• Why do established organizations often struggle to respond effectively to disruptive change?
• How can organizations identify early signals of disruption before its too late?
• What lessons can we learn from companies that failed to adapt to disruptive forces?
• What frameworks or tools help assess the potential impact of emerging tech?
• What role does cybersecurity play in implementing new technologies?
• What ethical considerations should guide the use of emerging technologies?
  **All questions need to be based on the organization American Airlines. Please cite and No ai**

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Business Question Addressed What ticket sale trends exist that may help us increase attendance? This assignment focuses on understanding how attendance and capacity utilization have changed across 2023, 2024, and 2025 for the Battle of the Badges hockey event. Important Context for This Assignment (Read Carefully) Although the Battle of the Badges event is held at the same venue each year, attendance performance is best evaluated relative to total capacity, not just raw ticket counts. Raw ticket counts tell us how many tickets were sold or distributed, but they do not indicate how full the arena was or how effectively available capacity was used. Percentages, on the other hand, provide a normalized measure of attendance performance, allowing for clearer comparison across years and clearer communication with decision-makers. For this reason, analysts often examine both counts and percentages: Counts show absolute volume Percentages show utilization and performance relative to capacity This assignment asks you to compare both views and evaluate which is more informative for understanding attendance trends over time. Purpose In this assignment, you will: Create Excel charts using ticket counts and ticket percentages Compare attendance patterns across multiple years Evaluate which metric (counts or percentages) is more appropriate for year-to-year comparison Interpret your charts in the context of a real business decision The goal is to connect data structure visualization choice business reasoning. Data for This Assignment You will use the Event Audit / Summary sections from: 2023 Final Ticket Sales.xlsx 2024 Ticket Sales Report.xlsx 2025 Final Tickets.xlsx From each file, you will extract: Sold Comps Opens Corresponding percentages (when available) Part A: Multi-Year Ticket Counts Chart (4 points) Step 1: Extract Ticket Counts From each years Event Audit summary, record the following: Year Sold Comps Opens 2023 ___ ___ ___ 2024 ___ ___ ___ 2025 ___ ___ ___ Step 2: Create the Chart Select the table above. Go to Insert Column Chart Clustered Column (This is commonly referred to as a bar chart.) Add: Chart Title: Ticket Counts by Year and Ticket Type X-axis label: Year Y-axis label: Number of Tickets Add data labels so the counts appear on top of each bar. Part B: Multi-Year Ticket Percentages Chart (4 points) Step 1: Extract Ticket Percentages From each Event Audit summary, record the ticket percentages: Year Sold % Comps % Opens % 2023 ___ ___ ___ 2024 ___ ___ ___ 2025 ___ ___ ___ (If percentages are not explicitly provided in the report, calculate them using the totals shown.) Step 2: Create the Chart Select the percentage table. Insert a column chart. Add: Chart Title: Ticket Distribution by Year (Percentage of Capacity) X-axis label: Year Y-axis label: Percentage of Capacity Add data labels showing the percentages on top of each bar. Part C: Interpretation Questions (2 points) Answer the following in complete sentences. Question 1 Which chart (counts or percentages) is more appropriate for comparing attendance across years? Explain why, focusing on what each metric reveals about attendance performance. Question 2 If you were presenting these results to Dartmouth Health leadership, which visualization would you emphasize when discussing attendance performance? Why? Submission Instructions Submit one Excel file that includes: Your two data tables The counts chart The percentage chart Charts must include: Clear titles Labeled x- and y-axes Data labels on bars Use professional formatting. Submit one Word document that includes the answers to your Questions from Part C. Reminder This assignment is about thinking like an analyst, not just using Excel. Your explanations should be: Grounded in the charts you created. Aware of the difference between absolute ticket counts and percentage-based measures of attendance performance. Clearly written and logically argued. Homework Rubric: Visualizing Attendance Trends Across Years Total Points: 10 Part A: Multi-Year Ticket Counts Chart (4 points) Criteria Excellent (Full Credit) Partial Credit Points Correct data extraction (counts) Sold, Comps, and Opens counts correctly extracted for all three years One or more values incorrect or missing 1 Appropriate chart type Clustered column (bar) chart correctly used to compare ticket types across years Chart type used but not ideal for comparison 1 Chart labeling & formatting Clear, descriptive title; x- and y-axes labeled correctly Missing or unclear title/axis labels 1 Data labels displayed Ticket counts clearly displayed on top of each bar Data labels missing or incomplete 1 Part B: Multi-Year Ticket Percentages Chart (4 points) Criteria Excellent (Full Credit) Partial Credit Points Correct data extraction (percentages) Sold, Comps, and Opens percentages correctly extracted or calculated for all years One or more values incorrect or missing 1 Appropriate chart type Clustered column (bar) chart appropriately used for percentage comparison Chart type used but limits interpretability 1 Chart labeling & formatting Clear, descriptive title; axes labeled with correct units (percentage) Missing or unclear labels 1 Data labels displayed Percentages clearly displayed on top of each bar Data labels missing or incomplete 1 Part C: Interpretation & Business Reasoning (2 points) Criteria Excellent (Full Credit) Partial Credit Points Comparison of counts vs. percentages Clearly explains why percentages are more appropriate for year-to-year comparison given changing arena capacity Mentions difference but explanation is vague or incomplete 1 Connection to business context Interpretation explicitly references the SNHU Arena capacity difference (e.g., 2024 upper bowl not opened) and ties insights to attendance strategy Business context mentioned but not clearly integrated 1

Hide Assignment Information
Instructions
Business Question Addressed What ticket sale trends exist that may help us increase attendance? This assignment focuses on understanding how attendance and capacity utilization have changed across 2023, 2024, and 2025 for the Battle of the Badges hockey event. Important Context for This Assignment (Read Carefully) Although the Battle of the Badges event is held at the same venue each year, attendance performance is best evaluated relative to total capacity, not just raw ticket counts. Raw ticket counts tell us how many tickets were sold or distributed, but they do not indicate how full the arena was or how effectively available capacity was used. Percentages, on the other hand, provide a normalized measure of attendance performance, allowing for clearer comparison across years and clearer communication with decision-makers. For this reason, analysts often examine both counts and percentages: Counts show absolute volume Percentages show utilization and performance relative to capacity This assignment asks you to compare both views and evaluate which is more informative for understanding attendance trends over time. Purpose In this assignment, you will: Create Excel charts using ticket counts and ticket percentages Compare attendance patterns across multiple years Evaluate which metric (counts or percentages) is more appropriate for year-to-year comparison Interpret your charts in the context of a real business decision The goal is to connect data structure visualization choice business reasoning. Data for This Assignment You will use the Event Audit / Summary sections from: 2023 Final Ticket Sales.xlsx 2024 Ticket Sales Report.xlsx 2025 Final Tickets.xlsx From each file, you will extract: Sold Comps Opens Corresponding percentages (when available) Part A: Multi-Year Ticket Counts Chart (4 points) Step 1: Extract Ticket Counts From each years Event Audit summary, record the following: Year Sold Comps Opens 2023 ___ ___ ___ 2024 ___ ___ ___ 2025 ___ ___ ___ Step 2: Create the Chart Select the table above. Go to Insert Column Chart Clustered Column (This is commonly referred to as a bar chart.) Add: Chart Title: Ticket Counts by Year and Ticket Type X-axis label: Year Y-axis label: Number of Tickets Add data labels so the counts appear on top of each bar. Part B: Multi-Year Ticket Percentages Chart (4 points) Step 1: Extract Ticket Percentages From each Event Audit summary, record the ticket percentages: Year Sold % Comps % Opens % 2023 ___ ___ ___ 2024 ___ ___ ___ 2025 ___ ___ ___ (If percentages are not explicitly provided in the report, calculate them using the totals shown.) Step 2: Create the Chart Select the percentage table. Insert a column chart. Add: Chart Title: Ticket Distribution by Year (Percentage of Capacity) X-axis label: Year Y-axis label: Percentage of Capacity Add data labels showing the percentages on top of each bar. Part C: Interpretation Questions (2 points) Answer the following in complete sentences. Question 1 Which chart (counts or percentages) is more appropriate for comparing attendance across years? Explain why, focusing on what each metric reveals about attendance performance. Question 2 If you were presenting these results to Dartmouth Health leadership, which visualization would you emphasize when discussing attendance performance? Why? Submission Instructions Submit one Excel file that includes: Your two data tables The counts chart The percentage chart Charts must include: Clear titles Labeled x- and y-axes Data labels on bars Use professional formatting. Submit one Word document that includes the answers to your Questions from Part C. Reminder This assignment is about thinking like an analyst, not just using Excel. Your explanations should be: Grounded in the charts you created. Aware of the difference between absolute ticket counts and percentage-based measures of attendance performance. Clearly written and logically argued. Homework Rubric: Visualizing Attendance Trends Across Years Total Points: 10 Part A: Multi-Year Ticket Counts Chart (4 points) Criteria Excellent (Full Credit) Partial Credit Points Correct data extraction (counts) Sold, Comps, and Opens counts correctly extracted for all three years One or more values incorrect or missing 1 Appropriate chart type Clustered column (bar) chart correctly used to compare ticket types across years Chart type used but not ideal for comparison 1 Chart labeling & formatting Clear, descriptive title; x- and y-axes labeled correctly Missing or unclear title/axis labels 1 Data labels displayed Ticket counts clearly displayed on top of each bar Data labels missing or incomplete 1 Part B: Multi-Year Ticket Percentages Chart (4 points) Criteria Excellent (Full Credit) Partial Credit Points Correct data extraction (percentages) Sold, Comps, and Opens percentages correctly extracted or calculated for all years One or more values incorrect or missing 1 Appropriate chart type Clustered column (bar) chart appropriately used for percentage comparison Chart type used but limits interpretability 1 Chart labeling & formatting Clear, descriptive title; axes labeled with correct units (percentage) Missing or unclear labels 1 Data labels displayed Percentages clearly displayed on top of each bar Data labels missing or incomplete 1 Part C: Interpretation & Business Reasoning (2 points) Criteria Excellent (Full Credit) Partial Credit Points Comparison of counts vs. percentages Clearly explains why percentages are more appropriate for year-to-year comparison given changing arena capacity Mentions difference but explanation is vague or incomplete 1 Connection to business context Interpretation explicitly references the SNHU Arena capacity difference (e.g., 2024 upper bowl not opened) and ties insights to attendance strategy Business context mentioned but not clearly integrated 1

Purpose

In this assignment, you will explore cluster analysis as a tool for identifying customer segments in a real-world business scenario. Businesses use clustering techniques to group customers based on shared characteristics, helping refine marketing strategies, create targeted product recommendations, and enhance customer engagement.

This assignment emphasizes the practical application of cluster analysis by having you interpret a pre-clustered dataset to extract meaningful insights, identify patterns, and develop strategic recommendations. Your goal is to analyze patterns, assess engagement, and develop strategic recommendations based on your findings.

Task

A Portuguese bank recently ran a direct marketing campaign to promote term deposit accounts, but engagement was lower than expected. As a data analyst at a marketing agency specializing in financial services, you have been hired to analyze customer clusters using a dataset provided by the bank.

Your analysis should focus on identifying which customer segments were most likely to respond positively and developing data-driven recommendations to improve future campaign performance. Your report will be presented to the banks marketing and strategy team, data analysts, and key executives and represents key deliverables you may encounter in the workplace as a business professional, requiring the ability to translate data insights into strategic business decisions.

Your task includes:

• Examining customer segment patterns
• Evaluating customer engagement levels
• Providing data-driven marketing recommendations

Understanding the Banks Marketing Challenge

The bank is experiencinglow customer engagementwith itsterm deposit offerings. Your analysis should address:

• How can the bank improve engagement with the right customers?
• What customer characteristics influence campaign success?
• Which customer segments are most likely to open a term deposit?

What is a Term Deposit?

Aterm depositis asavings account where customers deposit money for a fixed period in exchange for a guaranteed interest rate. Banks use term deposits to secure long-term investments. This campaign aimed toincrease customer participation in term deposits.

Understanding Customer Engagement

Customer engagement is measured using:

• Campaign Contacts:How many times a customer was contacted.
• Response Rate:Percentage of customers whoopened a term deposit (“Yes”) vs. those who declined (“No”).

Customer Clusters Explanation

Each customer has been pre-assigned to one of three clusters:

Cluster	Description
High-Value Customers	Large account balances, strong financial history, and most likely to engage with financial products.
Moderate-Value Customers	Some financial activity, potential for engagement with the right incentives.
Low-Value Customers	Low financial engagement, least likely to respond but may be incentivized.

Understanding the Dataset
The dataset includes customer characteristics, campaign interaction history, and pre-assigned cluster labels.

Variable	Description
Customer ID	Unique identifier for each customer
Age	Customer’s age
Job	Profession of the customer
Marital Status	Single, Married, or Divorced
Education	Primary, Secondary, or Tertiary
Balance	Account balance
Campaign Contacts	Number of times contacted
Previous Outcome	Outcome of previous marketing campaigns (Success/Failure)
Response (Yes/No)	Whether the customer opened a term deposit
Cluster	Customer segment (High-Value, Moderate, Low-Value)

Using the provided dataset, conduct an analysis to understand customer behavior, segment differences, and key drivers of engagement. Your goal is to extract meaningful insights that will inform strategic marketing recommendations. Complete the following steps in Excel and present your findings in a professional report.

1. Descriptive Statistics. Analyze customer behavior by calculating the following key statistics in Excel:

• Central Tendency (Mean, Median, Mode)
  • Age: Average customer age
  • Balance: Average customer balance
  • Campaign Contacts: Average number of times contacted
• Variability (Standard Deviation, Range)
  • Balance Distribution: Do account balances vary widely?
  • Campaign Contacts: Were some customers over-contacted?
• Categorical Breakdown
  • Marital Status: Percentage of married, single, and divorced customers.
  • Education Level: Percentage of primary, secondary, and tertiary education levels.
  • Cluster Distribution: Percentage of customers in each cluster.

2. Cluster Analysis Review. Examine how each cluster differs basked on:

• Demographics: Average age, marital status, and education.
• Engagement: Contact frequency, response rate.
• Term Deposit Likelihood: Which cluster is most responsive?
• Balance Differences: Do High-Value customers have significantly larger balances?
• Campaign Contacts: Are High-Value customers contacted more often?
• Response Rate: Which cluster had the highest “Yes” rate?

3. Visualizations for Cluster Comparisons. Use Excel charts to present key insights:

• Bar Chart: Show marital status or education level by cluster.
• Box Plot or Histogram: Compare balance across clusters.
• Scatter Plot: Show the relationship between balance and campaign contacts.

4. Comparative Analysis. Compare customer engagement and behavior across clusters:

• Compare Two Clusters (High-Value vs. Low-Value)
  • Behavior: How does engagement differ?
  • Engagement Patterns: Which cluster is more responsive?
  • Demographic Influence: Are factors like age or marital status affecting engagement?
• Identify Key Factors Driving Customer Engagement
  • Balance: Do higher balances correlate with more engagement?
  • Campaign Contacts: Does frequent outreach help?
  • Previous Outcome: Does a past “Success” predict future engagement?
• Use Basic Statistical Tests
  • Correlation: Relationship between balance & response rate

5. Strategic Recommendations. Based on your findings, provide three targeted strategies to improve the banks marketing efforts.

• What recommendations would you make for each customer segment (High-Value, Moderate-Value, Low-Value)?
• How should the bank adjust its approach to increase engagement?
• What specific marketing tactics could be most effective for each segment?

Submission

• Submit two documents, a Microsoft Word written analysis with data visualizations and a Microsoft Excel workbook file.
• Your report should be written from the perspective of a data analyst providing actionable insights to improve the banks marketing strategy. Ensure your analysis is clear, data-driven, and tailored to business decision-makers.
• Your submission must be your own original analysis, demonstrating your ability to interpret data, generate insights, and develop strategic recommendations.
• All recommendations should be supported by data from the analysis and, where applicable, credible external sources. Use proper APA citations to reference industry reports, academic research, or relevant business frameworks that support your conclusions.

In Tableau, connect to the saved file and drag the desired sheets into the workspace. Do not alter the data in any way at this point. Navigate to the tab labelled Sheet 1 from here you can start your summarisation and visualisation. Make sure you are working with the correct dataset for this capstone project.

Use Tableau to illustrate and summarise the information contained in this dataset, and insert any visualisations you create into this document by exporting them to image files and inserting them using the function Insert Pictures. The wording on images and captions do not form part of the maximum word count as set out in the brief.

You are allowed to use any of the functionality of Tableau to inform your visualisations.

Question 1: Report by location

In this question, you are required to visualise location dimensions using some of the variables found in the dataset. Examples of location dimensions include city, state, and country. Investigate these dimensions in relation to the measures of sales, profits, discounts, quantity, or a combination thereof (the choice is yours). Take note of the following requirements when preparing your submission:

Include one or more exported dashboards consisting of at least three visualisations, communicating key insights gained from the Superstores performance in relation to location.

Summarise your visualisations by reporting on what they represent:

(Max. 250 words)

Start writing (and insert visualisations) here:

Question 2: Report by division

In this question, you are required to visualise division dimensions using some of the measures found in the dataset. Examples of division dimensions include category, subcategory, and segment. Investigate these dimensions in relation to the measures of sales, profits, discounts, quantity, or a combination thereof (the choice is yours). Take note of the following requirements when preparing your submission:

Include one or more exported dashboards consisting of at least three visualisations, communicating key insights gained from the Superstores performance in relation to division.

Summarise your visualisations by reporting on what they represent:

(Max. 250 words)

Start writing (and insert visualisations) here:

Question 3: Report over time

In this question, you are required to visualise time dimensions using some of the measures found in the dataset. Examples of time dimensions include order date and ship date. You are required to investigate these dimensions in relation to the measures of sales, profits, discounts, quantity, or a combination thereof (the choice is yours). Take note of the following requirements when preparing your submission:

Include one or more exported dashboards consisting of at least three visualisations, communicating key insights gained from the Superstores performance in relation to time.

Summarise your visualisations by reporting on what they represent:

(Max. 250 words)

Start writing (and insert visualisations) here:

Question 4: Unassisted report

In this question, you can visualise and report on any insights you discover in the Superstore dataset. You have free rein to use any visualisation you desire, on any of the variables you deem appropriate. Take note of the following requirements when preparing your submission:

Include one or more exported dashboards, including at least three visualisations, communicating key insights gained from the Superstores dataset that you find interesting.

Summarise your visualisations by reporting on what they represent:

(Max. 250 words)

Start writing (and insert visualisations) here:

Objective:

To develop evidence-based recommendations for True North Foods by applying quantitative data analytics and data visualisation to address the strategic question: Should the company accelerate ESG investments or maintain margins?

Step 1: Download the materials

• Case Study:

Step 2: Review the case

Read the case carefully to understand the strategic dilemma, context, and the two scenarios (status quo vs. accelerated ESG investment).

Step 3: Analyse the data

• Apply quantitative data analytics (descriptive statistics, correlations, regressions) to the provided dataset.
• Use data visualisation tools (Tableau) to uncover patterns and communicate insights.

Step 4: Develop recommendations

Based on your analysis:

• Provide evidence-based insights on customers, employees, and store operations.
• Recommend the best course of action for True North Foods, supported by data and visualisations.

Deliverable

Prepare a 10-minute YouTube (unlisted) team presentation summarising:

• Key findings and insights developed from your analysis.
• Data and Visualisations that support your insights.
• Your recommendation and rationale.

Presentation Guidelines

• Duration: 10 minutes per team.
• Every team member must actively contribute.

Submission

Submit:

• PowerPoint presentation with embedded tables and data visualisations.
• Include a Link to the Unlisted YouTube Video (see upload settings in YouTube for unlisted)
• Include slides for references and appendices.
• No additional report required.

Referencing and Citation:

As part of your academic responsibilities, all submitted work must adhere to the APA 7th edition guidelines for proper referencing. Please refer to to verify citation and formatting rules before submitting your assignments.

Additionally, make sure to utilize the Coach to review your papers before submission (not after). Instructions for this can be found on the Program Immersion Course page.

Purpose

In the business world, prompting is not guessing the magic words. Its writing a clear work order for an AI tool, testing it, and iterating until outputs are reliable. In this lab you will:

• define a business problem with a small sample dataset,
• build a reusable Prompt Pack (prompt templates),
• run a Prompt Clinic (before/after improvement).

Allowed tools

You may use ChatGPT, Gemini, or Microsoft Copilot to run your prompts. You may also use Excel/Google Sheets for quick calculations.

• You must disclose any AI use in the AI Use Note section of the template.
• **Do not paste sensitive data** (PII, passwords, non-public company data). Use de-identified, public, or synthetic data.

What you submit

Submit **one completed Word document**:

• **CAI3801_Week03_Lab02_your_name.docx** (download by clicking the downward arrow) –

Step 1 Choose your problem + sample data (required)

Pick ONE of these options:

Option A (recommended): Use a Starter Case (provided in the template doc – starter case #1-4).

Choose one starter case and use the included mini-dataset.

Option B: Create your own business problem (creative option- starter case #5).

Your problem must include:

1) a clear **goal** (what success means),

2) **constraints** (at least 2),

3) **sample data** you can paste into an AI tool (minimum **5 rows** **4 columns** or equivalent text records).

**Important:** Your data can be small and synthetic. The key is that its realistic and supports your prompts.

Step 2 Prompt Pack (3 required + 2 optional)

In the template, complete **Prompt 13 (required)**:

• each prompt must be reusable (a teammate could reuse it) – but different from each other,
• include RTC-CO (Role, Task, Context, Constraints, Output format),
• include a structured output format (table, bullets with headings, JSON-like schema, etc.),
• version your prompts (v1, v2).
• run them in your chosen AI tool.

**Prompt 45 are optional** (extra practice; not required for grading unless you want to show more work and get extra credit).

Step 3 Prompt Clinic (before/after)

Pick **ONE** of your prompts (from Prompt 13) and do a before/after improvement:

1) Run a **baseline** version save a short excerpt of output.

2) Score it using the rubric (02 each: Relevance, Constraints, Actionability, Structure, Self-check).

3) Rewrite the prompt using RTC-CO + quality checks run again.

4) Re-score and briefly explain what improved.

Step 4:

Reflection (35 sentences/bullets): What did you learn? What broke on the worst case or what worked for the best case? What would you change next?

Grading (100 points + 10 extra credit)

• Problem definition + sample data quality: 20
• Prompt Pack (3 required prompts): 25
• Prompt Clinic (before/after + rubric scoring): 30
• Creativity (Use of different techniques learned from class lecture + defining your own business problem + actionable recommendations etc.): 15
• Professional practice (AI Use Note + clarity/formatting): 10
• Extra credit (2 optional prompts): 10

Tips for success

• Keep prompts **specific** (use the data you have, not generic advice).
• Use **constraints** (length, tone, do/dont, budget limits, CPA cap, etc.).
• Ask for a **structured output** (tables beat paragraphs for business work).
• Include a **self-check** step (flag assumptions + what to verify).

Academic integrity

This lab teaches responsible AI use. Using an AI tool is allowed (and expected), but you must:

• write your own scenario/data (or use the starter case),
• show your prompt versions and your scoring,
• submit your work in your own words.

(Your submission should reflect your thinking and decisions, not just raw AI output.)

Data Analytics in Hospitality kindly fill the formulas added and the attached outline for the project

For this week’s discussion, you will discuss the following:

1. What are the four questions that you need to ask before designing a dashboard.
2. Why is it important to ask these questions?
3. What would happen if you did not ask these questions?

EXAMPLE POST:

As an Architect with more than 14 years of experience in project management in the construction business, I have learned that a dashboard is only as effective as the questions asked before it is designed. Based on this weeks learning, there are four key questions that should always be addressed prior to building a dashboard.

The first question is: Who is the audience?
Understanding whether the dashboard is intended for executives, project managers, or operational teams is critical. Each group requires a different level of detail, frequency, and type of insight. For example, executives typically focus on KPIs and trends, while project managers need operational and task-level metrics.

The second question is: What decisions will be made using this dashboard?
Dashboards should be decision-driven, not data-driven. Clarifying the decisions ensures that the dashboard provides actionable insights rather than overwhelming users with unnecessary data.

The third question is: What metrics and data sources are required?
Identifying the right KPIs and ensuring data accuracy, relevance, and reliability is essential. In construction, using incorrect or inconsistent data can lead to flawed cost forecasts, scheduling errors, or resource misallocation.

The fourth question is: How often will the dashboard be used and updated?
The update frequency must align with the business process. Real-time dashboards may be necessary for operations, while weekly or monthly updates may be sufficient for strategic reviews.

It is important to ask these questions because they ensure alignment between business objectives, users needs, and data integrity. A well-designed dashboard supports clarity, efficiency, and confident decision-making.

If these questions are not asked, dashboards often become cluttered, confusing, and underutilized. They may present irrelevant metrics, mislead decision-makers, or fail to support timely actionsultimately reducing trust in data and analytics.

In summary, asking these four questions upfront transforms dashboards from simple visual reports into powerful decision-support tools that add real value to the organization.