Alt Text Long Description

Figure 2.1: A data storytelling planning framework displayed as a structured grid worksheet with four labeled row categories along the left side in orange vertical text: CONTEXT, AUDIENCE, STORY, and SITUATION. Each row contains multiple labeled planning cells. CONTEXT row includes four cells: WHY (Purpose: how data is shown: trends, correlations, comparisons); WHAT (Relationship that needs changed; Key variables); HOW (Link to what needs changing); and SO WHAT? (Change or no change: consequences). AUDIENCE row spans the full width with two cells: WHO (Identify all stakeholders and their roles — who are the decision makers? Relevant, because?); and DECISION PROMPTS (What do decision-makers know already about this topic?). STORY row contains four cells: Structure/Framework (How is data used to frame the story? How is the story engaging?); Character (Motive and personalities); Problem (Link to what needs changing; Why change now?); and Delivery Plan (Sequence of events; Key insights). SITUATION row contains two cells: Design (Relevant format for visual content; Audience expectations); and Ten Criteria: a checklist including validity and relativity of data, data credible, support action/decision, focus on desirability/feasibility/viability, visuals use appropriate scale, data is insightful, data is explanatory and concrete, emotional connection, story arc supports retention and comprehension, and tested on pilot group.

Figure 2.2: A completed example of the data storytelling planning framework worksheet, using climate change as the subject matter. The grid is organized into four orange-labeled row categories: CONTEXT, AUDIENCE, STORY, and SITUATION with all cells filled in with specific content. CONTEXT row: WHY shows Purpose: (1) To show information is beautiful, (2) Beautiful information can save the world by triggering an emotional response to take action on climate change; visuals (heart) give the why. WHAT shows The best visualizations hit you emotionally; Google Street View & Pilot Data — anxiety of our children and makes us want to save the world. HOW shows Climate change shows where cities will be in the future (underwater); uses Google Street View & World Underwater.org by Carbon Story pilot data. SO WHAT? shows Change: start considering the impact of climate change on our grandchildren; No Change: image of our world at street level under water. AUDIENCE row: WHO shows Humans are the decision makers; relevant because it shows our children will face this issue if we do nothing. DECISION PROMPTS shows Decision makers are split on their belief about climate change; the call to action is somewhat difficult — is it awareness or a call to visualize climate change for others? (marked with circled 1). STORY row: Structure/Framework — references McCandless: Data is Beautiful (airplane crashes — human error), Google Ideas Global Arms Trade, StatBuilder map cities (art), Asteroid 3D near collision, Climate Change Visual main point; story connects emotionally. Character shows Uses Commander Data of Star Trek: The Planet; Save the humans. Problem shows Our complacency on climate change; the visual of Google Street View and Pilot data from McCandless shows what our view will be. Delivery Plan shows Beautiful visuals that show insight we can’t see with static data; Key insights: Data is beautiful, even artwork, and beautiful data triggers us emotionally (even scary). SITUATION row: Design shows Visuals are videos showing beautiful art although the topic might be global; audience expects to see beautiful data visualizations and only after seeing a visual of what our planet would be like and experiencing negative emotions do we realize change is needed. Ten Criteria shows All items checked “Y” (Yes) except “Tested on pilot group” which is marked “?” (circled 2).

Figure 2.7: A blue and white “Infographic Checklist” reference card subtitled “Quality – Basic Infographic”, displaying 11 evaluation criteria as a checklist with empty orange checkbox squares on the left side of each item. The criteria listed are:
1: Accurate, compelling data; identify source of data and clever, well-crafted (concise) text. User understands measure for success.
2: Creative data visualizations and compelling layout that communicates as intended. Consistent visual style that is visually appealing.
3: Contains a “call to action.” Includes social sharing buttons, hashtags, or links to website (if applicable).
4: Includes a descriptive title and/or short description. Sensitive to use people-first language and appropriate labeling.
5: Ethical use of scales and sensitive as to how the infographic triggers emotions.
6: No spelling errors or grammar errors. Images and icons used are cited and/or permission. Infographic developer includes citation — claiming their design.
7: Infographic should be timely and relevant. If not a timeless infographic, include date for context.
8: Pleasing and coordinated color schemes with appropriate branding like organization colors or organization logo.
9: If a comparison, equal real estate provided for each (as if split into two equal parts).
10: Awareness of file size, functional and specific media size. High-resolution images, contrasts are sharp, no blurriness.
11: Not cluttered, but enough to tell a story without additional narrative.

Figure 2.8: Flowchart diagram titled “Workflow process for Αρετή (Arete) Journal of Excellence in Global Leadership,” subtitled “From Submission to Publication.” The Arete journal logo, featuring a stylized olive branch and open book, appears in the upper right corner. The diagram uses gray rectangular boxes for process steps, teal/dark blue boxes for terminal outcomes, and blue diamond shapes for decision points, with arrows indicating the flow of the editorial process.The workflow proceeds as follows: Submission phase: Authors submit an article, case study, or essay. The submission undergoes a similarity check (KB). If it does not pass, a rejection letter is issued, with an option to suggest resubmission. If it passes, the process continues. Editorial review phase: A cover letter review is conducted by the editor, followed by a fit check by the editor. If the submission does not pass the fit check, it proceeds to assign and request peer reviewers. If it passes, the submission is anonymized as needed (KB) and proceeds to peer review. Peer review phase: The submission splits into two tracks: Double-Blind Review for research articles, and Single-Blind Review for case studies and essays. Peer review forms and comments are collected. If reviews are negative and the editor decides accordingly, a rejection letter is issued; if reviews are negative but the topic is considered acceptable, resubmission is suggested. If reviews are positive and the editor decides to accept, an acceptance letter is issued. Post-acceptance phase (right column): The author is contacted by phone and email, an author contract is sent, revisions are completed, an editor check-in with peer review occurs, followed by layout, copy-editing, and proofreading. The piece is then added to issue galleries and digital formats, published as an issue gallery PDF and digital page flipper, and finally promoted via social media outreach (“Saturate Social Media”).

Figure 3.1:

Figure 3.2:

Figure 3.3: Four scatter plots illustrating Anscombe’s Quartet, each showing X-Axis values from 0 to roughly 15-20 and Y-Axis values from 0 to roughly 12-14. Dataset I shows points scattered around an upward-sloping linear trend with moderate variability, consistent with a typical linear relationship. Dataset II shows points forming a smooth curved arc that rises and then falls slightly, indicating a clear non-linear relationship despite sharing similar summary statistics with Dataset I. Dataset III shows points following a tight linear trend except for one point at approximately (13, 12.5) that deviates well above the line, illustrating the effect of an outlier. Dataset IV shows most points clustered vertically at X equal to 8 with varying Y values, plus one outlier point at approximately (19, 12.5) that alone creates the appearance of a linear trend. Together, the four datasets demonstrate how datasets with nearly identical statistical properties (mean, variance, correlation) can have dramatically different distributions and underlying patterns when graphed.

Figure 3.4:

Figure 3.9:

Exhibit 3.1: Spreadsheet table titled ‘POM University – Horizontal Analysis,’ showing Ticket Sale Revenue, Ticket Sale – Net Income, and % of Income to Revenue as a Base Year (2010) and for each year from 2011 to 2023. Ticket Sale Revenue grows steadily from 169,522 in 2010 to a peak of 426,897 in 2017, then declines through 2020 (down to 188,011) before recovering to 813,111 by 2023. Ticket Sale – Net Income follows a similar pattern, rising from 67,809 to a peak around 177,111 in 2017, dropping to a negative value of -988 in 2020, then climbing back up to 318,201 by 2023. The % of Income to Revenue column shows percentages mostly in the low-to-mid 40s through 2019, dropping sharply to -1% in 2020, then recovering to 20% and 39% in subsequent years. A ‘Sparklines’ column on the right contains small inline line and bar charts for each row, visually summarizing the trend across years. Below the table, a callout box labeled ‘Sparklines’ with a zoomed-in view of the bar and line sparklines is connected to the table via a large blue arrow, highlighting this feature.

Figure 3.10:

Figure 3.46:

Figure 3.48: Radar (spider) chart titled “Global Cloud Computing Industry – Compare Industry, AWS, and Alibaba” displaying a Porter’s Five Forces competitive analysis for the global cloud computing industry. The chart has five axes radiating from the center, each representing one of Porter’s Five Forces, with a scale from 0 at the center to 5 at the outer edge. The five axes are labeled: Buyer Power (top), Degree of Rivalry (upper right), New Entrants (lower right), Substitutes (lower left), and Supplier Power (left). Three series are plotted as connected polygon outlines, identified in the legend: Industry (blue line with square markers) represents the overall industry average scores across all five forces. The polygon extends to approximately 3 on Buyer Power, 4 on Degree of Rivalry, 2 on New Entrants, 1 on Substitutes, and 2 on Supplier Power. AWS (orange line with square markers) represents Amazon Web Services’ specific competitive position. The AWS polygon extends to 3 on the Substitutes axis, 2 Supplier Power, 2 for buyer power, 4 for degree of rivalry, and 3 for new entrants. Alibaba (green line with square markers) represents Alibaba Cloud’s competitive position. The Alibaba polygon tracks closely to the AWS polygon across most dimensions, with slight variations, reflecting a similar but not identical competitive profile as the second major global cloud provider.

Figure 3.49: Infographic titled “A SWOT Analysis” with subtitle “Comparison of Amazon Web Services (AWS) and Alibaba Cloud (2022).” The layout uses a four-column design with arched banner headers in teal/green for S (Strengths), W (Weaknesses), O (Opportunities), and T (Threats), each containing two sections, one for AWS and one for Alibaba Cloud, presented in alternating red/coral and dark teal color blocks with bullet points. S is the Strengths column. AWS Strengths: support from parent company, end markets, territorial diversity. Alibaba Strengths: presence across the entire e-commerce value chain, focus on R&D, growth in revenue. W is the Weaknesses column. AWS Weaknesses: lawsuits. Alibaba Weaknesses: decline in cash position. O is the Opportunities column. AWS Opportunities: partnerships and agreements, Global Cloud Computing Market, Global IT Services Market. Alibaba Opportunities: strategic initiatives, software market in China, positive outlook for global online retail market. T is the Threats column. AWS Threats: cyberattacks and Security Vulnerabilities, intense competition, increasing IT complexity. Alibaba Threats: tightening fair compliance from sellers, intense competition, compliance with regulations.

Figure 3.50: Infographic titled “PESTLE Landscape of Greece, Year: 2023” displaying a comprehensive PESTLE analysis for Greece in a six-column table format, sourced from the PESTLE Country Analysis Report: Greece, March 2023, MarketLine, MarketLine Advantage Database. A small map of Greece with a location pin appears in the upper left corner. Each column has a colored header banner with an icon and category name, followed by bullet points of key factors below. Political (purple column): Parliament type of government, high level of freedom, corruption and nepotism, surveillance bill, deteriorating press freedom. Economic (blue column): 5.3% growth in 2022, rising debt, rising foreign investment, increasing tourism. Social (orange column): 23% over 65 years old, healthcare costs are high, low graduation and high dropout rates, electricity subsidized, public can’t afford it. Technological (pink/red column): Ranked 25 out of 27, new 5G network project, high tech exports decreased, cyber-attacks. Legal (green column): Tax rate at 22%, minimum wage set at $750.5 EUR/month, high inflation, low number of patents filed. Environmental (teal column): First climate law in 2022, CO2 emission increasing, renewable energy projects.

Figure 3.51: Diagram titled “Balanced Scorecard Framework” displaying the classic four-perspective Balanced Scorecard model used in strategic management. The layout is organized around a central box labeled “Vision and Strategy” with four quadrants surrounding it, connected by blue directional arrows indicating the interrelationships between perspectives. Each of the four quadrants contains a perspective label, a guiding question, and a table with four column headers: Objectives, Measures, Targets, and Initiatives (all blank, serving as a template). Financial (top center, blue border) asks the guiding question, “To succeed financially, how should we appear to our stakeholders?” Customer (left center, blue border) asks the guiding question, “Connect to vision — How should we appear to our customers?” Internal Business (right center, blue border) asks the guiding question, “To satisfy our stakeholders and customers, what business processes must we excel at?” Learning & Growth (bottom center, blue border) asks the guiding question, “To achieve our vision, how will we sustain our ability to change and improve?”

Exhibit 3.12: Completed Balanced Scorecard Framework diagram for “Global International Air (GIA)” with “Operating Efficiency” noted as the strategic theme. The diagram is organized into four horizontal rows representing the four Balanced Scorecard perspectives (Financial, Customer, Internal, Learning), each containing a strategy map on the left with color-coded oval/bubble diagrams showing causal relationships, and four columns on the right (Objectives, Measures, Targets, Initiatives) filled with specific content. Financial Perspective (top row, blue bubbles): Strategy map shows profitability (central blue rectangle) driven by lower costs and increase revenue (blue ovals) with arrows indicating causal flow. Objectives: increase profit; less planes; increase revenue. Measures: market value, per seat revenue, plane lease cost. Targets: 25% per year, 20% per year, 5% per year. Initiatives: Optimize routes, standardize planes. Customer Perspective (second row, orange/yellow bubbles): Strategy map shows more customers (large yellow oval) driven by on-time flights and lowest prices (orange ovals), with arrows connecting them. Objectives: flight is on-time, lowest prices, more customers. Measures: FAA on-time, customer ranking, new customers. Targets: first in industry, 98% satisfaction; % change. Initiatives: quality management, customer loyalty program. Internal Perspective (third row, orange bubble): strategy map shows improve plane readiness time (single orange oval) connected upward to the customer perspective. Objectives: fast ground turnaround. Measures: on ground time, on-time departure. Targets: less than 25 minutes, 93% on-time. Initiatives: cycle time optimization. Learning Perspective (bottom row, olive/brown bubble): Strategy map shows ground Crew Teams (olive/brown oval) as the foundational driver connected upward through the framework. Objectives: ground crew alignment ground crew trained. Measures: % Ground crew stockholders, ground crew trained. Targets: Year 1: 70% hold stock, Year 4: 90% hold stock, Year 6: 100% hold stock. Initiatives: Stock Ownership plan, ground crew training.

Figure 3.52: Creation of a Cause and Effect diagram. The application ribbon at the top shows the Insert tab selected, with standard tool groups including Pages, Illustrations, Diagram Parts, Links, Text, and other tools. Shapes panel (left): A shapes library panel is open on the left side, showing a search box at the top and several shape categories listed: More Shapes, Quick Shapes, Arrow Shapes, and a highlighted section labeled “Cause and Effect Diagram Shapes” containing: Effect, Category 1, Category 2 (highlighted with a red circle labeled “1” indicating it is selected or being referenced), Fish frame (highlighted in blue as the active/selected shape), Primary cause (left and right variants), and Secondary cause 1 through 6 (multiple variants). A red circle labeled “2” highlights the Primary cause shapes. The main canvas is the fishbone diagram in progress: A partially constructed fishbone (Ishikawa) cause and effect diagram is visible on the gridded canvas. The fish-shaped outline (fish frame) forms the structure of the diagram. The effect box on the right side of the fish head is labeled “Poor coffee experience,” illustrated with a small coffee cup image. Four diagonal bones extend from the central spine, with category labels at their tips: People (upper left), Procedures (upper right), Material (lower left), and Equipment (lower right). The diagram appears to be in an early stage of construction with the main bones placed but cause branches not yet fully populated.

Exhibit 3.17: Decision tree diagram illustrating a three-way business decision analysis for app development. The diagram flows from left to right, beginning with a blue square “Decision Node” on the far left, branching into three decision paths, each leading to a circular “Chance Node,” which then branches into three probabilistic outcome scenarios terminating at orange triangular “Endpoint nodes” with associated outcome values. Decision A, Make Game App (cost: -$80k): Chance Node with Expected Value: $97,500. Successful Launch 35% → Outcome $250,000. Modest Launch 50% → Outcome $150,000. Dismal Launch 15% → Outcome $100,000. (EV calculation: 0.35×$250k + 0.50×$150k + 0.15×$100k = $87,500 + $75,000 + $15,000 = $177,500 gross; net of $80k cost = $97,500.) Decision B, Make Financial App (cost: -$60k): Chance Node with Expected Value: $102,000. Successful Launch 60% → Outcome $200,000. Modest Launch 30% → Outcome $120,000. Dismal Launch 10% → Outcome $60,000. (EV calculation: 0.60×$200k + 0.30×$120k + 0.10×$60k = $120,000 + $36,000 + $6,000 = $162,000 gross; net of $60k cost = $102,000.) Decision C, Revamp Existing App (cost: -$40k): Chance Node with Expected Value: $80,000. Successful Launch 58% → Outcome $150,000. Modest Launch 40% → Outcome $80,000. Dismal Launch 2% → Outcome $50,000. (EV calculation: 0.58×$150k + 0.40×$80k + 0.02×$50k = $87,000 + $32,000 + $1,000 = $120,000 gross; net of $40k cost = $80,000.).

Figure 3.54:

Figure 3.55:

Figure 3.56:

Figure 3.57: TABLE

Exhibit 3.19: Decision tree diagram analyzing a “Host sporting event” decision with two bidding strategies (Decision A and Decision B), built in a diagramming application. The diagram flows left to right from a starting node labeled “Host sporting event,” branching into two main decision paths. Each path includes chance nodes (blue circles), endpoint nodes (orange triangles), and outcome values, with two summary calculation tables displayed on the right side. Decision A, Cost to bid: -$50,000. Chance Node branches into: Not Awarded = 0.7 → Endpoint node → Outcome: Out cost of bid (-$50,000) or Awarded = 0.3 → Cost of hosting (-$100,000) → Second Chance Node. Successful Host = 0.6 → Endpoint node → Outcome: $500,000. Unsuccessful Host = 0.4 → Endpoint node → Outcome: $400,000. Event A summary table (top right) shows Payoff Amounts / Prob / Calculated: $500,000 × 0.6 = $300,000, $400,000 × 0.4 = $160,000, subtotal = $460,000. Cost of hosting subtotal = $360,000. Probability × 0.3 = $108,000. Cost of bidding = $15,000 (appears to show -$50,000 context). Net Worth A = $58,000. Decision B, cost to bid: -$15,000. Chance Node branches into: Not Awarded = 0.1 → Endpoint node → Outcome: Out cost of bid (-$15,000) or Awarded = 0.9 → Cost of hosting (-$25,000) → Second Chance Node. Successful Host = 0.9 → Endpoint node → Outcome: $150,000. Unsuccessful Host = 0.1 → Endpoint node → Outcome: $100,000. Event B summary table (bottom right) shows Payoff Amounts / Prob / Calculated: $150,000 × 0.9 = $135,000, $100,000 × 0.1 = $10,000, subtotal = $145,000. Cost of hosting subtotal = $120,000. Probability × 0.9 = $108,000. Cost of bidding = $15,000. Net Worth B = $93,000.

Exhibit 3.29: The interface is in Preview mode (indicated by the blue “Preview” tab selected at the top center, with a red arrow pointing to it) with a “Data” tab also visible. Main chart area (left/center) is a line chart displayed showing multiple colored trend lines over time. The vertical axis shows values ranging from 0 to approximately 4,000,000,000 (4 billion), and the horizontal axis appears to show years or time periods. Multiple lines in different colors (blue, red, purple, dark blue, orange, yellow, green, teal, and others) show varying trajectories. Several lines start low and rise steeply toward the upper right, with one blue dotted line and one purple line reaching approximately 3,500,000,000–4,000,000,000 by the end of the time period. A red line rises to approximately 2,500,000,000. Several other lines (orange, yellow, green, teal, and others) remain relatively flat near the bottom of the chart, clustered below 500,000,000. The right panel are the chart settings. A configuration panel is visible with the following options: Selected template: “Line, bar and pie charts 27.3.1”; Chart type dropdown set to “Line chart”; Grid mode with “Single chart” selected (blue) and “Grid of charts” option; Height mode with “Auto,” “Standard,” and “Aspect ratio” options; Collapsed sections for Controls & filters, Colors, Lines dots and areas, Labels, X axis, Y axis, and Plot background. A search bar appears at the bottom of the panel. The top toolbar includes undo/redo buttons, zoom controls, and “Create a story” and “Export & publish” buttons in the upper right corner.

Figure 3.67:

Exhibit 3.36:

Figure 3.76: Human Development Center Data

Exhibit 3.44: Screenshot of the OECD Data Explorer Archive interface. A left sidebar shows filter categories including Country, Indicator, Sex, Age Group, and Time, with Time expanded to show checkboxes for years 2016 through 2020 and beyond. The main panel shows ‘Applied filters’ for Country (Greece, Japan, United States, OECD – Average), Indicator (Share of female managers), and Sex (Women), with ’36 data points’ noted below. A data table titled ‘Employment’ with the indicator ‘Share of female managers,’ Sex ‘Women,’ and Age Group ‘Total’ displays percentage values by country (Greece, Japan, United States, OECD Average) across years 2014 through 2020. Greece’s values rise from about 28.1% in 2014 to 31.4% in 2020; Japan’s values rise from about 11.4% to 13.4%; United States values rise from about 38.9% to 41% by 2020; OECD Average rises from about 31.3% to 33.8%. Toolbar icons above the table allow switching between Overview, Table, and Chart views, and options for Labels, Layout, Download, Developer API, and Full screen.

Exhibit 4.2:

Exhibit 4.4: Table as percentages:

Figure 4.15: Table: Icon Set Used for Conditional Format (Applied by Row)

Exhibit 4.5: Colorful graphic recording or visual note-taking illustration from an “Accreditation Roundtable” event hosted by the Global Leadership Institute (GLI), identified by the GLI logo in the lower left corner and the Big Paper Strategy logo in the lower right. The illustration is densely packed with hand-drawn text, icons, diagrams, and visual metaphors capturing key themes and discussions from the event, rendered in a vibrant mix of blues, oranges, yellows, greens, and purples on a white background. Major themes and text elements visible throughout the illustration include: “Comprehensive International Collaboration Framework”; “P20” (referenced with a timeline); “We need to sit down together and talk about what is missing in curriculum”; “Not just USA influences.” Top center: “Sustainability of Programs”; “Tough with leadership changes/evolution”; “What are we doing?”; “Somebody has to do the work!”; “To build Editing and Engaging Talent/Students”; icons suggesting books and learning; “Musts — Accrediting body fit within institution level accreditation”; “Some global students may have a smaller view of the world than we realize”; references to networks and standards; “The New Global System”; “Inclusive Standards”; “Encouraging Standards Globally”; globe illustration; references to historical perspectives and literature; “A Standard for Global Leadership” (prominent central text in a large banner); “Can we gather ideas across other disciplines?”; “Can we do minimum standard?”; “We all have biases — how do we check them and teach that?”; “Lesson Curation”; “Rigorous Experiential Learning”; “Breaking the Organizational Silos”; star/rating graphics; “Life Skills” (prominent); “Accreditation Roundtable” (large orange banner); references to service learning and mentorship. Bottom left has GLI, Global Leadership Institute logo with institutional branding.

Exhibit 4.9:

Exhibit 5.8: Gantt chart titled “XCaliber Food Distribution: Add & Modify Products” covering the period 2025–2026. The chart uses three color-coded bar types identified in the legend: Survey (dark navy/charcoal), Analysis (cyan/light blue), and Update System (yellow/gold). The horizontal axis shows bimonthly time periods from May/Jun through Mar/Apr, and the vertical axis lists eight project phases. The rightmost column identifies the responsible team or person for each phase. The eight project phases, their timing, activity types, and responsible parties are: New Product Strategy: Survey bar (short, May/Jun), Responsible: Matt/Sales Comm. Idea Generation: Survey bar (May/Jun) and Analysis bar (Jul/Aug), Responsible: Matt/Engineering. Screening: Survey bar (Jul/Aug through Sep/Oct), Responsible: Survey. Concept Testing: Survey bar (Sep/Oct) and Update System bar (Sep/Oct, short) — Responsible: Engineering. Business Analysis: Analysis bar spanning approximately Sep/Oct through Nov/Dec, Responsible: Sales Comm. Product Development: Analysis bar spanning approximately Sep/Oct through Jan/Feb (longest Analysis bar), Responsible: Sales Comm/Survey. Market Testing: Survey bar spanning approximately Nov/Dec through Mar/Apr (long bar), Responsible: Survey. Commercialization: Analysis bar (short, Mar/Apr), Responsible: Sales Comm.

Exhibit 5.9: Two side-by-side dot pattern diagrams, each forming the shape of a uppercase letter “a” constructed from colored circles. On the left, the letter “a” shape is formed using large red circles for the curved arch and descender of the letterform. A horizontal row of medium-sized blue circles crosses through the middle of the figure, representing a baseline or axis line. The color contrast between the red letter shape and the blue horizontal line is clear and distinct, making the two elements easily distinguishable. On the right, the same letter “a” shape is shown, but the color assignment of the dots is partially reversed or redistributed. The upper arch of the letter remains in red circles, but the lower portions of the letterform (the descender and lower curves) are rendered in blue circles matching the color of the horizontal baseline row, which now contains red circles on its outer ends. This mixing of colors between the letter shape and the baseline makes the two elements harder to distinguish visually, as the blue dots of the letter blend with the blue dots of the baseline.

Exhibit 5.13: Radar (spider) chart titled “Sales by Category” displaying sales performance across 15 food product categories for XCaliber Food Distribution, consistent with the companion horizontal bar chart showing the same categories. The chart has 15 axes radiating from a central point, each labeled with a category name, with concentric rings representing increasing sales values from the center outward. The 15 category axes arranged clockwise from the top are: Beverages, Sauces & Gravies, Jams/Preserves, Dairy Products, Dried Fruit & Nuts, High-end canned meat, Candy, Baked Goods & Specialty mixes, Pasta, High-end soups, Condiments, Oil, Fruit & Veg, Grains, and back to Beverages. A single blue outlined polygon connects the data points across all 15 axes. The shape of the polygon is highly irregular and distinctly non-circular, reflecting the uneven distribution of sales across categories: The Beverages axis shows by far the longest extension outward, reaching nearly to the outer rings of the chart, consistent with its dominant $130k revenue shown in the companion bar chart. Sauces & Gravies and Jams/Preserves also show notable but smaller extensions. The majority of the remaining categories, particularly Grains, Fruit & Veg, Oil, Condiments, High-end soups, Pasta, Baked Goods, Candy, High-end canned meat, and Dried Fruit & Nuts, show very short extensions close to the center, reflecting their relatively low sales values. The resulting polygon shape resembles a narrow teardrop or raindrop, elongated sharply toward the Beverages axis and compressed tightly toward the center for most other categories, visually emphasizing the heavy sales concentration in the Beverages category relative to all others.

Exhibit 5.16: Baseball pitch zone heat map displaying batting performance for right-handed batters (RH Batter tab selected) with a left-handed batter (LH Batter) tab also available. The visualization shows a grid of small colored squares representing different zones of the strike zone and surrounding areas, with a color-coded legend on the right labeled “Zone Score” ranging from Best (orange) to Worst (light blue). The heat map grid is approximately 13×13 squares, representing the full hitting zone from the batter’s perspective including the strike zone (outlined by a yellow/gold rectangle in the center of the grid) and the ball zones surrounding it. The interior of the strike zone and the areas immediately surrounding it show warm orange and dark orange/brown colors, indicating the highest batting performance zones, the pitches that right-handed batters hit most effectively. The core of the strike zone, particularly the middle and inner portions, shows the deepest orange coloring. Moving outward from the strike zone toward the edges and corners of the grid, the colors transition through progressively cooler tones, tan/khaki, gray-green, teal, and finally light blue at the outermost edges, indicating increasingly poor batting performance on pitches in those locations.

Figure 5.1: Horizontal bar chart titled “Demonstrating the importance of curriculum when selecting a graduate program” with the survey question “In general, which attributes are the most important to you in selecting a graduate program? Choose up to 3.” The horizontal axis shows percentage selecting each attribute from 0% to 80%. The eleven attributes and their selection percentages from highest to lowest are: Curriculum Interest: 73% (longest bar, highest rated attribute), Ph.D. & Experts in Field Teach: 55%, Emphasize action research: 40%, Colleague recommendation: 35%, The quick pace — 1 year: 20%, Hybrid & Fully Online Availability: 20%, The beauty of the grounds: 10%, Attended as undergrad: 8%, Career support: 5%, Affordability: 5%, Catholic: 3%. Two annotation boxes provide interpretive commentary: The first notes that “Survey shows that curriculum interest is the single most important dimension when choosing a graduate program.” The second notes that “The quick pace (one year) and the hybrid & fully online availability were hypothesized to be very important in the decision making process, were both cited less frequently as important attributes,” indicating these two factors performed below expectations in the survey results.

Figure 5.2: Horizontal bar chart titled “In general, which attributes are the most important to you in selecting a graduate program?” The horizontal axis shows percentage selecting each attribute from 0% to 80%. Two colors are used for the bars: black bars highlight the two attributes of special analytical interest, while gray bars represent the remaining attributes. The eleven attributes and their approximate selection percentages from highest to lowest are: Curriculum Interest: approximately 73% (black bar, longest — highest rated attribute), Ph.D. & Experts in Field Teach: approximately 55% (gray bar), Emphasize action research: approximately 40% (gray bar), Colleague recommendation: approximately 35% (gray bar) The quick pace — 1 year: approximately 20% (black bar, highlighted) Hybrid & Fully Online Availability: approximately 20% (black bar, highlighted), The beauty of the grounds: approximately 10% (gray bar), Attended as undergrad: approximately 8% (gray bar), Career support: approximately 5% (gray bar), Affordability: approximately 5% (gray bar), Catholic: approximately 3% (gray bar, shortest). Two annotation boxes provide interpretive commentary on the right side: The first states “Survey shows that curriculum interest is the single most important dimension when choosing a graduate program.” The second notes that “The quick pace (one year) and the hybrid & fully online availability were hypothesized to be very important in the decision making process, were both cited less frequently as important attributes,” with both terms in bold. The black color coding on these two bars and on the top bar visually emphasizes the key findings discussed in the annotations, distinguishing them from the other gray-coded attributes.

Figure 5.3: Side-by-side comparison of two line charts illustrating the effect of applying Gestalt design principles to data visualization, titled “Before Applying Gestalt Principles” (left) and “After Applying Gestalt Principles” (right). Both charts show the same data — monthly Order Received and Ticket Volume Processed figures across a full calendar year — but differ significantly in visual design and clarity. The left chart, “Before”: the chart has a cluttered appearance with several design issues: the vertical axis uses dollar sign formatting ($0.00 to $300.00) despite the data representing counts rather than currency; month labels are displayed diagonally at an angle making them harder to read; the two lines (blue for “Order Received” and orange for “Ticket Volume Processed”) are thin and similar in weight; a legend is placed below the chart requiring eye movement back and forth between the legend and the lines; the background has heavy gray horizontal gridlines; and the overall color scheme provides limited contrast between the two series. The two lines track closely together throughout the year, making differentiation difficult. The right chart, “After”: the redesigned chart demonstrates multiple Gestalt improvements: the vertical axis is simplified to clean numeric values (0 to 300) without unnecessary currency symbols; month abbreviations are displayed horizontally and legibly; the two lines are rendered in high-contrast colors — bold blue for “Order” and bold red for “Processed” with increased line thickness; direct labels (“Order” in blue and “Processed” in red) are placed adjacent to the lines at the right end of the chart, eliminating the need for a separate legend and using the Gestalt principle of proximity; gridlines are lighter and less intrusive; and the overall layout is cleaner and less cluttered. The same data patterns are visible but are now significantly easier to read and interpret.

Exhibit 5.17: Scatter plot chart titled “Perceived Astringency of Red Wine” displaying the relationship between tannin concentration and perceived astringency across approximately 35 data points. The horizontal axis is labeled “Tannin Concentration” ranging from 0.00 to 1.20, and the vertical axis is labeled “Perceived Astringency” ranging from -1.50 to 1.50. Individual data points are plotted as blue diamond markers scattered across the chart. A red dashed trend line (linear regression line) runs diagonally from the lower left to the upper right of the chart, indicating a clear positive correlation between tannin concentration and perceived astringency. At low tannin concentrations (approximately 0.20–0.35), perceived astringency values are predominantly negative, ranging from approximately -1.25 to -0.50, indicating wines with low tannin are perceived as less astringent. As tannin concentration increases through the mid-range (approximately 0.40–0.70), astringency values cluster around zero and into slightly positive territory. At high tannin concentrations (approximately 0.75–1.00), perceived astringency values are predominantly positive, ranging from approximately 0.10 to 1.05, indicating wines with higher tannin content are perceived as more astringent. The data points show moderate scatter around the trend line, indicating a moderately strong positive linear relationship between tannin concentration and perceived astringency, consistent with the well-established enological principle that tannins are the primary driver of astringency perception in red wines.

Exhibit 5.18: Scatter plot chart titled “Parking Revenue” displaying clustering of data points grouped into three labeled clusters, illustrating a segmentation or cluster analysis of parking revenue data. The vertical axis shows revenue values ranging from 0 to 2,500, and the horizontal axis shows a numeric scale ranging from 0 to 14. Three distinct clusters of blue data points are visually identified by rounded rectangular borders drawn around each group, with labels identifying each cluster. Conferences (upper left cluster) show five data points grouped in a rounded rectangle, with values ranging approximately from x=1 to x=5 and y=1,200 to y=2,200. The points show moderate spread in both dimensions, representing conference events that generate mid-to-high parking revenue. Craft Fairs (upper right cluster) show four data points grouped in a rounded rectangle, with values ranging approximately from x=10 to x=13 and y=2,100 to y=2,400. The points are tightly clustered at consistently high revenue levels, representing craft fair events that generate the highest and most consistent parking revenue. Summer Camps (lower center cluster) show four data points grouped in a rounded rectangle, with values ranging approximately from x=6 to x=9 and y=250 to y=700. The points are clustered at low revenue levels, representing summer camp events that generate the lowest parking revenue.

Figure 5.6: Educational diagram consisting of six panels, each titled “5 Adults out of 100 exercise 30 minutes/day,” illustrating six different preattentive visual attributes used in data visualization and visual perception theory. Each panel contains a field of similar symbols with exactly 5 target symbols embedded among approximately 95 distractor symbols, demonstrating how different visual properties allow the eye to instantly identify outliers without conscious counting. Top left is the Preattentive attribute. It’s shape is a grid of vertical line/pipe symbols (|) with 5 targets marked as § (section signs), scattered at irregular positions throughout the field. The § symbols are immediately distinguishable from the | symbols due to their different shape. Top center is Preattentive attribute. Its orientation is a grid of vertical line symbols (|) with 5 targets shown as diagonal or forward-slash symbols (/), scattered throughout the field. The tilted orientation of the target symbols stands out immediately from the uniform vertical orientation of the distractors. Top right is Preattentive attribute. Its hue is a grid of small black dots with 5 targets shown as red dots, scattered throughout the field. The color difference allows the red dots to pop out instantly from the black dot field. Bottom left is Preattentive attribute. Its enclosure is a grid of vertical line symbols (|) with 5 targets enclosed in small rectangles (□|□), scattered throughout the field. The bounding boxes around the target symbols make them immediately identifiable. Bottom center is Preattentive attribute. Its curvature is a grid of curved parenthesis/arc symbols ()) filling the entire field with no visible outliers. All symbols appear identical, suggesting that when all symbols share the same attribute value, no preattentive pop-out occurs, or the 5 targets are indistinguishable from distractors in this rendering. Bottom right is Preattentive attribute. Its add marks is a grid of vertical line symbols (|) with 5 targets shown as cross/plus symbols (+ or †) with an additional horizontal mark added, scattered throughout the field. The added horizontal mark creates a distinctive cross shape that stands out from the simple vertical lines. The diagram is a standard visual perception and data visualization educational illustration demonstrating that certain visual properties (shape, orientation, hue, enclosure, and added marks) enable rapid preattentive processing, the ability to identify targets instantly and effortlessly, making them powerful tools for highlighting key data points in charts and dashboards.

Figure 5.8: Educational infographic titled “Omitting the Baseline” on a teal/green background, explaining the concept of truncated graphs and baseline manipulation in data visualization. A dark header box contains the explanatory text: “In most cases, the baseline for a graph is 0. But writers can skew how data is perceived by making the baseline a different number. This is known as a ‘truncated graph’.” The infographic presents a side-by-side comparison of two bar charts showing the same EBIT and business segment data, labeled “Misleading” (left, with a frowning face icon) versus “Accurate” (right, with a smiling face icon), separated by a “VS” circle in the center. Data values shown in both charts (six categories): EBIT: 1,225 (blue bar); Chem: -600 (red bar); Mat: -178 (red bar); Tech: 139 (blue bar); Ag: 189 (blue bar); Other: -216 (red bar). Left chart shows Misleading (truncated baseline). The vertical axis does not start at zero. All bars appear above the baseline, making the negative values (Chem -600, Mat -178, Other -216) appear as positive upward bars differentiated only by their red color. The visual impression is that all segments are performing positively, with only color (red vs. blue) distinguishing losses from gains. The explanatory bullets note: “This design ignores the baseline. The designer assumes the user will notice that we will notice the colors (red as negative)” and “Very misleading. Color will not trump the baseline!” The right chart is Accurate (zero baseline). The vertical axis starts at 0, with positive values extending upward and negative values extending downward below the zero line. EBIT (1,225) extends far upward, while Chem (-600), Mat (-178), and Other (-216) extend clearly downward below zero, accurately conveying losses. The explanatory bullets note: “Starting the vertical axis at 0 offers a more accurate depiction of the data” and “It is understood that some products lost money. Color helps the reader in this case.”

Table 6.3:

Table 6.4:

*Mean Rank difference is significant at the 0.05 level.

Exhibit 7.11: A data storytelling planning framework organized as a grid with four orange row labels on the left: Context, Audience, Story, and Situation, and multiple content cells to the right of each label. Context row contains four cells: WHY (purpose: to tell a story to salespeople about XCaliber’s 2024 sales successes, challenges, and strategies for future goals); WHAT (relationship that needs changing — from part-time to full-time, with less isolation; key variables include inconsistent sales, niche opportunities, and payment system errors); HOW (what needs changing: consistent sales, wider product line, upselling, expanding customer base, same-day shipping, better support, frequent reporting and feedback); and SO WHAT? (consequences of no change: missed 2024 sales goal, missed expansion opportunities, customer dissatisfaction, isolation and communication issues). Audience row spans two wide cells: WHO (salespeople; relevant because the most important changes involve consistent selling, upselling, expanding into new states, same-day shipping, payment errors, and better support); and DECISION PROMPTS (what decision-makers already know: remote selling with no peer support, feelings of isolation, no monthly feedback, no additional sales incentives). Story row contains four cells: Structure/Framework (show sales by salesperson, consistency, product range, payment blanks, and KPIs; make the story engaging by inviting salespeople to be part of the solution); Character (Joe, a new XCaliber salesperson who lacks motivation incentives and feels isolated, but succeeds by knowing the product, keeping promises, joining weekly overviews, and mentoring others); Problem (consistent sales, new niche customers, system errors, same-day shipping; urgency driven by need to meet growth goals and become profitable); and Delivery Plan (use Power BI with bookmarks to tell the story; key insights include inconsistent selling, 15 customers in 15 states, 9 salespeople, 2.8-day shipping, missed sales goals, customer demand for beer and more frequent contact, one salesperson accounting for 22.4% of sales, need for upselling and monthly minimums). Situation row contains two cells: Design (Power BI report with visuals tied to Joe’s story; audience expects to see themselves in Joe’s experience and share their goals); and Ten Criteria (a checklist of data storytelling standards, with checkmarks indicating completion: validity and relativity of data, data credibility, support for action/decision, focus on desirability/feasibility/viability, appropriate visual scale, insightful data, explanatory and concrete data, emotional connection, story arc supporting retention and comprehension; “Tested on pilot group” is the only unchecked item).

Figure 7.26: Two-panel instructional screenshot demonstrating right-click drill-through functionality in a Power BI bar chart titled “Revenue by Month.” Left panel shows a bar chart with monthly revenue values on the y-axis (0–80K) and months on the x-axis (January through April partially visible). Bar heights include January at 47K, February (shorter, unlabeled), March at 65K, and a partial April bar. A tooltip is displayed over the January bar showing “Month: January, Revenue: 47,158.61” with a cursor hand icon. A yellow arrow points to text reading “Right-click to drill through,” prompting the user to interact with the chart. The right panel shows the same “Revenue by Month” bar chart (months January through July visible, with values including 47K, 47K, 23K, 37K, 59K, and 29K) with a right-click context menu open over the January bar. A red arrow and annotation reading “RIGHT CLICK TO DRILL THROUGH OR ANALYZE” point to the context menu, which displays options including: Show data point as a table, Show as a table, Include, Exclude, Drill through (with submenu arrow), Analyze (with submenu arrow), Group, Summarize, and Copy.

Figure 7.27: Power BI “Analyze” insight panel titled “Here are the filters that cause the distribution of Sum of Revenue by Month to change the most.” A “Category” section explains that three product categories most affect the distribution: Dried Fruit & Nuts (9.8% of records), Beverages (25.3% of records), and Sauces & Gravies (8% of records), among others. Three tab buttons allow switching between categories: “Dried Fruit & Nuts” (currently selected, shown in dark green), “Beverages,” and “Sauces & Gravies.” Below the tabs is a dual-axis bar chart comparing monthly revenue patterns across all 12 months (January through December) on the x-axis. The left y-axis (0–10K) measures “Sum of Revenue for Dried Fruit & Nuts” shown as solid blue bars. The right y-axis (0–150K) measures overall “Sum of Revenue” shown as gray dashed-outline bars representing the total for context. Key observations from the chart: Dried Fruit & Nuts revenue peaks sharply in December (approximately 10K), with a secondary peak in February (approximately 7K) and June (approximately 4K). The overall revenue (gray bars) also peaks in December at approximately 150K. Most other months show relatively low Dried Fruit & Nuts revenue between 0–2K.

Figure 8.5: A screenshot of Microsoft PowerPoint displaying a Microsoft Power BI report embedded on slide 1 of 2. The report is titled “XCaliber Sales Summary” and contains a navigation menu on the left with tabs: XCaliber Foods, Product Revenue, Revenue Trend, Decomp Tree, and KPI & Goals (currently selected). The dashboard shows four data visualizations: a line chart of Revenue by Month across January–December with a dashed trend line around 50K; a bar chart of Revenue by Customer showing Neighbor Grocers as the highest earner among roughly 15 customers; a pie chart of Revenue by Payment Type broken down into Credit Card (48.13%), Cash (41.19%), Check, and Blank (7.86%); a gauge chart showing 2024 Revenue vs. Year Target at 510.33K out of a 1.02M goal; a KPI card showing a 2024 Goal per Customer of 40K; and a card showing 2024 Actual Revenue Compared to Target at 510.33K against a $600K goal (–14.95%). The status bar at the bottom indicates “Live data” last updated 3/13/24 at 12:57 PM. The slide panel on the left shows two slides, with slide 2 labeled with a circled number 3.

Figure 8.9: A chart titled “Select Cultural Dimensions Related to Communication” comparing four countries: India (IN, purple), USA (US, green), Korea (KR, blue), and Germany (GR, yellow) across five cultural dimensions. Each dimension is represented as a horizontal spectrum with opposing labels on the left and right, and the four country icons plotted along a scale between them. Red lines connect each country’s positions across all dimensions to show their overall cultural profile. The five dimensions and country placements are: Low Context vs. High Context: USA and Germany lean Low Context, India and Korea lean High Context. Direct Negative Feedback vs. Indirect Negative Feedback: Germany and USA lean Direct, India and Korea lean Indirect. Consensual Deciding vs. Top-Down Deciding: Germany and USA lean Consensual, India and Korea lean Top-Down. Task Based vs. Relationship Based: USA and Germany lean Task Based, Korea and India lean Relationship Based. Confrontational vs. Avoids Confrontation: Germany and USA lean Confrontational, Korea and India lean Avoids Confrontation. The dimension labels in the center of the chart read: Communicating, Evaluating, Deciding, Trusting, and Disagreeing.

Figure 8.21: An infographic titled “Ethical Considerations in Data Visualization” on a dark background. The introduction states that ethical considerations play a crucial role in data visualization, as it involves presenting data in a way that is fair, transparent, and respectful, and that addressing ethical concerns is essential to ensure the integrity and credibility of information. Below, four key considerations are listed in color-coded rows, each with a label, description, and icon. Data Privacy (blue): respecting user privacy and safeguarding sensitive data is crucial for trust and rights protection, illustrated with a padlock icon; Precision (yellow): presenting data accurately and truthfully, without manipulating visuals, is crucial to prevent misleading interpretations, illustrated with a balance scale icon; Diversity (red): ensuring inclusive data visualization represents diverse perspectives and demographics promotes fairness and avoids biases, illustrated with geometric shape icons; Transparency (teal): clear explanations of data sources, methodologies, and limitations in visualization promote transparency and accountability, illustrated with a database icon.

Exhibit 8.4:

Instructor Solution and Resource Guide:

2.1 Exercise 1 Instructor Notes:

Potential Solution for Example 1: CONTEXT includes the Why, What, How, and So What? sections. WHY: Purpose. Data needs to be enjoyed when looking at health from 1810 to now in an animated and interesting, fun way. How is data shown (trends, correlations, comparisons). WHAT: Relationship that needs changed–showing that lower income countries have poorer health. Key variables: income is correlated to successful births. HOW: link to what needs changing–differences between the world (income) still impacts sick. SO WHAT?: Change or now change–consequences. It is in the interest of the world to note the consequences (health and live birth success) if less income). AUDIENCE includes WHO and DECISION PROMPTS. WHO: identify all the stakeholders and their role–who are the decision makers? Relevant, because? Identified by country–showing remarkable progress with a converging world. DECISION PROMPTS: What do decision-makers know already about this topic? 200 countries worth of data show that the power countries have poorer health, but this is an animation that shows the trend since 1810. STORY includes structure/framework, character, problem, and delivery plan. Structure/Framework: How is data used to frame the story? Or make your point? Data is shown in trends and it is animated so it is very engaging. How is the story engaging? Hans Rosling seems to be a part of it. Character: Motive and personalities. Hans Rosling interacts with the data as it is animated and projected over him. Problem: Link to what needs changing. As a stakeholder, we need to understand how wealth impacts health. Why change now? The world is converging but there are still countries that struggle. Delivery Plan: Sequence of events: starts with data projected as if Hans is writing the data. Key insights: shows a correlating trend of wealth to successful births. SITUATION includes Design and Ten Criteria. Design: Relevant format for visual content. The design is interesting, but it is shadowing and appears to be done in an old warehouse. This could be updated. More interesting to show when a pandemic hit or World War II hit. Audience expectations: impactful data that means more as you see the correlation and trends come alive. Ten criteria: validity and relativity of data, data credible, support action/decision, focus on desirability, feasibility, and viability, visuals use appropriate scale, data is insightful, data is explanatory and concrete, emotional connection, story arc supports retention and comprehension, tested on pilot group (all ten checked).

Potential Solution for Example 2: CONTEXT includes the Why, What, How, and So What? sections. Why: Purpose–to be able to see how different countries are meeting the SDG’s. How is data shown (trends correlations, comparisons). User picks a country and then can click through SDG’s. WHAT: Relationship that needs to be changed–this shows scores and relevant indicators. Key variables: SDG’s by country. HOW: Link to what needs changing. Unless a user understands the timeline to meet the SDG’s, they may not quite get this report. SO WHAT?: Change or no change–consequences. I see this could be helpful to use with other social and cultural data. The 17 SDG’s don’t mean as much unless you are comparing different countries. AUDIENCE includes WHO and DECISION PROMPTS. WHO: identify all the stakeholders and their role–who are the decision makers? Relevant, because? This is based on a 2022 report, and the stakeholders include the citizens of the country. DECISION PROMPTS: What do decision-makers know already about this topic? Decision makers may know the 17 SDG’s as it relates to their country but may not know how they compare to other countries. STORY includes structure/framework, character, problem, and delivery plan. Structure/Framework: How is data used to frame the story? Or make your point? The SDG’s are a measurement and provide a score. How is the story engaging? Since the user has to click to see interaction, the story is not clear. Character: Motive and personalities. There is no character other than the user interacting with the report as they explore the data. Problem: Link to what needs changing–the user should be interested in seeing how their country is meeting the SDG goals. Why change now? These are high level goals worth pursuing. Delivery Plan: Sequences of events–the user controls the sequence. Key insights–the user is guided to click a country and then an SDG and a country profile report is available. SITUATION includes Design and Ten Criteria. Design: Relevant format for visual content. Visually sound. Easy to see and understand. Audience expectations–audience expect unbiased metrics, but a user may not always understand the indicators and how they impact the score. Ten criteria: validity and relativity of data (checked), data credible (checked), support action/decision (checked), focus on desirability (checked), feasibility (checked), and viability (checked), visuals use appropriate scale (checked), data is insightful (checked), data is explanatory and concrete (checked), emotional connection, story arc supports retention and comprehension, tested on pilot group.

Potential Solution for Example 3: CONTEXT includes the Why, What, How, and So What? sections. WHY: purpose–to show how Americans die and the peaks when certain events happened. How is data shown (trends, correlations, comparisons). These are animated graphs. WHAT: Relationship that needs changed–mortality rate should be improving for ages 45-54 but it is not even though cancer and heart disease have become less deadly. Key varies are mortality rate, AIDS, suicide, drugs, murder by firearm. HOW: link to what needs changing–not much shown on the how–this seems to be more of an awareness. Perhaps this starts with an awareness of the concern for ages 45-54. About one-third of all deaths are people 85 and older. SO WHAT?: Change or not change–consequences. The downside to living so long means that dementia, Alzheimer, and senility is on the rise for those about 75. AUDIENCE includes WHO AND DECISION PROMPTS. WHO: identify all the stakeholders and their role–who are the decision makers? Relevant, because? Stakeholders are those taxpayers, healthcare industry and families caring for parents or dealing with suicide. Government–organizing Medicare benefits. DECISION PROMPTS: What do decision-makers know already about this topic? Decision makers know and are aware of suicide, drugs, and murder, but may not be aware of the trends in mortality for ages 45-54. STORY includes: structure/framework, character, problem, delivery plan. Structure/Framework: how is data used to frame the story? or make your point? The data is visualized, along with a narrative with each graph. How is the story engaging? It raises more questions. Character: motive and personalities. Bloomberg is branded on all the materials, so I’m assuming this is the character. Bloomberg is well-respected, so it seems more credible. Problem: link to what needs changing–a review of mortality rate of 45-54 age group, and a strategy needed for fighting dementia, Alzheimer and senility. What change now? the over 75 population are more likely to need health care coverage. Delivery Plan: Sequences of events–mortality stop improving tin the mid-1990s. Key insights: life expectancy has improved the most for under 25, but has deteriorated for age 25-44. Key points: suicide, AIDS, drugs, and disease like Alzheimer. SITUATION includes Design and Ten Criteria. Design: relevant format for visual content–animated slides with narrative. Audience expectations–audience expects data to be understandable. This feels like an awareness campaign. It is obvious that the U.S. has some issues that need a strategic plan and goals. Ten criteria: validity and relativity of data (checked), data credible (checked), support action/decision, focus on desirability (checked), feasibility (checked), and viability (checked), visuals use appropriate scale (checked), data is insightful (checked), data is explanatory and concrete (checked), emotional connection (checked), story arc supports retention and comprehension (checked), tested on pilot group.