Centralized Data Model

Overview

This repository supports financial planning and analysis holistically, including datasets specific but not limited to:

• Sales performance relative to:
  • Opportunity
  • Product
  • Customer
• Revenue Operations
• Income Statement Transactions
• Aged Accounts Receivables

This repository supports financial planning and analysis holistically, including datasets specific but not limited to:

• Sales performance relative to:
  • Opportunity
  • Product
  • Customer
• Revenue Operations
• Income Statement Transactions
• Aged Accounts Receivables

/ ├── data/ # Raw or sample data files ├── src/ # Source code (scripts, notebooks, functions) ├── docs/ # Documentation, guides, and tutorials ├── tests/ # Automated tests ├── README.md # This file └── … # Other relevant files/folders

Domo Admin Tasks & Projects

I. General Administrative Tasks

1. Employ AI to generate precise meeting notes

   • Particularly during discussions with Edward.
   • Leverage Domo’s AI chat box for advice:
     • Tracking Customer Health Scores over time
     • Data warehouse governance, Schema management/maintenance
     • Dashboard recommendations based on the task set that’s feeding each dashboard

2. Conduct thorough research on business tools utilizing Grok/AI.

3. Develop Domo Newsletter

   • Consider incorporating various videos (excluding any financial or sensitive data).

4. Optimize datasets

   • Implementation of hash keys
   • Dataset partitioning
   • Creation of indexes

5. Deploy a Python script for dataset cleaning (with metadata output table)

   • Establish a table/dataset to systematically identify:
     1. Columns
     2. Beast Modes
     3. Variables
     4. Cards
     5. Data Sets / Data Flows—archive originals in GitHub repository
     6. Dashboards
   • Target removal of elements that:
     • Remain unused or underutilized
     • Contain predominantly null values
     • Closely mirror other high-value columns
     • Exhibit minimal traffic based on card viewership
   • Enforce column standardization—reference Opportunities with Products:
     • Ensure uniform names across diverse datasets and versions
     • Identify columns with differing names yet producing identical categories/measures
     • Scrutinize columns sharing value types (e.g., date, numeric, categorical) despite name variations
   • Introduce prefixes (e.g., Opportunity, Product, Solution) to column naming conventions
   • Organize datasets: date columns first, then sub-dimension groups, sub-fact groups, and appendix for ad-hoc elements

6. Construct a user group mapping table (anchored on Entity)

   • Leverage Governance Toolkit for:
     • Group Management / User Management (from Bamboo and Entity/Department)
     • Implement PDP Automation for Domo user governance and automated PDP protocols

7. Establish a repository mechanism

   • Ensure every query modification automatically uploads to GitHub

8. Investigate subscription options for Hex and/or alternative data warehousing tools

   • Evaluate Domo Workbench as a potential solution

9. Compute Z-Scores/Composite Scores

   • For advanced benchmarking on:
     • One Page Plan
     • P&C Scorecard

II. Functional Income Statement Dashboard

1. Integrate comprehensive logic and notes within the Summary section

2. Incorporate Actuals “Compare To” Variable with options

   • Budget, Annual, Quarterly, Trailing 3-Month Average, Monthly

3. Implement fixed start and end date filter variables (relative date configuration)

   • End Date: LAST_DAY(DATE_SUB(CURRENT_DATE(), INTERVAL 1 MONTH))
   • Start Date: [To be specified]

4. Include a display link to the data dictionary

III. Functional Income Statement Queries/Datasets

1. Functional Income Statement with Cross Join

   • Embed detailed logic and notes in the Summary SQL Query Template

2. Apply:

   • Updated functional income statement mapping logic
   • Budget amounts

3. Deploy to the dashboard, deprecating the original Functional Income Statement dataset

   • Archive the original in GitHub repository

4. Commit all changes to the repository

5. Transfer existing PDP rules

6. Migrate the data dictionary

   • Generate a new table or webform

7. Reduce granularity from Customer level to partitions solely based on:

   • "Entity"
   • "1-Account Group"
   • "4-Account Department"
   • "Account Number"

8. Document the applied logic:

   • Option 1: Filter transactions with post dates outside the Fiscal Year—exclude transactions beyond FY boundaries or >= current month

   • Option 2 (chosen):

     • Include all NetSuite transactions codified under a Fiscal Year, correct post months to FY Start & End Months for consistency (12-month uniformity)
     • Adjust Post Month display to cap at FY boundaries; exclude adjusted “Post Month”s >= current month

   • Cross join with date dimension

     • Generate all conceivable dimensional combinations per post month, even if no transaction occurred
     • Enforces consistency in period-over-period calculations and avoids missing months due to no source data