Dashboard Design, Storytelling and Managerial Communication

Introduction

A dashboard is more than a collection of charts. It is a designed decision-support system that brings together data, visual encoding, context, interpretation and action. In Power BI and Tableau, a technically correct dashboard can still fail if managers cannot identify the most important issue, understand its significance or determine what should happen next.

Dashboard design therefore combines analytical reasoning with communication design. The designer must decide which measures matter, how they should be compared, what level of detail is appropriate, how uncertainty should be disclosed and how findings should be translated into managerial choices.

This chapter examines four common dashboard purposes—operational, tactical, strategic and executive—and explains how to build narratives that move from evidence to interpretation and action. It also discusses annotation, uncertainty, accessibility, governance and the responsible use of AI-assisted analytics.

Learning Objectives

By the end of this chapter, you should be able to:

1. Distinguish operational, tactical, strategic and executive dashboards.
2. Select appropriate metrics, visual forms and interaction patterns for a decision context.
3. Apply principles of visual hierarchy, information scent and cognitive load reduction.
4. Structure a dashboard narrative from context and evidence to implication and recommendation.
5. Annotate findings without overstating what the data proves.
6. Communicate uncertainty, data quality limitations and confidence appropriately.
7. Tailor dashboards and recommendations to managerial audiences.
8. Design and evaluate dashboards in Power BI and Tableau.
9. Recognise ethical, accessibility, privacy and governance requirements.

1. Dashboards as Decision Systems

A dashboard connects five elements:

A dashboard is effective when it reduces the time and effort required to move from a business question to an appropriate action. This does not mean showing as much information as possible. It means showing sufficient, relevant and trustworthy information for the intended decision.

A useful design chain is:

Business objective → decision question → analytical measure → visual representation → interpretation → action.

For example, the objective may be to improve service reliability. The decision question may be, “Which distribution centres require intervention this week?” Suitable measures could include on-time delivery rate, order backlog and average delay. A ranked table with exception colouring may be more useful than a decorative map because the manager needs to prioritise sites.

Dashboard, report and scorecard

These terms are related but not identical.

• A report commonly provides detailed information for exploration, compliance or periodic review.
• A scorecard compares performance against defined objectives, often using a small number of indicators.
• A dashboard usually combines indicators and visual analysis for monitoring and decision support.
• An analytical application provides deeper exploration, modelling and scenario analysis.

In practice, a Power BI report or Tableau workbook may contain several dashboard-like pages, detailed drill-through pages and analytical views. The important issue is not the label but the user’s task.

2. Four Dashboard Types

2.1 Operational dashboards

Operational dashboards support frequent, near-term decisions. Users may include supervisors, service managers, logistics coordinators and contact-centre leaders. Data may be refreshed hourly, daily or continuously, depending on the process.

Typical questions include:

• Which orders are late right now?
• How many cases are waiting beyond the service-level target?
• Which machines have produced unusual defect levels today?
• Where is staffing inadequate for the current workload?

Operational dashboards should emphasise current status, exceptions, thresholds and ownership. Compact tables, bullet charts, control-style indicators and trend sparklines are often useful. Excessive narrative text and long historical comparisons may distract from immediate action.

A good operational dashboard answers three questions quickly:

1. What is happening now?
2. Is it outside an acceptable range?
3. Who must respond?

2.2 Tactical dashboards

Tactical dashboards support decisions over weeks or months. They are commonly used by department managers and analysts. The focus is on diagnosing causes, allocating resources and improving performance.

Typical questions include:

• Which customer segments are declining?
• Which products have low margin despite high sales?
• Which branches are underperforming relative to comparable branches?
• What explains the increase in employee absence?

Tactical dashboards need comparisons, segmentation and moderate analytical depth. They may include period-over-period changes, contribution analysis, decomposition trees, cohort views and filters for region, product or customer type.

2.3 Strategic dashboards

Strategic dashboards support longer-term direction and resource allocation. Users may include senior managers, business-unit leaders and strategy teams. The dashboard should connect operational performance to strategic objectives.

Typical questions include:

• Are we progressing toward the annual growth objective?
• Which strategic initiatives are creating value?
• Are investments improving customer retention?
• What risks could prevent achievement of the plan?

Strategic dashboards should show trends, targets, leading and lagging indicators, dependencies and material risks. They should avoid false precision. A long-term objective should not be represented as if it were fully determined by a single monthly value.

2.4 Executive dashboards

Executive dashboards are designed for limited time and high consequence. Executives need a concise view of organisational health, material deviations and decisions requiring attention.

An executive dashboard often contains:

• a small number of enterprise-level indicators;
• current value and target;
• direction and rate of change;
• major drivers or exceptions;
• risks, assumptions and recommended decisions.

Executive dashboards should not be merely simplified operational dashboards. They require a different abstraction level. A chief executive may not need every branch’s sales value, but may need to know that revenue growth is concentrated in one declining-margin segment.

Dashboard type comparison

The same metric may appear in all four dashboards, but its definition, time scale, comparison and visual context should change.

3. Designing the Analytical Foundation

3.1 Begin with decisions, not charts

A common failure is to open Power BI or Tableau and select attractive visuals before defining the decision. A stronger process begins with a design brief:

• Who is the primary audience?
• What decision will be made?
• How frequently will the dashboard be used?
• What action follows an exception?
• What data is available and how reliable is it?
• Which comparisons are legitimate?
• What should the user not conclude?

A dashboard requirement should be expressed as a decision statement, such as: “The regional service manager will use this view each Monday to identify branches requiring a staffing adjustment.” This is more useful than “Create a branch performance dashboard.”

3.2 Metric definitions and semantic consistency

A metric must have a clear numerator, denominator, population, time period and treatment of missing values. For example, an on-time delivery rate may be defined as:

The numerator is the number of eligible deliveries that met the agreed deadline, and the denominator is the total number of eligible deliveries. The formula assumes that eligibility and deadline rules are consistently recorded. Cancelled orders, test transactions and deliveries without a valid promised date must be treated according to documented policy.

A percentage-point change differs from a percentage change. If a rate increases from 82% to 86%, the change is 4 percentage points. The relative percentage increase is:

For the example, the relative change is ((86 − 82) ÷ 82) × 100% ≈ 4.9%. Dashboards should label these two concepts clearly.

Other useful measures include:

These formulas assume that the target is expressed in the same unit and direction as the actual value. For measures where lower is better, such as complaints or processing time, the meaning of a positive variance must be explained rather than inferred from colour alone.

3.3 Leading and lagging indicators

A lagging indicator records an outcome, such as monthly profit, customer churn or injury frequency. A leading indicator is associated with future performance, such as preventive maintenance completion, unresolved complaints or sales pipeline coverage. Leading indicators are not automatically causal predictors; their usefulness depends on evidence and context.

A balanced dashboard often combines both. Showing only lagging indicators can make the dashboard descriptive but not actionable. Showing only leading indicators can encourage managers to optimise activity without confirming results.

3.4 Targets, benchmarks and baselines

A target is a desired level. A benchmark is a comparison standard, such as a peer group or industry reference. A baseline is a starting point used to measure change. These should not be treated as interchangeable.

A target may be ambitious and therefore show a large shortfall even when performance is improving. A benchmark may be inappropriate if organisations differ in size, market or operating model. A baseline may become obsolete after a major process change. Dashboard labels should identify the comparison type explicitly.

4. Visual Design for Managerial Attention

4.1 Visual hierarchy

Visual hierarchy directs attention in a deliberate order. Important elements should be prominent through position, size, contrast and whitespace. A practical page structure is:

1. Header: purpose, reporting period and data refresh status.
2. KPI strip: a small set of headline measures.
3. Main evidence: the trend, comparison or exception requiring attention.
4. Explanation: drivers, segments or supporting detail.
5. Action area: recommendation, owner, deadline or drill-through link.

The upper-left position is often noticed early in left-to-right reading environments, but hierarchy should not depend solely on cultural reading direction or screen size. Test the design with real users.

4.2 Choosing visual forms

Bars are generally effective for comparing magnitudes because their common baseline supports length comparison. Truncated axes can exaggerate differences and should be used only with explicit labelling and a legitimate analytical reason. Three-dimensional effects, ornamental gauges and excessive colour often reduce rather than improve comprehension.

4.3 Colour and accessibility

Colour should encode meaning consistently. For example, blue may represent actual performance and grey a benchmark, while amber and red indicate increasing levels of attention. Do not rely on red-green distinctions alone because colour-vision deficiencies are common. Use labels, symbols, patterns or position as additional cues.

Use a restrained palette. If every mark is brightly coloured, nothing is visually prioritised. In Power BI, themes and conditional formatting should be centrally managed. In Tableau, use discrete and continuous colour intentionally and provide clear legends.

4.4 Interaction and progressive disclosure

Filters, tooltips, drill-through pages and parameters can support exploration, but interaction also creates hidden states. A manager may overlook a filter that remains active from a previous session. Display active filters clearly and provide a reset mechanism.

Progressive disclosure means showing essential information first and allowing users to open detail when needed. It prevents the initial view from becoming crowded while preserving analytical depth. However, critical information should not be hidden behind complex interactions that are difficult to discover.

5. Storytelling with Data

Data storytelling is the organised presentation of evidence so that an audience can understand a situation, its significance and the available choices. It is not manipulation or dramatic decoration. A credible data story preserves the distinction between observation, interpretation and recommendation.

5.1 A practical narrative structure

A useful managerial narrative has five stages:

1. Context – What business objective or situation is being considered?
2. Current state – What does the evidence show?
3. Change or contrast – How does this differ from the target, previous period or relevant peer?
4. Explanation – Which segments, processes or factors contribute to the pattern?
5. Action – What should be done, by whom, and by when?

For example: “Customer retention fell from 91% to 87% in the last quarter. The decline is concentrated among small-business customers in the northern region, where unresolved support cases increased. The evidence supports prioritising case resolution and testing a proactive renewal programme. The causal effect is not yet established, so the programme should be evaluated over the next quarter.”

This statement separates the measured change, observed concentration, proposed response and limitation.

5.2 The assertion–evidence–implication pattern

Each major chart should support a clear assertion. The evidence should be visible in the chart, and the implication should explain why the audience should care.

• Assertion: Delivery reliability has deteriorated.
• Evidence: On-time delivery declined from 94% to 88% over four months, with the largest decrease in two distribution centres.
• Implication: Managers should review capacity and carrier performance at those centres before the next peak period.

A chart title such as “On-time delivery by month” describes the chart. A more communicative title such as “On-time delivery fell for four consecutive months” states the supported finding. Titles should not claim more than the evidence demonstrates.

5.3 Annotation

Annotations may identify a target breach, a policy change, a supply interruption, a promotion or an unusual observation. Effective annotations are concise and tied to a visible mark or interval.

Annotations should answer one of three questions:

• What changed?
• Why might it have changed?
• What should the user examine next?

The phrase “caused by” should be used only when supported by an appropriate causal analysis. In routine dashboards, phrases such as “coincides with,” “is concentrated in” or “is consistent with” are usually more defensible.

5.4 Avoiding misleading narratives

A narrative becomes misleading when it selectively displays favourable evidence, hides a denominator, changes the time window, uses inconsistent scales or presents a correlation as a cause. Designers should show relevant counter-evidence and document exclusions. A recommendation should identify assumptions and possible unintended consequences.

6. Communicating Uncertainty and Data Quality

Managers often need a decision before perfect information is available. The responsible response is not to hide uncertainty but to express it in a usable way.

6.1 Sources of uncertainty

Uncertainty may arise from:

• incomplete or delayed data;
• measurement error;
• changing definitions;
• sampling variation;
• missing records that are not random;
• forecasts and model assumptions;
• aggregation that conceals subgroup differences.

A dashboard should display data freshness, coverage and important caveats. A simple note such as “Sales data complete through 14 June; returns may be recorded up to seven days later” can prevent misinterpretation.

6.2 Forecasts and intervals

If a dashboard shows a forecast, it should distinguish the estimated central value from a prediction interval. A prediction interval represents a range of plausible future outcomes under the model and assumptions; it is not a guarantee that the outcome will fall within that range.

For a sample proportion, an approximate standard error under a simple random-sampling assumption is:

Here, p̂ is the observed sample proportion and n is the sample size. This approximation is most defensible when observations are reasonably independent and the sample is not extremely small or near a proportion of zero or one. Dashboard designers should not display a confidence interval without understanding how it was produced.

Uncertainty bands may be shown around forecasts, but the legend and tooltip should explain their meaning. Historical variation, model uncertainty and data-quality risk are different concepts and should not be represented as if they were identical.

6.3 Small denominators and suppression

A rate based on five cases can change dramatically after one additional case. Dashboards should show counts alongside rates and may suppress or flag rates below a minimum denominator. This is particularly important for employee, patient, student or customer data, where small groups may also create privacy risks.

7. Tailoring Communication to Managerial Audiences

Managers differ in authority, expertise, incentives and time available. A dashboard should therefore be designed for a specific primary audience, even when it can be accessed by others.

A recommendation should be framed in managerial terms. Instead of “Region B has a negative variance,” write: “Region B is £120,000 below the quarterly revenue target, primarily because two high-value accounts were delayed. The commercial director should review recovery plans by Friday.” The second statement contains magnitude, context, possible driver, owner and timing.

Decision-oriented recommendations

A recommendation should specify:

• the proposed action;
• the expected benefit or risk addressed;
• the responsible owner;
• timing;
• assumptions;
• a measure for evaluating success.

When alternatives exist, present the trade-off. For example, overtime may reduce backlog but increase labour cost and fatigue. A dashboard should not imply that an operational metric can be improved without affecting other objectives.

8. Implementation in Power BI and Tableau

8.1 Power BI considerations

In Power BI, dashboard quality depends on the semantic model as well as page design. Measures should be defined centrally using DAX rather than repeatedly calculated in visual-level expressions. Relationships, filter direction, date tables and row-level security should be reviewed before publication.

Useful implementation practices include:

• creating a dedicated measures table;
• using a consistent date hierarchy;
• displaying the last refresh time;
• configuring report-page tooltips;
• using drill-through for exception analysis;
• applying themes for colour consistency;
• testing slicers and bookmarks with representative users;
• validating totals after filters and cross-highlighting.

A KPI card should not be trusted merely because it looks correct. Test it against independently calculated values and boundary cases, such as a blank period, a zero denominator and a partial month.

8.2 Tableau considerations

In Tableau, dimensions, measures, aggregation and level of detail must be carefully controlled. A calculation at customer level may produce a different result when aggregated at region level. Level-of-detail expressions can support fixed or controlled calculations, but they should be documented so users understand the grain.

Useful practices include:

• designing worksheets around analytical questions;
• using dashboard actions deliberately;
• keeping filters visible and understandable;
• using parameters only when they support a clear decision;
• checking aggregation and null handling;
• providing device-specific layouts;
• publishing certified data sources where governance permits.

In both platforms, performance matters. A slow dashboard interrupts managerial work and encourages users to export data into uncontrolled spreadsheets. Reduce unnecessary visuals, optimise queries, limit high-cardinality interactions and use extracts or appropriate model structures where suitable.

9. Evaluation and Usability Testing

Dashboard evaluation should combine technical validation, visual inspection and user testing.

Technical validation

• Are metric definitions documented?
• Do totals reconcile with the source system?
• Are filters and security rules correct?
• Are dates, currencies and units consistent?
• Are missing and duplicate records handled?
• Does the dashboard show its refresh status?

Communication evaluation

• Can users identify the main message within a short period?
• Can they explain the comparison being made?
• Can they distinguish fact from interpretation?
• Can they locate the next action?
• Do they understand caveats and uncertainty?

Usability test

Give a manager realistic tasks, such as “Identify the branch requiring intervention and explain why.” Observe whether the user succeeds without coaching. Measure errors, time to answer and confidence. A dashboard is not successful because its designer understands it; it is successful when intended users can use it accurately.

10. Case Study: Regional Service Reliability Dashboard

The following is an illustrative case.

Background

MetroServe, a fictional facilities-services company, operates 42 regional branches. Its executive team reports an overall on-time completion rate of 93%, apparently close to the annual target of 94%. However, customer complaints have increased, and branch managers disagree about where the problem lies.

Problem

The existing report contains a national KPI, a map with coloured branches and a table of monthly completion rates. It does not show case volume, denominator size, service type, backlog age or the difference between scheduled and emergency work. Managers therefore cannot prioritise interventions confidently.

Analysis

The analytics team defines on-time completion as the number of eligible jobs completed by the contractual deadline divided by the number of eligible completed jobs. They exclude cancelled jobs and document the treatment of jobs still open at the reporting date.

The revised analysis shows:

• the national rate is 93%, one percentage point below target;
• three branches account for most of the shortfall;
• those branches have high emergency-work volume and older open cases;
• one small branch has a rate of 78%, but its denominator is only 18 jobs, so the rate is unstable;
• a large branch has a rate of 89% across 1,240 jobs and therefore represents a more material performance issue.

The analysis demonstrates why rates must be interpreted with counts, workload composition and materiality.

Approach and solution

The team creates four views:

1. Executive page: overall rate, target variance, complaint trend and three material risks.
2. Regional manager page: branch ranking, job volume, backlog age and service-type comparison.
3. Operational page: open jobs past deadline, assigned owner and escalation date.
4. Methodology page: metric definitions, exclusions, refresh timing and data-quality notes.

The main chart uses a sorted bar chart of branch rates with job volume in the label and a target reference line. A scatter plot places job volume on the horizontal axis and on-time rate on the vertical axis, allowing managers to identify high-volume, low-performance branches. A note warns that correlation between emergency-work volume and delay does not establish causation.

Annotations identify a scheduling-system change and a temporary contractor shortage. These events are described as plausible contributors, not proven causes.

Outcome

Managers prioritise the high-volume branch rather than the branch with the lowest percentage. They reallocate planners, review emergency-work scheduling and create an escalation queue for cases approaching breach. The next month’s dashboard shows improvement in backlog age, while the on-time rate changes only modestly. This leads managers to track both leading indicators and final outcomes.

Learning points

1. A headline KPI can conceal operational concentration.
2. Rates require denominators and context.
3. A map is not automatically the best geographic visual.
4. An annotation should distinguish evidence from explanation.
5. Recommendations become actionable when owners and deadlines are explicit.
6. Dashboard success depends on decisions, not visual complexity.

11. Ethical, Governance and Regulatory Considerations

Dashboards influence resource allocation, performance evaluation and reputational outcomes. Errors can therefore cause material harm.

Privacy and confidentiality

Use the minimum necessary personal information. Apply row-level security and role-based access. Avoid exposing identifiable records when aggregated information is sufficient. Small-cell suppression may be necessary in employee, health, education or customer contexts.

Fairness and bias

A dashboard can reproduce bias present in historical data or introduce bias through selection, missingness and inappropriate benchmarks. Compare performance across relevant groups, investigate material disparities and avoid using protected characteristics as simplistic explanations.

Accountability and auditability

Maintain documentation of metric definitions, data sources, transformations, refresh schedules, owners and known limitations. Version changes to calculations and communicate material revisions. Certified datasets and controlled publication processes reduce the risk of competing numbers.

AI-assisted dashboarding

Power BI and Tableau increasingly provide natural-language queries, automated insights, forecasting and assisted visual selection. These features can accelerate exploration but do not replace professional judgement. AI-generated explanations may omit confounding factors, misread a measure or present correlation as causation. Users should verify the underlying data, calculation, population and time period before communicating an AI-generated insight.

12. Best-Practice Checklist

Before publishing a dashboard, ask:

• Is the decision purpose stated clearly?
• Is the primary audience known?
• Are metrics defined and reconciled?
• Are targets, benchmarks and baselines distinguished?
• Does the visual hierarchy reveal the most important issue first?
• Are comparisons fair and units explicit?
• Are counts shown with rates where appropriate?
• Are filters, refresh time and data coverage visible?
• Are uncertainty and limitations communicated?
• Does each major chart support a clear assertion?
• Are recommendations linked to owners and timing?
• Has the dashboard been tested with real users?
• Are privacy, accessibility, security and governance requirements satisfied?

Chapter Summary

Dashboard design is a decision-support discipline, not simply a visualisation exercise. Operational dashboards focus on immediate exceptions; tactical dashboards support diagnosis and resource allocation; strategic dashboards track direction and risk; and executive dashboards present a concise view of material issues and choices.

Effective dashboards begin with a decision question and a well-defined semantic foundation. Measures require clear numerators, denominators, units, targets and treatment of missing data. Visual hierarchy, appropriate chart selection, restrained colour and progressive disclosure reduce cognitive effort. Storytelling structures evidence through context, current state, change, explanation and action, while annotation must distinguish observed patterns from causal claims.

Managers need uncertainty, data-quality information and denominator context, especially when rates, forecasts or small groups are involved. Recommendations should identify the action, owner, timing, assumptions and success measure. Power BI and Tableau provide strong implementation capabilities, but technical features cannot compensate for unclear metrics or poor communication. Finally, privacy, fairness, accessibility, security, auditability and human review are essential because dashboards influence real organisational decisions.

Key Terms

Dashboard: A visual decision-support interface that presents relevant measures, comparisons and insights.

Operational dashboard: A dashboard for frequent monitoring and immediate response.

Tactical dashboard: A dashboard for medium-term diagnosis, resource allocation and improvement.

Strategic dashboard: A dashboard linking performance to long-term objectives and risks.

Executive dashboard: A concise enterprise-level view designed for senior decision-makers.

KPI: A key performance indicator used to monitor an important objective.

Leading indicator: A measure associated with future performance or emerging conditions.

Lagging indicator: A measure of an outcome that has already occurred.

Target: A desired level of performance.

Benchmark: A comparison standard, often based on peers or external practice.

Baseline: A reference point used to measure change.

Variance: The difference between an actual value and a target or comparison value.

Denominator: The reference quantity in a rate or proportion.

Annotation: Explanatory text or marking linked to a visual observation.

Progressive disclosure: Presenting essential information first and revealing detail when required.

Data freshness: The extent to which displayed data reflects the most recent available information.

Prediction interval: A range representing plausible future outcomes under stated assumptions.

Semantic model: The structured representation of entities, relationships, measures and business definitions.

Row-level security: Controls that restrict which records a user can view.

Materiality: The importance of an issue in relation to decision consequences or organisational objectives.

Review Questions

Short Answer Questions

1. What is the main difference between an operational and a strategic dashboard?
2. Why should a rate usually be displayed with its denominator or count?
3. Distinguish a percentage-point change from a relative percentage change.
4. What is the purpose of an annotation in a dashboard?
5. Name two sources of uncertainty that may affect dashboard interpretation.

Long Answer Questions

1. Explain how a dashboard can be designed around a decision rather than around a collection of charts.
2. Compare the visual and communication requirements of tactical and executive dashboards.
3. Discuss how visual hierarchy, colour and progressive disclosure affect managerial comprehension.
4. Explain the assertion–evidence–implication structure and illustrate it with a business example.
5. Evaluate the ethical and governance responsibilities involved in publishing a dashboard used for employee or customer decisions.

Analytical / Application Questions

1. A branch reports an on-time rate of 78% from 18 jobs, while another reports 89% from 1,240 jobs. Which branch should receive priority investigation, and what additional information would you request before deciding?
2. Design a one-page executive dashboard for a company whose revenue is above target but profit margin is declining. Specify the KPIs, comparisons, visual forms, annotations and recommended action.
3. A dashboard shows that regions with more training hours have higher productivity. Explain why this does not by itself prove that training caused the higher productivity, and propose a responsible way to investigate the relationship.

Multiple Choice Questions with Answers

Question 1

Which dashboard type is primarily designed for immediate monitoring and response?

A. Operational dashboard

B. Strategic dashboard

C. Executive dashboard

D. Research dashboard

Correct Answer: A. Operational dashboard

Question 2

A delivery rate increases from 82% to 86%. What is the change in percentage points?

A. 4 percentage points

B. 4.9 percentage points

C. 48 percentage points

D. 0.4 percentage points

Correct Answer: A. 4 percentage points

Question 3

Which visual is generally most suitable for ranking branches from highest to lowest performance?

A. Unsorted pie chart

B. Sorted bar chart

C. Decorative gauge panel

D. Three-dimensional area chart

Correct Answer: B. Sorted bar chart

Question 4

What should a dashboard narrative normally do after presenting the current state?

A. Remove all caveats

B. Present a change or contrast

C. Add unrelated background information

D. Hide the underlying measure

Correct Answer: B. Present a change or contrast

Question 5

Why should counts often accompany rates?

A. Counts eliminate the need for metric definitions

B. Counts reveal the size of the underlying population

C. Counts always prove causation

D. Counts make targets unnecessary

Correct Answer: B. Counts reveal the size of the underlying population

Question 6

Which statement is most responsible when evidence shows two variables moving together?

A. The first variable definitely caused the second

B. The variables are unrelated in every situation

C. The pattern may be associated, but causation requires further evidence

D. The data should be removed from the dashboard

Correct Answer: C. The pattern may be associated, but causation requires further evidence

Question 7

What is the main purpose of progressive disclosure?

A. To hide all detailed information permanently

B. To show essential information first and reveal detail when needed

C. To increase the number of colours used

D. To prevent users from applying filters

Correct Answer: B. To show essential information first and reveal detail when needed

Question 8

Which item is especially important when publishing a dashboard containing personal data?

A. Unrestricted access for convenience

B. Role-based access and data minimisation

C. Removal of all metric definitions

D. Use of three-dimensional charts

Correct Answer: B. Role-based access and data minimisation

Question 9

A target is 94% and actual performance is 91%. What is the variance in percentage points?

A. 3 percentage points below target

B. 3% above target

C. 97 percentage points below target

D. 0.03 percentage points below target

Correct Answer: A. 3 percentage points below target

Question 10

Which statement best describes an executive dashboard?

A. It should reproduce every operational transaction

B. It should present concise enterprise-level issues and decisions

C. It should avoid targets and comparisons

D. It should contain as many visual elements as possible

Correct Answer: B. It should present concise enterprise-level issues and decisions