Data Storytelling for Business Professionals

Learn to turn data into a story by revealing underlying insights and actions, not just creating slides, using structure, context, action, and impact.

Explore the four-part data story framework—context, insight, impact, and action plan—and learn to frame problems, identify a key insight, and propose concrete actions.

Investigate the mystery of falling Zenly subscriptions by tracing churn to the trial package and influencer channel, then fix Jack's miscommunication and create a deck for corrective actions.

Learn to move from the generic big picture to small pieces to reveal insight, then segment problems and identify root causes to optimize acquisition, churn, and revenue.

Analyze a case study on a 10% Q2 revenue drop, uncovering EU phone sales decline caused by manual stock tracking, and propose changing logistics and implementing automated stock management.

Explore a multi-layer data analysis approach to uncover stock management and price control issues driving regional revenue and margin changes.

Learn to organize insights and drive action by starting with the problem, identifying the root cause through sequential analysis, segmenting data, and ending with clear recommendations and an action plan.

Learn to pick the right chart and tell the story with visuals that match your data, exploring chart types and case studies to guide the right approach.

Explore the chart selector guide and learn to choose the right Excel chart for your data, from bar and line charts to pie, histogram, box plots, and scatter plots.

Use a bar chart to compare budgets across channels like Google, Facebook, YouTube, and TikTok, since pie charts are hard to read and can mislead; selectively compare a few categories.

Use line charts to show trends over time, as with monthly recurring revenue from Jan to May. Avoid bar charts for time series because they break continuity.

Use a pie chart to show part to whole distribution of JetFlix's four plans—basic, premium, student, and family—across total active users, emphasizing proportions over a bar chart.

Use a boxplot to show salary distribution for data analysts across regions, highlighting the median, the first quartile and third quartile, min and max, and outliers.

Explore how to use histograms to visualize the distribution of daily steps in a fitness app, grouping steps into ranges and reporting mean and median.

Learn how scatter plots reveal the relationship between variables, showing time spent on a shopping app correlates with purchases, and why a line chart misleads with distinct users in Excel.

Learn how using the wrong chart can mislead decisions; compare the pie chart with the line chart to reveal trends and absolute values behind five-year revenue data.

Show how visuals reveal the correlation between number of features and customer spend with a scatter plot in Excel, enabling clear insight from large datasets.

Choose bar charts to compare distinct categories, line charts to show trends, and pie charts for proportions; use histograms or box plots for distribution, and scatter plots for relationships.

Identify common data visualization traps that mislead audiences, including truncated axes, percentage traps, normalization, and the use of averages or isolated data, to spot manipulation in data storytelling.

Learn how visuals can distort data even when accurate, and identify common pitfalls like truncating axes, percentage traps, and missing normalization to ensure truthful business storytelling.

Use truncated axes to emphasize changes by starting from nonzero values, making drops or gains look sharper and shaping the business narrative with visuals.

explain how percentage comparisons can mislead without absolute figures or reach, using conversion from campaigns A and B; emphasize keeping reach constant and asking for absolutes to compare performance accurately.

Identify and fix missing normalization by converting totals into per-user metrics, such as complaints per 100 users, to reveal true trends over time.

Isolate data and segment by restaurant and month to determine whether the new sixth location adds incremental revenue or dilutes the existing sales, using a control group.

Explore how relying on averages misleads promotions and forecasting. Analyze usage by data slabs to reveal how different segments respond to higher allowances and why distribution matters.

Learn to avoid data storytelling traps by normalizing metrics, isolating control groups, and showing full distributions. Use complaints per user to prevent misleading averages and percentages for business professionals.

Align your business story with your audience by tailoring language to their metrics and KPI focus, reflecting audience relevance, and delivering insights clearly.

Learn to tell executive stories by focusing on KPIs that matter, such as revenue run rate, churn, cash burn, and industry metrics like same-store sales and conversion rate by source.

Analyze SaaS KPIs by comparing actual monthly recurring revenue to targets, interpret churn rate trends and seasonality, and assess burn rate and runway to guide management decisions.

Identify and monitor kpis across industries, including onboarding funnel conversion, activation, value realization, ticket age, feature adoption, order fulfillment time, stock-out, cart abandonment, site uptime, wait times, and readmission rates.

Analyze SaaS operation KPIs across onboarding, tickets, and feature adoption, comparing signup-to-trial-to-purchase funnels, ticket age distribution, and adoption rates against past periods and benchmarks.

Apply the one message per slide rule to deliver a clear message, focusing on a key performance indicator or problem, like revenue status or unresolved tickets, avoiding mixed goals.

Explore real life data visual examples for complex datasets beyond standard charts, and learn to tell clearer stories with matrix views, color coding, and cross-sectional comparisons.

Tailor updates for executives and operations, focusing on strategy, trends, runway, cost of acquisition, recurring revenue, conversion, and bottlenecks, using dashboards to deliver one slide message with an actionable insight.

Discover how bias, framing, and context can mislead even accurate data, and explore cognitive bias, survivorship, cherry picking, and distortion with practical storytelling examples.

Bias shapes how audiences perceive data storytelling, guiding attention and decisions; beware confirmation, survivorship, and framing bias to avoid misleading narratives and drive accurate actions.

discover how bias shapes data storytelling through real-life cases: housing market optimism, survivor bias in WWII aircraft, and framing COVID news, and learn to consider unseen data.

Learn why cherry-picking data in storytelling misleads audiences and how showing all relevant data builds a truthful business narrative.

Explore three real-life cherry-picking cases in data storytelling, from stock market framing and political polls to marketing metrics, and learn to spot missing context and avoid manipulation.

Spot spin in data stories for business professionals using a simple framework of questions: who is missing, the timeframe, framing, and what is left out to judge trustworthiness.

Discover good examples of data storytelling that demonstrate precision, empathy, and impact through clear, context-rich, well-designed messages, featuring Spotify, Strava, and Google.

Explore how data storytelling uses personalized stats, emotion, benchmarking, and shareable visuals through Spotify Wrapped, Strava, and Google trends to engage audiences and reflect identity.

Identify bias in data storytelling, avoid cherry picking, and emphasize the full picture; personalize data to engage audiences and contextualize insights from platforms like Spotify and Strava.

Walk into any room and tell a data-driven story that moves people to act. Apply what you learned to your next presentation or report to transform conversations with data storytelling.