffle charts visualize the category value versus the target value, offering a clear representation of how the actual value compares to the target value

For example, the waffle chart below tracks job applications from CV selection to final selection, ideal for storytelling about the hiring process. This is a funnel adapted from Storytelling with Data.

Using text effectively

Text is best used when there are very few metrics to compare. Using text allows you to focus on a small number of key metrics without overwhelming the audience with too much visual information. Texts can be used to tell multiple types of stories:

• Summarize the main take-away using text: Concisely summarizing the key message or insight using text helps to reinforce the main point and ensure that the audience understands the significance of the data.
  
  
• Use pre-attentive attributes like colour, weight, size, etc. to drive your message home: Leveraging pre-attentive attributes, such as using bold text for emphasis or varying font sizes to create a visual hierarchy, can help to draw the audience's attention to the most valuable information and make the message more impactful.

Tables and heatmaps

• Tables provide a straightforward way to organize and display data in rows and columns. They are versatile and can accommodate several types of data, making them suitable for presenting numerical values, text, or a combination of both. Heatmaps, on the other hand, visualize data using color gradients where each cell represents a value, making it easy to spot patterns, trends, and variations across large datasets at a glance. Tables and heatmaps are effective visuals for visualizing the following insights
  
  
• Trend in one category, column, or row: Tables are adept at displaying trends within a single category, column, or row, offering a clear visual representation of patterns or changes in the data over time or across different variables.

• Identifying exceptional values or outliers: Heatmaps are useful for highlighting exceptional values, whether they are unusually low or high, within specific cells of a dataset.

By using colour gradients to represent the magnitude of data points, heatmaps make it easy to visually identify outliers or areas of interest that deviate significantly from the overall pattern. For example, we can spot that projectors & screens purchased in the United States are outliers shown in dark green due to the high purchase volume.

By utilizing the advanced charting capabilities of Inforiver Analytics+ for Power BI, you can create a variety of visually compelling and informative charts that are ideal for storytelling. Analytics+ offers over 100+ charts tables and KPI cards that enhance your storytelling capabilities in Power BI.

Download your free copy of Inforiver Analytics+ here, and then follow just 2 simple steps to create your desired charts! You can download this PBIX file to explore hands-on examples of all the charts mentioned.

Executive Funnel Charts

For instance, let's create an executive funnel in three straightforward steps:

Step 1: Drag and drop the required fields into the Axis and Values fields within Power BI's interface on the right. 

Step 2: Use the Chart Type dropdown menu on the toolbar to select the desired variation (in this case, Executive funnel) from the Special charts section of the  Inforiver Analytics+ toolbar.

Step 3: Click on the Sort option in the menu to choose the desired order of your visual.

💡Quick tip: You can enable Labels to display the variance between each column of an executive funnel chart  to enhance your storytelling.

In the example below, observe the drop-off rates between each stage of the funnel. Clicking on these labels allows you to toggle between displaying absolute values and showing drop-off rates as percentages.

Your funnel chart is now ready!

Lollipop Charts

Next, we will see how to create a lollipop chart using Inforiver Analytics+ in Power BI in 2 simple steps. The lollipop chart is a space-saving and visually appealing alternative to the traditional bar chart.

• Step 1: Add one category to the Axis field and the corresponding values (e.g., Profit - Actual) to the Values field.

• Step 2: Use the Chart Type dropdown menu on the toolbar to select the desired variation (in this case, lollipop chart) from the Special charts section of Inforiver Analytics+.

💡Quick tip: In the chart type menu, the orientation of the chart can be changed, in this case, to a horizontal lollipop chart, or from the rotate option in the main menu as well.

Dot Plot

A dot plot provides a clear and concise visual representation of data distribution and comparisons. Let’s see how to create one in Power BI using Inforiver Analytics+.

• Step 1: Drag and drop the required fields into the Axis and Values fields within Power BI's interface. In this case, let's put "Region" under Axis and "Profit - Actual"," Profit - Forecast" and "Profit - Target" under Values.

Step 2: Under the horizontal orientation, choose the dot plot chart type from the bullet chart section of Inforiver Analytics+.

We can observe that we can easily compare the three regions in a single chart. In this case, you may notice that only the South region has exceeded the targeted and forecasted profit.

💡 To bring this region into focus for effective storytelling, we can use the customization feature called a reference band. Here's how to do it:

Step 1: Navigate to the Story section -> Analytics -> Reference Band.

Step 2: Change the orientation of the reference band and apply it to only the third metric.

Step 3: Rename the band according to your needs, in this case, “South exceeds target and forecast”.

This allows you to highlight key insights and improve the clarity of your data storytelling.

Note: The downloadable ebook includes detailed explanations on creating various other storytelling charts and features using Analytics+. Take a look!

By selecting the appropriate chart type to convey your message, you can craft a compelling narrative that captivates your audience and reinforces your main points. Whether you are emphasizing trends over time, comparing various categories, or showcasing exceptional values, choosing the right chart enables you to tell a story that resonates with your audience and leaves a lasting impact.

With the ability to engage and inform, these charts play a vital role in transforming raw data into meaningful insights that drive informed decision-making and facilitate deeper understanding.

Inforiver Analytics+ for Power BI offers a diverse selection of 100+ chart types, ranging from those designed to highlight trends and compare categories to those perfect for displaying exceptional values. With such a comprehensive toolkit at their disposal, storytellers can effectively convey their message and present their data with clarity and impact.

Check out our webinar replay on "Popular charts for storytelling".