At a glance
- Combines data, narrative structure, and visualization into a single message.
- The main goal is a decision or an action, not entertaining the audience.
- Data analysis searches for insights; data storytelling conveys them.
- In management reporting, orientation, causes, and signals matter more than dramaturgy.
- DeltaMaster supports data storytelling with clear coloring, comments, graphical tables, and AI-generated short texts.
What would you like to know about Data Storytelling?
Your input is sent to an AI service to answer it and is stored to improve the answers. Please do not enter any personal data. Privacy policy
Data storytelling definition
Data storytelling refers to the preparation of data analyses as a comprehensible line of argument with a beginning, a key message, and a conclusion. A data story answers not only the question "What do the figures show?" but also "What does that mean?" and "What do we do now?".
The best-known model comes from analytics expert Brent Dykes. He describes data, narrative, and visualization as the three central elements of data storytelling, which only unfold their effect in combination. A visualization alone is therefore not yet a data story.
In a corporate context, data storytelling sits between data analysis and decision. Controllers, analysts, and business departments use the method to prepare results in an understandable way for management, the executive board, or the supervisory board.
„Since we prepare our monthly reports with DeltaMaster as a data story, the sales management recognizes the decisive variance on a single page. The action title and the comments lead us directly to the cause instead of through 40 KPIs.“Head of Sales Controlling
Industrial company
What do companies need data storytelling for?
Companies benefit from data storytelling when reports are complete but do not trigger a decision. Typical symptoms are report piles, long meetings with fundamental discussions about figures, and recipients who overlook the decisive variance.
In its projects, Bissantz observes: many companies do not have a data problem but a communication problem. The data is available in the data warehouse, but the key message gets lost in the mass of KPIs.
Trusted by market leaders and hidden champions
What is the main goal of data storytelling?
The main goal of data storytelling is a well-founded decision or action on the part of the recipient. Effective data stories, used correctly, can influence decisions and initiate change.
This results in three sub-goals: the recipient should grasp the key message quickly, understand the cause, and recognize the next measure. Entertainment or aesthetic effect is not a goal.
Bissantz holds a clear position here: data visualization for business intelligence should not entertain executive boards with journalistic data storytelling, but show where targets were met or missed, analyze causes, and provide signals. For reporting, data storytelling is therefore a tool for orientation, not for staging.
What is the difference between data analysis and data storytelling?
Data analysis searches for insights in data, data storytelling conveys these insights to decision makers. Analysis is exploratory and open, data storytelling is explanatory and focused on a single statement.
| Criterion | Data analysis | Data storytelling |
|---|---|---|
| Guiding question | What is in the data? | What does it mean, and what do we do? |
| Approach | Exploratory, open-ended | Explanatory, focused on a core message |
| Target audience | Analysts, controllers | management, departments, committees |
| Scope | Many hypotheses, many analyses | Few, selected pieces of evidence |
| Result | Insight | Decision or measure |
| Typical methods | Data mining, variance analysis, root cause analysis | Core message, narrative structure, annotation, commentary |
Both disciplines build on each other. Without sound analysis, the data story lacks a foundation. Without data storytelling, a good analysis often remains without impact.
What are the 6 elements of data storytelling?
The six elements of data storytelling denote the essential elements of a data story: data foundation, core message, explanatory focus, linear sequence, dramaturgical elements, and visual anchors.
| Element | Meaning | Implementation in reporting |
|---|---|---|
| 1. Data foundation | The statement is based on verified facts. | Uniform data basis, verified data quality |
| 2. Core message | One central insight instead of many details. | Meaningful report title as the core message |
| 3. Explanatory focus | Explain the how and why, not just the what. | Reveal causes, for example via drill-down |
| 4. Linear sequence | Evidence builds up step by step. | Report booklet from overview to detail |
| 5. Dramaturgical elements | Provide context: what came before, why is it relevant? | Prior-year and plan comparison, time series |
| 6. Visual anchors | Visualizations support the narrative but do not replace it. | Graphical tables, sparklines, color signals |
Charts should illustrate the story but not tell it on their own. In controlling, this means: a report without a core message and commentary remains a collection of numbers.
What steps does data storytelling involve
Data storytelling follows a clear process from the question to the recommendation for action. The sequence prevents visualizations from being created before the statement is established.
| Step | Guiding question | Result |
|---|---|---|
| 1. Clarify audience and occasion | Who decides about what? | Clear addressee and decision need |
| 2. Check and analyze data | Are the figures complete and correct? | Verified analysis results |
| 3. Formulate the core message | What is the one most important insight? | One sentence that carries the statement |
| 4. Select and order evidence | Which figures support the statement? | Logical sequence from overview to detail |
| 5. Choose the presentation | Which form shows the statement most clearly? | Table, graphical table, time series |
| 6. Derive a recommendation for action | What follows from this? | Concrete measure with a responsible person |
| 7. Review and reduce | What can be removed without harming the statement? | Concise, focused report |
The most important step is the core message. Anyone who cannot formulate it in a single sentence has not yet completed the analysis.
What techniques are there in data storytelling
Data storytelling uses techniques for structure, text, and visualization. The following overview shows common techniques and their benefit in business intelligence.
| Technique | Description | Use in controlling |
|---|---|---|
| Action title | The heading states the core message instead of just the topic. | “Revenue gap is due to the South region” instead of “Revenue October” |
| Pyramid principle | First the conclusion, then the reasoning. | Board proposals, management summary |
| Data storytelling arc | Narrative arc of initial situation, rising insights, key insight, and solution. | Project and strategy presentations |
| Martini-glass structure | First a guided narrative, then free exploration by the user. | Interactive reports with drill-down |
| Annotation and commentary | Short texts explain anomalies directly on the report. | Variance comments in the monthly report |
| Emphasis through color | Color only for signals, for example red and blue for negative and positive variances. | Plan-actual comparison |
| Small multiples | Identically structured small charts allow direct comparisons. | Regions, product groups, stores |
| From overview to detail | First the overall view, then the causes at deeper levels. | Cost centers, customers, items |
Bissantz explicitly recommends a restrained color choice, since the eye distinguishes red and blue more easily than red and green for physiological reasons. Uniform color rules are a technique of data storytelling because recipients recognize signals without a legend.
What mistakes often occur in data storytelling
The most common mistakes in data storytelling arise when form becomes more important than the statement. The following table shows typical mistakes and how companies avoid them.
| Mistake | Consequence | How you avoid the mistake |
|---|---|---|
| No core message | Recipients have to search for the statement themselves. | Give every report an action title |
| Chart first, statement later | The presentation determines the message. | Formulate the core message before the visualization |
| Selecting data to fit the story | Biased decisions, loss of trust | Conduct the analysis open-endedly, check counter-evidence |
| Too many colors and effects | Signals get lost in the decoration. | Use color only for variances |
| Dramaturgy instead of orientation | Presentations entertain but do not clarify. | Put objectives, variances, and causes at the center |
| Inconsistent presentation | Every report has to be read anew. | Define uniform notation and scaling |
| No recommendation for action | The insight remains without consequence. | Name the measure and the responsible person in the commentary |
A particularly critical mistake is selective data selection. Anyone who uses data only to confirm a preconceived opinion is engaging in opinion-making, not data storytelling.
Application examples: where is data storytelling used?
Data storytelling is used in many areas of controlling. The following examples show how clear key messages make decisions easier.
Where would you like to use Data Storytelling?
Your input is sent to an AI service to answer it and is stored to improve the answers. Please do not enter any personal data. Privacy policy
Practical example: data storytelling in the monthly report with Bissantz
A typical scenario from sales controlling shows how data storytelling works with DeltaMaster from Bissantz. The figures are illustrative.
Starting situation: The monthly revenue of an industrial company is 4 percent below plan. The previous report lists 40 KPIs, and sales management does not recognize the cause.
Analysis: The controller investigates the variance in DeltaMaster, from total revenue across regions down to individual customers. The navigation methods reveal that the gap arises almost entirely in the South region, caused by two major customers with postponed orders.
Preparation as a data story:
| Element | Implementation in DeltaMaster |
|---|---|
| Key message | Action title: "Revenue gap arises in the South region due to two postponed major orders" |
| Context | Graphical table with plan, actual, and prior year per region, supplemented by sparklines for the twelve-month trend |
| Signal | Red and blue mark negative and positive variances |
| Evidence | Linked detail report with the affected customers |
| Action | Comment: clarify delivery dates with both customers by month-end |
| Timeliness | Dynamic report summaries adjust automatically at the next monthly close |
AI support: With AI reporting in DeltaMaster, comments are created partly automatically. The AI prepares the key messages of the reports as easy-to-understand short texts, and the Deterministic Analytics Engine provides all AI-determined KPIs with an audit trail. The data story thus remains verifiable.
Result: Sales management grasps the cause on one report page instead of in 40 KPIs. The meeting addresses the measure, not the search for the variance.
| Function | Benefit |
|---|---|
| Action title as the key message | the report title names the central insight instead of just the topic |
| Graphical tables and sparklines | plan, actual and prior year including trend can be grasped at a glance |
| Color signals in red and blue | negative and positive variances are recognizable without a legend |
| Comments and annotations | causes and measures appear directly on the report |
| AI-generated short texts with an audit trail | key messages are created automatically and remain verifiable |
„The uniform notation and the color signals make our reports instantly readable, without us having to discuss the figures first in every meeting. The AI-generated short texts with an audit trail save us time and remain verifiable.“Head of Financial Controlling
Food producer
Frequently asked questions
What is data storytelling explained simply?
Data storytelling, explained simply, means: explaining figures so that everyone immediately understands what happened, why it happened and what needs to be done now. Bissantz describes data storytelling in reporting as a combination of a clear key message, a few suitable figures and a short comment.
Is data storytelling manipulative?
Data storytelling is not manipulative as long as the analysis is conducted with an open outcome and the presentation reflects the data correctly. Bissantz warns against techniques from media opinion-making, such as distorted axes or selective data selection, and recommends a consistent, rule-based notation for data storytelling in controlling.
Do controllers need data storytelling?
Controllers benefit from data storytelling because their reports prepare decisions and do not merely deliver figures. Bissantz supports controllers with DeltaMaster in analyzing variances, formulating key messages as action titles, and adding comments to reports.
Can AI handle data storytelling?
AI can handle parts of data storytelling, such as identifying important variances and formulating short texts. In DeltaMaster's AI reporting, Bissantz relies on Explainable AI with an audit trail, so that every AI-generated statement in data storytelling is based on verifiable KPIs.
What is the difference between dashboards and data storytelling?
A dashboard shows the current state of many KPIs for ongoing monitoring; data storytelling explains a specific insight with its cause and a recommended action. At Bissantz, the two complement each other: the dashboard in DeltaMaster or the DeltaApp shows the variance, and the commented report folder provides the data story to go with it.
Summary
Data storytelling combines data, narrative structure, and visualization so that analysis results turn into decisions. In management reporting, the key message, cause, and recommended action matter more than dramaturgy. Bissantz supports data storytelling with DeltaMaster through meaningful coloring, comments, graphical tables, dynamic report titles, and AI-generated short texts with an audit trail.
Turn stacks of reports into clear data stories
No more endless report piles that no one wants to work through. With analysis and reporting software from Bissantz, you turn data from all source systems into clear data stories that reveal the need for action more transparently than ever before.
Reporting with Bissantz