Comprehensive End-to-End Dashboard Project Process

Prepared by : Christian Robles
March 16, 2025

This document outlines a detailed, streamlined end-to-end process for completing a dashboard project. It serves as a comprehensive guide for both new and experienced Business Analysts, detailing the necessary steps and sub-tasks involved in creating an effective dashboard. The goal is to ensure clarity and efficiency throughout the project lifecycle, from initial requirements gathering to final delivery and maintenance.

1. Project Initiation

1.1 Define Project Scope

Identify stakeholders and their needs.
Determine the objectives of the dashboard.
Establish the key performance indicators (KPIs) to be tracked.
Setup Data Kick-off call with SME
Establish weekly recurring sync-up meetings
Create project group chat, and other helpful applications

1.2 Gather Requirements

Conduct interviews with stakeholders.
Initiate call with Data SMEs
Distribute surveys to gather additional insights if needed.
Document functional and non-functional requirements.

1.3 Create Project Plan

Develop a timeline with milestones (Estimated Duration: 3 days).
Create Kanban board or similar project management board
Assign roles and responsibilities.
Identify resources required for the project.

Requirements gathering :

What should I clarify about the request with the stakeholder to make sue that I can deliver everything that the requestor needs on time?
What's the business context from an ask like this, and who's the audience?
Which metrics and dimensions are crucial to the analysis?
Where can I access the data that I need?

High-level framework for EDA:

Identify the question type
Break the big business questions down into smaller questions
Translate the questions to the technique

Types of exploratory requests :

Overall trends. What are the overall trends in sales?
Growth rates. What were our monthly and yearly growth rates?
Performance measurement: How is the new loyalty program performing? Should we keep using it?
KPI Reporting. What were our refund rates and average order value?

Overall Trends

Seasonality:
- What are the sales by that time?

dimensional Segmentation
- What are the sales by country? Region? Product?
Summary Stats:
- Looking at descriptive stats like min, max, and average

Growth Rates :

Seasonality:
- Are there certain months or seasons where the growth rates are consistently high/low?

dimensional Segmentation
- what were the growth rates sliced by different dimensions
Summary Stats:
- Which month have the highest growth? What is the average growth rate across a specific year?

Majority of EDA can be tackled with :

Pivot Tables (Table and Matrix in PBI)
Conditional formatting
Graphing (line graphs, bar graphs, area charts, scatter plots
Summary Stats / Aggregation functions

Concrete Steps for EDA :

Step 1. Spend 1-2 hours exploring and cleaning the data
Propose a business question like on of the high-level questions that I showed you above, and pick 1-2 of the smaller questions that you want to answer.
Spend about 4-6 hours answering these questions directly in a technical tool.
Spend 1-2 hours reviewing your analysis to pull high-level insights

Table of Contents (sample) :

Tile of the Project
- Project Background
- Executive Summary
- Insights Deep-Dive
  - Sales Trend and Growth Rates
  - Key Product Performance
  - Customer Growth and Repeat Purchase Trends
  - Loyalty Program Performance
  - Sales by Platforms & Channels
  - Refund Rate Trends
- Recommendations
- Clarifying Questions, Assumptions, and Caveats
  - Questions for Stakeholders Prior to Project Advancement
  - Assumptions and Caveats

2. Data Collection and Preparation

2.1 Identify Data Sources

List all potential data sources (databases, APIs, spreadsheets).
Assess data availability and quality.

2.2 Data Extraction

Extract data from identified sources.
Ensure data is up-to-date and relevant.

2.3 Data Cleaning and Transformation

Resolve Data Type Issues
1. Remove duplicates and irrelevant data.
2. Check for missing data
3. Incorrect values
4. Incorrect data type
5. Mix data types in a single column (i.e. "N/A" value in a numeric column)
6. Categorical variables misclassified (i.e. {0,1,2} for {"low", "medium", "high")
7. Numeric Data with Leading/Trailing Characters (extra space characters is the best example)
8. Precision and Overflow Issues
9. Missing Values Misrepresented ("N/A" instead of null or [1,2,"NA",4"] NA as 0 or null)
10. Boolean Misrepresentation (yes/no, 1/0, true/false as strings)
11. Date and Time Format inconsistencies. Or Dates that needs to use locale to correctly converts to date
Prepare Summary Statistic results for each column
1. For numeric columns
  1. Count and Distinct Count
  2. Mean, Median, Mode
  3. Maximum, Minimum, Range
  4. Standard Deviation, Sample Variance
  5. Standard Deviation, Standard Error
  6. Kurtosis, Skewness
2. For Date and Time
  1. Count, Distinct count
  2. Date Range in days, Days between average
  3. Earliest date, Latest date, Median date
  4. most Frequent date
  5. Weekend count
3. for String
  1. Trim, clean values
  2. Perform required word/phrase case
  3. Check for dependencies
  4. Check for IDs related to column content
  5. Spelling
  6. single cell multiple content (i.e. products ordered)
Normalize data formats (1NF, 2NF, etc. using Power Query).
1. Avoid over-normalization
Denormalize for simplified star or snowflake schema (flat dimension tables and centralized fact table).
Create calculated fields if necessary.

2.4 Exploratory Data Analysis (EDA)

Sub-steps:
- Analyze data distributions (mean, median, mode).
- Identify outliers and anomalies.
- Visualize data relationships (scatter plots, correlation matrices).
- Check for missing values and assess their impact.
Tips:
- For categorical data, look for frequency counts.
- For numerical data, assess skewness and kurtosis.

3. Dashboard Design

3.1 Wireframe Development

Sketch initial layout ideas for the dashboard.
Determine the placement of charts, tables, and filters.

3.2 Select Visualization Tools

Choose appropriate software (e.g., Tableau, Power BI, Excel).
Ensure compatibility with data sources.

3.3 Design Prototypes

Create mock-ups of the dashboard.
Gather feedback from stakeholders on design.

4. Dashboard Development

4.1 Build Dashboard

Implement the design using the chosen visualization tool.
Be sure to check client's official color scheme, logos, etc.
Connect to data sources and ensure data flows correctly.

4.2 Add Interactivity

Incorporate filters, drill-downs, and tooltips.
Ensure user-friendly navigation.
Key is user-friendliness

4.3 Test Functionality

Conduct unit testing for each component.
Validate data accuracy and performance.

Dashboard Development and Deployment Process

5. Review & Feedback

5.1 Stakeholder Review

Present the dashboard to stakeholders.
Gather feedback and suggestions for improvements.

5.2 Iteration

Make necessary adjustments based on feedback.
Conduct additional testing if required.

6. Deployment

6.1 Finalize Dashboard

Ensure all components are functioning as intended.
Prepare documentation for users.

6.2 Launch Dashboard

Deploy the dashboard to the production environment.
Notify stakeholders of the launch.

7. Training and Support

7.1 User Training

Conduct training sessions for end-users.
Provide user manuals and resources.

7.2 Ongoing Support

Establish a support channel for users.
Plan for regular updates and maintenance.

8. Evaluation and Improvement

8.1 Monitor Usage

Track dashboard usage and performance metrics.
Gather user feedback for future enhancements.

8.2 Continuous Improvement

Schedule periodic reviews to assess the dashboard's effectiveness.
Implement changes based on evolving business needs.

Checklist for Dashboard Project Process

[ ] Project Initiation (Estimated Duration: 3 days)
- [ ] Define project scope and objectives.
  1. Data Kick-off Call
  2. Business Questions
  3. Business Requirement Documents
- Identifying Key Roles / Stakeholders
  1. Requestor
  2. Target Viewers
  3. Data Engineering
  4. Data Analyst
- [ ] Gather requirements from stakeholders.
  1. Identify KPIs and Relevant Metrics
  2. Existing related Report/Dashboard
  3. Dashboard Sketch
- [ ] Create a project plan with timeline and resources.
  1. Define Project Milestones
  2. Schedule Recurring Sync-up Meetings

[ ] Data Collection and Preparation (Estimated Duration: 5 days)
- [ ] Conduct Data Discovery
- [ ] Identify and assess data sources.
- [ ] Extract and clean data.
  - [ ] Normalize data formats.
  - [ ] Conduct Exploratory Data Analysis.
    - [ ] Analyze data distributions.
    - [ ] Identify outliers.
    - [ ] Visualize relationships.
    - [ ] Check for missing values.

[ ] Dashboard Design (Estimated Duration: 4 days)
- [ ] Develop wireframes for dashboard design.
- [ ] Select visualization tools.
- [ ] Create prototypes and gather feedback.

[ ] Dashboard Development (Estimated Duration: 5 days)
- [ ] Build the dashboard and ensure data connectivity.
- [ ] Add interactivity and test functionality.

[ ] Review and Feedback (Estimated Duration: 3 days)
- Conduct Data
- [ ] Present dashboard to stakeholders for review.
- [ ] Make adjustments based on feedback.

[ ] Deployment (Estimated Duration: 2 days)
- [ ] Finalize and deploy the dashboard.
- [ ] Conduct user training and provide support.

[ ] Evaluation and Improvement (Ongoing)
- [ ] Monitor usage and plan for continuous improvement.
- [ ] Closed Feedback Loop

This structured approach ensures that Business Analysts can effectively manage dashboard projects, leading to successful outcomes that meet stakeholder expectations.

Free to use but don't forget to give credit. Please contact me : xian@xedarph.com

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