Saved Searches Overview

Saved searches are NetSuite's Swiss Army knife for data retrieval. They power reports, KPIs, portlets, dropdowns, script data sources, and mass updates. Mastering saved searches is essential for any NetSuite implementation.

Search Architecture

SAVED SEARCH STRUCTURE
═══════════════════════════════════════════════════════════════

┌─────────────────────────────────────────────────────────────┐
│                     SEARCH DEFINITION                        │
├─────────────────────────────────────────────────────────────┤
│  Record Type:  Transaction, Customer, Item, etc.            │
│  Search Title: Human-readable name                          │
│  Search ID:    customsearch_xxx (for scripting)             │
│  Public:       Available to all roles                       │
│  Audience:     Specific roles/employees                     │
└─────────────────────────────────────────────────────────────┘
                          │
          ┌───────────────┼───────────────┐
          ▼               ▼               ▼
┌─────────────┐   ┌─────────────┐   ┌─────────────┐
│  CRITERIA   │   │   RESULTS   │   │  AVAILABLE  │
│  (Filters)  │   │  (Columns)  │   │  (Filters)  │
├─────────────┤   ├─────────────┤   ├─────────────┤
│ Standard    │   │ Fields      │   │ User-facing │
│ filters     │   │ Formulas    │   │ filter      │
│ Summary     │   │ Summary     │   │ options at  │
│ type        │   │ functions   │   │ runtime     │
│ Filter      │   │ Sort order  │   │             │
│ expressions │   │ Links       │   │             │
└─────────────┘   └─────────────┘   └─────────────┘
                        

Filter Types

Formula Fields

Formula fields use SQL syntax to calculate values. Common functions:

COMMON FORMULA PATTERNS
═══════════════════════════════════════════════════════════════

DATE CALCULATIONS
───────────────────────────────────────────────────────────────
Days Since:      {today} - {trandate}
Days Until Due:  {duedate} - {today}
Month Name:      TO_CHAR({trandate}, 'Month YYYY')
Fiscal Quarter:  'Q' || TO_CHAR({trandate}, 'Q') || ' ' || TO_CHAR({trandate}, 'YYYY')

TEXT MANIPULATION
───────────────────────────────────────────────────────────────
Concatenate:     {firstname} || ' ' || {lastname}
Extract:         SUBSTR({tranid}, 1, 3)
Replace:         REPLACE({phone}, '-', '')
Upper/Lower:     UPPER({name}) / LOWER({email})

CONDITIONAL LOGIC
───────────────────────────────────────────────────────────────
Simple IF:       CASE WHEN {amount} > 0 THEN 'Credit' ELSE 'Debit' END
Nested:          CASE
                   WHEN {daysoverdue} > 90 THEN 'Critical'
                   WHEN {daysoverdue} > 60 THEN 'High'
                   WHEN {daysoverdue} > 30 THEN 'Medium'
                   ELSE 'Current'
                 END
Null Check:      NVL({memo}, 'No memo')
NullIf:          NULLIF({quantity}, 0)

NUMERIC CALCULATIONS
───────────────────────────────────────────────────────────────
Percentage:      ROUND({amount} / NULLIF({total}, 0) * 100, 2)
Running Total:   SUM({amount}) OVER (ORDER BY {trandate})
Absolute:        ABS({amount})
Round:           ROUND({rate}, 2)
                        

Summary Types

Summary searches aggregate data across records:

Joins and Related Records

SEARCH JOINS
═══════════════════════════════════════════════════════════════

TRANSACTION SEARCH (Invoice)
│
├── Main Record Fields
│   └── {tranid}, {trandate}, {amount}, {status}
│
├── Customer Join (entity...)
│   └── {entity.companyname}, {entity.email}, {entity.salesrep}
│
├── Item Join (item...)
│   └── {item.displayname}, {item.type}, {item.class}
│
├── Created By Join (createdby...)
│   └── {createdby.entityid}, {createdby.email}
│
├── Applied To Join (appliedtotransaction...)
│   └── {appliedtotransaction.tranid}, {appliedtotransaction.type}
│
└── Custom Record Join (custbody_xxx...)
    └── {custbody_project.custrecord_project_manager}

JOIN NOTATION:
───────────────────────────────────────────────────────────────
{fieldname}              Main record field
{join.fieldname}         Related record field
{join.join2.fieldname}   Nested join (2 levels max)
                        

Performance Optimization

Search Scheduling

Lists → Search → Saved Searches → Edit → Email

Implementation Checklist

Reports Overview

NetSuite provides hundreds of standard reports across all functional areas. Understanding report types and customization options helps deliver the right information to stakeholders.

Financial Report Builder

Reports → Financial → Financial Report Builder

The Financial Report Builder creates customized financial statements with full control over row/column structure:

FINANCIAL REPORT STRUCTURE
═══════════════════════════════════════════════════════════════

                    │ Jan 2025  │ Feb 2025  │ Mar 2025  │  Q1 Total │
────────────────────┼───────────┼───────────┼───────────┼───────────┤
REVENUE             │           │           │           │           │
  Product Sales     │  125,000  │  142,000  │  138,000  │   405,000 │
  Service Revenue   │   45,000  │   48,000  │   52,000  │   145,000 │
  Other Income      │    5,000  │    3,500  │    4,200  │    12,700 │
────────────────────┼───────────┼───────────┼───────────┼───────────┤
  TOTAL REVENUE     │  175,000  │  193,500  │  194,200  │   562,700 │
                    │           │           │           │           │
COST OF GOODS SOLD  │           │           │           │           │
  Direct Materials  │   50,000  │   56,800  │   55,200  │   162,000 │
  Direct Labor      │   25,000  │   28,400  │   27,600  │    81,000 │
────────────────────┼───────────┼───────────┼───────────┼───────────┤
  TOTAL COGS        │   75,000  │   85,200  │   82,800  │   243,000 │
                    │           │           │           │           │
GROSS PROFIT        │  100,000  │  108,300  │  111,400  │   319,700 │
  Gross Margin %    │    57.1%  │    56.0%  │    57.4%  │     56.8% │

ROW DEFINITIONS:
───────────────────────────────────────────────────────────────
• Account ranges (4000-4999)
• Account types (Income, Expense)
• Specific accounts
• Calculated rows (formulas)
• Subtotal rows
• Blank/header rows

COLUMN DEFINITIONS:
───────────────────────────────────────────────────────────────
• Date range (absolute or relative)
• Subsidiary/Location/Class/Department
• Budget vs. Actual
• Comparative periods
• Calculated columns (variance, %)
                        

Financial Report Builder Features

Report Customization

Standard reports can be customized without building from scratch:

Key Financial Reports

Report Scheduling

Schedule reports for automatic distribution:

SuiteAnalytics Workbook Overview

SuiteAnalytics Workbook provides a modern, Excel-like analytics experience within NetSuite. It overcomes many saved search limitations with multi-dataset joins, advanced pivoting, and rich visualizations.

Workbook Architecture

WORKBOOK STRUCTURE
═══════════════════════════════════════════════════════════════

WORKBOOK
├── DATA
│   ├── Dataset 1: Transactions
│   │   ├── Fields: Date, Amount, Customer, Item
│   │   └── Filters: Date range, Status
│   │
│   ├── Dataset 2: Customers
│   │   ├── Fields: Name, Industry, Sales Rep
│   │   └── Filters: Active only
│   │
│   └── Joins
│       └── Transactions.Customer = Customers.ID
│
├── PIVOT TABLES
│   ├── Pivot 1: Sales by Month x Customer
│   └── Pivot 2: Sales by Item Category x Region
│
├── CHARTS
│   ├── Chart 1: Monthly Trend Line
│   ├── Chart 2: Top 10 Customers Bar
│   └── Chart 3: Category Pie Chart
│
└── TABLES
    └── Table 1: Transaction Detail List
                        

Dataset Creation

Analytics → Dataset Builder → New Dataset

Dataset Components

Pivot Tables

Pivot tables summarize large datasets dynamically:

PIVOT TABLE EXAMPLE
═══════════════════════════════════════════════════════════════

CONFIGURATION:
  Rows:     Customer Industry
  Columns:  Fiscal Quarter
  Values:   Sum of Amount, Count of Transactions
  Filters:  Year = 2025

RESULT:
───────────────────────────────────────────────────────────────
                │    Q1 2025    │    Q2 2025    │    Q3 2025    │
Industry        │ Amount  Count │ Amount  Count │ Amount  Count │
────────────────┼───────────────┼───────────────┼───────────────┤
Manufacturing   │ $425K    142  │ $512K    168  │ $489K    155  │
Retail          │ $312K    287  │ $398K    324  │ $445K    356  │
Services        │ $189K     67  │ $223K     78  │ $256K     89  │
Technology      │ $567K     45  │ $623K     52  │ $701K     58  │
────────────────┼───────────────┼───────────────┼───────────────┤
Grand Total     │ $1.49M   541  │ $1.76M   622  │ $1.89M   658  │
                        

Visualizations

Multi-Dataset Joins

DATASET JOIN EXAMPLE
═══════════════════════════════════════════════════════════════

USE CASE: Customer Profitability Analysis

Dataset 1: Invoices
  └── Customer ID, Invoice Amount, Invoice Date

Dataset 2: Payments
  └── Customer ID, Payment Amount, Payment Date

Dataset 3: Support Cases
  └── Customer ID, Case Count, Resolution Hours

Dataset 4: Customers
  └── Customer ID, Name, Industry, Acquisition Date

JOINS:
───────────────────────────────────────────────────────────────
  Invoices.Customer  ──►  Customers.ID
  Payments.Customer  ──►  Customers.ID
  Cases.Customer     ──►  Customers.ID

RESULT: Single view with revenue, collections, and support cost
        by customer—impossible in a single saved search!
                        

Analytics Warehouse Overview

NetSuite Analytics Warehouse (NSAW) is a cloud data warehouse that replicates NetSuite data for advanced analytics. It enables enterprise BI capabilities without impacting transactional system performance.

NSAW Architecture

ANALYTICS WAREHOUSE ARCHITECTURE
═══════════════════════════════════════════════════════════════

┌─────────────────────────────────────────────────────────────┐
│                    NETSUITE ERP                              │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐            │
│  │ Transactions│ │  Customers  │ │    Items    │            │
│  └──────┬──────┘ └──────┬──────┘ └──────┬──────┘            │
└─────────┼───────────────┼───────────────┼───────────────────┘
          │               │               │
          ▼               ▼               ▼
┌─────────────────────────────────────────────────────────────┐
│               DATA REPLICATION (Near Real-Time)              │
└─────────────────────────────────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────┐
│              ANALYTICS WAREHOUSE (NSAW)                      │
│  ┌─────────────────────────────────────────────────────────┐│
│  │              PRE-BUILT DATA MODELS                       ││
│  │  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐       ││
│  │  │Financial│ │  Sales  │ │Procure- │ │Inventory│       ││
│  │  │   Cube  │ │   Cube  │ │ment Cube│ │   Cube  │       ││
│  │  └─────────┘ └─────────┘ └─────────┘ └─────────┘       ││
│  └─────────────────────────────────────────────────────────┘│
│  ┌─────────────────────────────────────────────────────────┐│
│  │              ANALYTICS INTERFACE                         ││
│  │  ┌─────────┐ ┌─────────┐ ┌─────────┐                   ││
│  │  │Dashboard│ │ Reports │ │  Query  │                   ││
│  │  │ Builder │ │ Builder │ │  Direct │                   ││
│  │  └─────────┘ └─────────┘ └─────────┘                   ││
│  └─────────────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────────────┘
          │               │               │
          ▼               ▼               ▼
┌─────────────┐   ┌─────────────┐   ┌─────────────┐
│  Embedded   │   │  External   │   │   Export    │
│  Dashboards │   │   BI Tools  │   │   (Excel)   │
└─────────────┘   └─────────────┘   └─────────────┘
                        

Pre-Built Analytics

NSAW includes ready-to-use analytics for common business needs:

Dashboard Builder

Create executive dashboards with drag-and-drop interface:

EXECUTIVE DASHBOARD EXAMPLE
═══════════════════════════════════════════════════════════════

┌─────────────────────────────────────────────────────────────┐
│                    CEO DASHBOARD                             │
├──────────────────────────┬──────────────────────────────────┤
│   REVENUE YTD            │   GROSS MARGIN TREND             │
│   ┌─────────────────┐    │   ┌─────────────────────────────┐│
│   │                 │    │   │    ╭──────╮                 ││
│   │   $12.4M        │    │   │ ───╯      ╰───              ││
│   │   ▲ 15% vs LY   │    │   │                             ││
│   │                 │    │   │  Jan Feb Mar Apr May Jun    ││
│   └─────────────────┘    │   └─────────────────────────────┘│
├──────────────────────────┼──────────────────────────────────┤
│   TOP 10 CUSTOMERS       │   SALES BY REGION                │
│   ┌─────────────────┐    │   ┌─────────────────────────────┐│
│   │ Acme Corp  $2.1M│    │   │     ████ West 35%          ││
│   │ Beta Inc   $1.8M│    │   │     ████ East 28%          ││
│   │ Gamma LLC  $1.2M│    │   │     ████ Central 22%       ││
│   │ ...            │    │   │     ████ International 15%  ││
│   └─────────────────┘    │   └─────────────────────────────┘│
├──────────────────────────┴──────────────────────────────────┤
│   MONTHLY REVENUE TREND                                      │
│   ┌─────────────────────────────────────────────────────────┐│
│   │         ╭─────╮                                         ││
│   │    ╭────╯     ╰────╮        ╭─────                      ││
│   │ ───╯               ╰────────╯                           ││
│   │  Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct       ││
│   └─────────────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────────────┘
                        

When to Use NSAW

SuiteAnalytics Connect Overview

SuiteAnalytics Connect provides direct database-style access to NetSuite data via ODBC/JDBC drivers, enabling integration with external BI tools, custom applications, and advanced SQL queries.

Connection Architecture

SUITEANALYTICS CONNECT ARCHITECTURE
═══════════════════════════════════════════════════════════════

┌─────────────────────────────────────────────────────────────┐
│                    EXTERNAL TOOLS                            │
│  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐        │
│  │  Excel  │  │Power BI │  │ Tableau │  │ Custom  │        │
│  │         │  │         │  │         │  │   App   │        │
│  └────┬────┘  └────┬────┘  └────┬────┘  └────┬────┘        │
└───────┼────────────┼────────────┼────────────┼──────────────┘
        │            │            │            │
        ▼            ▼            ▼            ▼
┌─────────────────────────────────────────────────────────────┐
│              SUITEANALYTICS CONNECT                          │
│  ┌─────────────────────────┐  ┌─────────────────────────┐   │
│  │      ODBC/JDBC          │  │       SuiteQL           │   │
│  │       Drivers           │  │       REST API          │   │
│  └───────────┬─────────────┘  └───────────┬─────────────┘   │
│              │                            │                  │
│              ▼                            ▼                  │
│  ┌─────────────────────────────────────────────────────────┐│
│  │              CONNECT SERVICE                             ││
│  │  • Authentication (Token-based)                         ││
│  │  • Query Processing                                      ││
│  │  • Role-based Data Access                                ││
│  │  • Governance Limits                                     ││
│  └─────────────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────────────┘
                          │
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                    NETSUITE DATA                             │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐            │
│  │ Transactions│ │  Entities   │ │    Items    │            │
│  └─────────────┘ └─────────────┘ └─────────────┘            │
└─────────────────────────────────────────────────────────────┘
                        

SuiteQL

SuiteQL is NetSuite's SQL-like query language for direct data access:

SUITEQL EXAMPLES
═══════════════════════════════════════════════════════════════

BASIC SELECT
───────────────────────────────────────────────────────────────
SELECT
    t.tranid,
    t.trandate,
    e.companyname AS customer,
    t.foreigntotal AS amount
FROM
    transaction t
    JOIN entity e ON t.entity = e.id
WHERE
    t.type = 'CustInvc'
    AND t.trandate >= TO_DATE('2025-01-01', 'YYYY-MM-DD')
ORDER BY
    t.trandate DESC

AGGREGATION
───────────────────────────────────────────────────────────────
SELECT
    TO_CHAR(t.trandate, 'YYYY-MM') AS month,
    COUNT(*) AS invoice_count,
    SUM(t.foreigntotal) AS total_amount,
    AVG(t.foreigntotal) AS avg_amount
FROM
    transaction t
WHERE
    t.type = 'CustInvc'
    AND t.trandate >= ADD_MONTHS(SYSDATE, -12)
GROUP BY
    TO_CHAR(t.trandate, 'YYYY-MM')
ORDER BY
    month

JOIN MULTIPLE TABLES
───────────────────────────────────────────────────────────────
SELECT
    c.companyname,
    c.email,
    COUNT(t.id) AS order_count,
    SUM(t.foreigntotal) AS lifetime_value,
    MAX(t.trandate) AS last_order_date
FROM
    customer c
    LEFT JOIN transaction t ON c.id = t.entity
        AND t.type = 'SalesOrd'
GROUP BY
    c.id, c.companyname, c.email
HAVING
    SUM(t.foreigntotal) > 10000
ORDER BY
    lifetime_value DESC
                        

ODBC Setup

Configure ODBC connection for external tools:

Governance & Limits

Common Use Cases

Industry KPI Overview

Different industries focus on different metrics. Understanding industry-specific KPIs helps build relevant dashboards and reports that drive business decisions.

Building Industry Dashboards

DASHBOARD DESIGN FRAMEWORK
═══════════════════════════════════════════════════════════════

1. IDENTIFY KEY QUESTIONS
   └── What decisions does this dashboard inform?

2. SELECT METRICS (5-7 MAX)
   ├── Leading indicators (predict future)
   └── Lagging indicators (measure past)

3. DEFINE DATA SOURCES
   ├── Saved searches for real-time
   ├── Workbooks for analysis
   └── NSAW for historical trends

4. DESIGN LAYOUT
   ├── Big numbers for KPIs
   ├── Trends for patterns
   └── Tables for details

5. SET REFRESH CADENCE
   ├── Real-time for operational
   ├── Daily for management
   └── Weekly/Monthly for executive

6. CONFIGURE ALERTS
   └── Threshold-based notifications
                        

Implementation Checklist

NSAW Architecture Overview

NETSUITE ANALYTICS WAREHOUSE ARCHITECTURE
═══════════════════════════════════════════════════════════════════

    NETSUITE ERP                              NSAW (Oracle Cloud)
┌───────────────────┐                    ┌────────────────────────────┐
│ Transaction Data  │                    │  DIMENSIONAL DATA MODEL    │
│  - Sales Orders   │   ETL Pipeline     │  ┌──────────────────────┐  │
│  - Invoices       │ ─────────────────→ │  │ FACT TABLES          │  │
│  - Payments       │   (Nightly Sync)   │  │  - Transaction Lines │  │
│  - Inventory      │                    │  │  - GL Journal Lines  │  │
│                   │                    │  │  - Item Fulfillments │  │
│ Master Data       │                    │  └──────────────────────┘  │
│  - Customers      │                    │  ┌──────────────────────┐  │
│  - Items          │                    │  │ DIMENSION TABLES     │  │
│  - Vendors        │                    │  │  - Customer_Dim      │  │
│  - Employees      │                    │  │  - Item_Dim          │  │
└───────────────────┘                    │  │  - Time_Dim          │  │
                                         │  │  - Location_Dim      │  │
                                         │  └──────────────────────┘  │
                                         │                            │
                                         │  QUERY ENGINE              │
                                         │  - Oracle Analytics Cloud  │
                                         │  - SQL Interface           │
                                         │  - BI Publisher            │
                                         └────────────────────────────┘

KEY CHARACTERISTICS:
• Data refresh: Nightly (typically 2-4 AM account time zone)
• Latency: T-1 (yesterday's data by morning)
• Retention: Historical data preserved indefinitely
• Scale: Billions of rows supported
• Access: SQL, Workbooks, Connect, REST API
                        

Enabling NSAW

PATH: Setup → Company → Enable Features → Analytics

Requirements:
  □ NetSuite Analytics Warehouse SKU purchased
  □ Administrator role for initial setup
  □ Data sync must complete (24-48 hours initial load)

Feature Checkbox:
  ☑ NetSuite Analytics Warehouse

Post-Enable Steps:
  1. Wait for initial data sync (check status in Setup → Analytics)
  2. Configure data retention policies
  3. Set up user access (Analytics role permissions)
  4. Review default subject areas

Status Check:
  Path: Setup → Analytics → SuiteAnalytics Warehouse → Sync Status

  Status: ✓ Sync Complete
  Last Sync: 2024-12-10 03:42:15
  Records Synced: 12,847,293
  Next Scheduled: 2024-12-11 02:00:00
                        

NSAW Subject Areas

NSAW organizes data into pre-built subject areas for common analytics needs.

Subject Area	Primary Facts	Key Dimensions	Use Cases
Transaction Lines	Amount, Quantity, Rate	Item, Customer, Date, Location	Sales analysis, margin reports
GL Journal Lines	Debit, Credit, Amount	Account, Period, Department, Class	Financial reporting, trial balance
Inventory Balance	On Hand, Available, On Order	Item, Location, Bin	Stock analysis, reorder planning
A/R Aging	Balance, Days Outstanding	Customer, Invoice, Aging Bucket	Collections, credit risk
A/P Aging	Balance, Days Outstanding	Vendor, Bill, Aging Bucket	Cash management, vendor payments
Budget	Budget Amount	Account, Period, Department	Budget vs actual, variance
Time Tracking	Hours, Billable Amount	Employee, Customer, Project	Utilization, project costing

SQL Query Patterns

NSAW supports standard SQL via SuiteAnalytics Connect. Here are essential query patterns:

Pattern 1: Sales Trend Analysis

-- Monthly Sales by Customer Segment
SELECT
    TO_CHAR(t.trandate, 'YYYY-MM') AS month,
    c.category AS customer_segment,
    SUM(tl.netamount) AS revenue,
    COUNT(DISTINCT t.id) AS order_count,
    COUNT(DISTINCT t.entity) AS unique_customers
FROM
    transaction t
    JOIN transactionline tl ON t.id = tl.transaction
    JOIN customer c ON t.entity = c.id
WHERE
    t.type = 'SalesOrd'
    AND t.trandate >= ADD_MONTHS(SYSDATE, -12)
    AND tl.mainline = 'F'
GROUP BY
    TO_CHAR(t.trandate, 'YYYY-MM'),
    c.category
ORDER BY
    month, revenue DESC;
                        

Pattern 2: Inventory Turnover

-- Inventory Turnover by Product Category
WITH cogs AS (
    SELECT
        i.custitem_category AS category,
        SUM(tl.costestimate) AS total_cogs
    FROM
        transactionline tl
        JOIN item i ON tl.item = i.id
        JOIN transaction t ON tl.transaction = t.id
    WHERE
        t.type IN ('CustInvc', 'CashSale')
        AND t.trandate BETWEEN
            ADD_MONTHS(TRUNC(SYSDATE, 'YEAR'), -12)
            AND TRUNC(SYSDATE, 'YEAR') - 1
        AND tl.mainline = 'F'
    GROUP BY i.custitem_category
),
avg_inventory AS (
    SELECT
        i.custitem_category AS category,
        AVG(ib.quantityonhand * i.averagecost) AS avg_inv_value
    FROM
        inventorybalance ib
        JOIN item i ON ib.item = i.id
    GROUP BY i.custitem_category
)
SELECT
    c.category,
    c.total_cogs,
    ai.avg_inv_value,
    ROUND(c.total_cogs / NULLIF(ai.avg_inv_value, 0), 2) AS turnover_ratio,
    ROUND(365 / NULLIF(c.total_cogs / ai.avg_inv_value, 0), 1) AS days_on_hand
FROM
    cogs c
    JOIN avg_inventory ai ON c.category = ai.category
ORDER BY
    turnover_ratio DESC;
                        

Pattern 3: Customer Lifetime Value

-- Customer LTV with Cohort Analysis
WITH first_purchase AS (
    SELECT
        entity,
        MIN(trandate) AS first_order_date,
        TO_CHAR(MIN(trandate), 'YYYY-Q') AS cohort
    FROM transaction
    WHERE type IN ('SalesOrd', 'CustInvc')
    GROUP BY entity
),
customer_revenue AS (
    SELECT
        t.entity,
        fp.cohort,
        fp.first_order_date,
        SUM(t.foreigntotal) AS total_revenue,
        COUNT(DISTINCT t.id) AS order_count,
        MAX(t.trandate) AS last_order_date,
        ROUND(MONTHS_BETWEEN(MAX(t.trandate), fp.first_order_date)) AS tenure_months
    FROM
        transaction t
        JOIN first_purchase fp ON t.entity = fp.entity
    WHERE
        t.type IN ('SalesOrd', 'CustInvc')
    GROUP BY
        t.entity, fp.cohort, fp.first_order_date
)
SELECT
    cohort,
    COUNT(*) AS customers,
    ROUND(AVG(total_revenue), 2) AS avg_ltv,
    ROUND(AVG(order_count), 1) AS avg_orders,
    ROUND(AVG(tenure_months), 1) AS avg_tenure_months,
    ROUND(SUM(total_revenue), 0) AS cohort_total_revenue
FROM customer_revenue
GROUP BY cohort
ORDER BY cohort;
                        

Performance Optimization

Technique	Implementation	Impact
Filter Early	Apply WHERE clauses before JOINs	Reduce rows processed by 50-90%
Date Partitioning	Always filter on trandate or period	Leverage partition pruning
Select Specific Columns	Avoid SELECT *; list needed columns	Reduce I/O by 60-80%
Use Summary Tables	Pre-aggregate for common queries	10-100x faster for dashboards
Limit Result Sets	Use FETCH FIRST n ROWS	Faster response for exploration
Avoid Functions on Indexed Columns	Use range filters instead of TO_CHAR	Enable index usage

QUERY OPTIMIZATION EXAMPLE
═══════════════════════════════════════════════════════════════════

❌ SLOW: Function on indexed column
SELECT * FROM transaction
WHERE TO_CHAR(trandate, 'YYYY') = '2024';

✓ FAST: Range filter
SELECT * FROM transaction
WHERE trandate >= DATE '2024-01-01'
  AND trandate < DATE '2025-01-01';

❌ SLOW: SELECT *
SELECT * FROM transactionline tl
JOIN transaction t ON tl.transaction = t.id;

✓ FAST: Specific columns
SELECT t.tranid, t.trandate, tl.item, tl.quantity, tl.amount
FROM transactionline tl
JOIN transaction t ON tl.transaction = t.id;

❌ SLOW: Late filtering
SELECT ... FROM large_table
JOIN another_table ON ...
WHERE large_table.date > SYSDATE - 30;

✓ FAST: Filter in subquery
SELECT ... FROM (
    SELECT * FROM large_table WHERE date > SYSDATE - 30
) filtered
JOIN another_table ON ...;
                        

NSAW vs Other Analytics Tools

Capability	Saved Searches	Workbooks	NSAW
Data Freshness	Real-time	Real-time	T-1 (nightly sync)
Historical Depth	Current data only	Current data only	Years of history
Query Complexity	Limited joins	Multi-dataset	Full SQL capability
Performance at Scale	Degrades >100K rows	Good to 1M rows	Billions of rows
External BI Tools	CSV export only	Limited	JDBC/ODBC native
Best For	Operational reports	Self-service analytics	Strategic analytics

Workbook Architecture

WORKBOOK COMPONENT HIERARCHY
═══════════════════════════════════════════════════════════════════

WORKBOOK
│
├── DATASETS (data sources)
│   ├── Primary Dataset (required)
│   │   ├── Record Type (Transaction, Customer, etc.)
│   │   ├── Criteria (filters)
│   │   └── Fields
│   │
│   └── Linked Datasets (optional)
│       └── Join conditions to primary
│
├── DATA DEFINITIONS
│   ├── Calculated Fields (formulas)
│   ├── Groupings (hierarchies)
│   └── Filters (user parameters)
│
└── VISUALIZATIONS
    ├── Tables (raw data view)
    ├── Pivot Tables (aggregations)
    ├── Charts (bar, line, pie, etc.)
    └── KPI Tiles (single metrics)

EXECUTION FLOW:
Datasets → Apply Criteria → Calculate Fields →
Group/Aggregate → Filter → Render Visualization
                        

Dataset Design Patterns

Pattern 1: Transaction Analysis Dataset

DATASET: Sales Transaction Analysis
═══════════════════════════════════════════════════════════════════

Record Type: Transaction

Criteria:
  Type: is any of [Sales Order, Invoice, Cash Sale]
  Main Line: is false
  Date: within last 365 days

Fields:
  DIMENSIONS                      MEASURES
  ├── Transaction > Date          ├── Amount
  ├── Transaction > Document #    ├── Quantity
  ├── Customer > Name             ├── Cost
  ├── Customer > Category         ├── Tax Amount
  ├── Item > Name                 └── Foreign Amount
  ├── Item > Type
  ├── Location > Name
  ├── Department > Name
  └── Class > Name

Join Pattern:
  Transaction (main)
    └── Customer (entity field)
    └── Item (item field)
    └── Location (location field)
                        

Pattern 2: Multi-Dataset Customer 360

DATASET: Customer 360 View
═══════════════════════════════════════════════════════════════════

Primary Dataset: Customer
  Record Type: Customer
  Fields: ID, Name, Category, Sales Rep, Date Created

Linked Dataset 1: Sales History
  Record Type: Transaction
  Join: Transaction.Entity = Customer.ID
  Criteria: Type in [Invoice, Cash Sale], Date > 1 year ago
  Fields: SUM(Amount) as Total Revenue, COUNT(*) as Order Count

Linked Dataset 2: Open A/R
  Record Type: Transaction
  Join: Transaction.Entity = Customer.ID
  Criteria: Type = Invoice, Status = Open
  Fields: SUM(Amount Due) as Open Balance, MAX(Days Overdue)

Linked Dataset 3: Support Cases
  Record Type: Support Case
  Join: Case.Company = Customer.ID
  Fields: COUNT(*) as Case Count, COUNT(Open) as Open Cases

Result: Single row per customer with aggregated metrics
                        

Calculated Field Formulas

Use Case	Formula	Notes
Gross Margin %	({amount} - {cost}) / {amount} * 100	Handle divide by zero
Year-over-Year	({current_year} - {prior_year}) / {prior_year} * 100	Requires date grouping
Running Total	SUM({amount}) OVER (ORDER BY {date})	Window function
Rank	RANK() OVER (PARTITION BY {category} ORDER BY {amount} DESC)	Within groups
Days Between	DATEDIFF({end_date}, {start_date})	Date arithmetic
Fiscal Period	CONCAT('FY', SUBSTR({posting_period}, 1, 4), '-Q', CEIL(MONTH({date})/3))	Custom periods

Advanced Formula Examples

CALCULATED FIELD EXAMPLES
═══════════════════════════════════════════════════════════════════

-- Customer Tier Based on Revenue
CASE
    WHEN {total_revenue} >= 100000 THEN 'Enterprise'
    WHEN {total_revenue} >= 50000 THEN 'Mid-Market'
    WHEN {total_revenue} >= 10000 THEN 'SMB'
    ELSE 'Starter'
END

-- Aging Bucket
CASE
    WHEN {days_overdue} <= 0 THEN 'Current'
    WHEN {days_overdue} <= 30 THEN '1-30 Days'
    WHEN {days_overdue} <= 60 THEN '31-60 Days'
    WHEN {days_overdue} <= 90 THEN '61-90 Days'
    ELSE '90+ Days'
END

-- Percentage of Total (within group)
{amount} / SUM({amount}) OVER (PARTITION BY {category}) * 100

-- Moving Average (3 periods)
AVG({amount}) OVER (
    ORDER BY {date}
    ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
)

-- First Purchase Flag
CASE
    WHEN {date} = MIN({date}) OVER (PARTITION BY {customer})
    THEN 'First Order'
    ELSE 'Repeat'
END

-- Contribution Margin
{amount} - {variable_cost} - ({amount} * {commission_rate})
                        

Pivot Table Techniques

PIVOT TABLE CONFIGURATION
═══════════════════════════════════════════════════════════════════

EXAMPLE: Sales by Region and Product Category

ROWS (Dimensions):
  Level 1: Region
  Level 2: Sales Rep

COLUMNS (Time/Category):
  Level 1: Fiscal Year
  Level 2: Fiscal Quarter

VALUES (Measures):
  □ Revenue: SUM(Amount)
  □ Margin: SUM(Amount - Cost)
  □ Margin %: (SUM(Amount) - SUM(Cost)) / SUM(Amount)
  □ Order Count: COUNT(DISTINCT Transaction ID)

Result Layout:
┌────────────────────────────────────────────────────────────────┐
│                    │   FY2024                │   FY2025       │
│                    │  Q1  │  Q2  │  Q3  │  Q4  │  Q1  │  Q2  │
├────────────────────┼──────┼──────┼──────┼──────┼──────┼──────┤
│ West Region        │      │      │      │      │      │      │
│   ├── John Smith   │ 125K │ 142K │ 138K │ 165K │ 155K │ 172K │
│   └── Jane Doe     │  98K │ 105K │ 112K │ 128K │ 135K │ 141K │
│ East Region        │      │      │      │      │      │      │
│   ├── Bob Wilson   │ 145K │ 158K │ 162K │ 175K │ 168K │ 182K │
│   └── ...          │      │      │      │      │      │      │
├────────────────────┼──────┼──────┼──────┼──────┼──────┼──────┤
│ Grand Total        │ 856K │ 924K │ 967K │1.2M  │1.1M  │1.3M  │
└────────────────────────────────────────────────────────────────┘

PIVOT OPTIONS:
  ☑ Show row totals
  ☑ Show column totals
  ☑ Show % of total
  ☐ Show variance
  ☑ Conditional formatting (heat map)
                        

Chart Types and Best Practices

Chart Type	Best For	Avoid When
Bar Chart	Comparing categories, rankings	>12 categories, time series
Line Chart	Trends over time, continuous data	Categorical comparisons
Pie/Donut	Part-to-whole (2-5 segments)	>5 segments, comparing sizes
Stacked Bar	Composition over categories	Comparing individual segments
Area Chart	Cumulative values, volume	Precise value comparison
Scatter Plot	Correlation, distribution	Few data points, categories
KPI Tile	Single critical metric	Multiple related metrics

Workbook Performance Optimization

PERFORMANCE CHECKLIST
═══════════════════════════════════════════════════════════════════

□ LIMIT DATE RANGE
  - Default to 90 days or current quarter
  - Provide date filter for users to expand

□ FILTER RECORD TYPES
  - Only include needed transaction types
  - Use Main Line = false for line details

□ SELECT ONLY NEEDED FIELDS
  - Don't add fields "just in case"
  - Each field increases query size

□ USE CRITERIA vs FILTERS
  - Criteria: Applied at data load (faster)
  - Filters: Applied at render (flexible but slower)

□ LIMIT LINKED DATASETS
  - Each link = additional query
  - Pre-aggregate where possible

□ OPTIMIZE CALCULATED FIELDS
  - Simple math > complex CASE statements
  - Avoid nested window functions

□ TEST WITH PRODUCTION DATA VOLUMES
  - Sandbox may have less data
  - Test with 100K+ rows

TYPICAL PERFORMANCE TARGETS:
  < 5 seconds: Excellent
  5-15 seconds: Acceptable
  15-30 seconds: Needs optimization
  > 30 seconds: Redesign required
                        

Common Workbook Patterns

Executive Summary Workbook

WORKBOOK: Executive KPI Dashboard
═══════════════════════════════════════════════════════════════════

Page 1: Financial Overview
┌─────────────────────────────────────────────────────────────────┐
│  [Revenue YTD]   [Margin %]   [Cash Balance]   [A/R Days]      │
│    $4.2M          42.3%         $1.8M            38            │
│    ▲ 12% YoY      ▲ 2.1pts      ▲ $200K          ▼ 3 days      │
├─────────────────────────────────────────────────────────────────┤
│ [Revenue Trend - Line Chart]        [Revenue by Segment - Pie] │
│ 12-month trend with forecast        Top 5 + Other              │
├─────────────────────────────────────────────────────────────────┤
│ [Top 10 Customers - Bar]            [Regional Performance]     │
│ YTD revenue + vs prior year         Heat map by region/month   │
└─────────────────────────────────────────────────────────────────┘

Page 2: Operational Metrics
  - Order fulfillment rate
  - Inventory turnover
  - Customer acquisition cost
  - Support ticket resolution

Page 3: Drill-Down Tables
  - Customer detail with filters
  - Product performance
  - Regional breakdown
                        

Implementation Checklist

Dashboard Design Principles

THE DASHBOARD HIERARCHY OF NEEDS
═══════════════════════════════════════════════════════════════════

Level 4: ACTIONABLE      "I know what to do next"
         ▲               Alerts, thresholds, drill-downs
         │
Level 3: CONTEXTUAL      "I understand the 'so what?'"
         │               Trends, comparisons, targets
         │
Level 2: INFORMATIVE     "I see the current state"
         │               KPIs, metrics, status
         │
Level 1: ACCESSIBLE      "I can find the data"
         │               Navigation, search, shortcuts

Most dashboards fail at Level 4. Users see data but don't
know what action to take. Every metric should answer:
"If this changes, what should I do?"
                        

Role-Based Dashboard Templates

Executive Dashboard

ROLE: CEO / CFO / Executive
PURPOSE: Strategic overview, exception-based management
REFRESH: Daily (morning)
═══════════════════════════════════════════════════════════════════

LAYOUT (3 columns):
┌────────────────────────────────────────────────────────────────┐
│ ROW 1: KPI TILES (Critical Metrics)                            │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐           │
│ │ Revenue  │ │ Margin   │ │ Cash     │ │ Pipeline │           │
│ │ MTD      │ │ %        │ │ Position │ │ Value    │           │
│ │ $2.4M    │ │ 41.2%    │ │ $3.1M    │ │ $8.7M    │           │
│ │ ▲ 8%     │ │ ▼ 1.2pts │ │ ▲ $400K  │ │ ▲ 12%    │           │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘           │
├────────────────────────────────────────────────────────────────┤
│ ROW 2: TRENDS & COMPARISON                                     │
│ ┌─────────────────────────────┐ ┌─────────────────────────────┐│
│ │ Revenue vs Plan (Line)      │ │ Revenue by Segment (Pie)    ││
│ │ Actual | Budget | Forecast  │ │ Product mix breakdown       ││
│ └─────────────────────────────┘ └─────────────────────────────┘│
├────────────────────────────────────────────────────────────────┤
│ ROW 3: EXCEPTIONS & ACTIONS                                    │
│ ┌─────────────────────────────┐ ┌─────────────────────────────┐│
│ │ Deals Requiring Attention   │ │ Upcoming Commitments        ││
│ │ - Stalled opportunities     │ │ - Large payments due        ││
│ │ - High-risk renewals        │ │ - Contract expirations      ││
│ └─────────────────────────────┘ └─────────────────────────────┘│
└────────────────────────────────────────────────────────────────┘

PORTLETS USED:
  - KPI Scorecard (custom portlet or Key Performance Indicators)
  - Saved Search (chart) - Revenue trend
  - Saved Search (chart) - Segment breakdown
  - Saved Search (list) - Exception alerts
  - Reminders portlet - Upcoming items
                        

Sales Manager Dashboard

ROLE: Sales Manager
PURPOSE: Team performance, pipeline management
REFRESH: Real-time
═══════════════════════════════════════════════════════════════════

LAYOUT (3 columns):
┌────────────────────────────────────────────────────────────────┐
│ ROW 1: TEAM PERFORMANCE                                        │
│ ┌─────────────────────────────┐ ┌─────────────────────────────┐│
│ │ Quota Attainment by Rep     │ │ Pipeline by Stage           ││
│ │ (Horizontal Bar)            │ │ (Funnel Chart)              ││
│ │ Green/Yellow/Red status     │ │ Qualification → Close       ││
│ └─────────────────────────────┘ └─────────────────────────────┘│
├────────────────────────────────────────────────────────────────┤
│ ROW 2: ACTIVITY & VELOCITY                                     │
│ ┌─────────────────────────────┐ ┌─────────────────────────────┐│
│ │ Activities This Week        │ │ Win Rate Trend (Line)       ││
│ │ Calls | Meetings | Demos    │ │ Last 6 months by rep        ││
│ │ vs Target                   │ │                             ││
│ └─────────────────────────────┘ └─────────────────────────────┘│
├────────────────────────────────────────────────────────────────┤
│ ROW 3: DEAL FOCUS                                              │
│ ┌──────────────────────────────────────────────────────────────┐│
│ │ Opportunities Closing This Month (Table)                     ││
│ │ Account | Deal | Value | Stage | Probability | Next Step    ││
│ └──────────────────────────────────────────────────────────────┘│
├────────────────────────────────────────────────────────────────┤
│ ROW 4: QUICK LINKS                                             │
│ [Create Lead] [Create Opportunity] [Pipeline Report] [Forecast]│
└────────────────────────────────────────────────────────────────┘
                        

Operations Manager Dashboard

ROLE: Operations / Fulfillment Manager
PURPOSE: Order status, inventory alerts, shipment tracking
REFRESH: Real-time
═══════════════════════════════════════════════════════════════════

LAYOUT (3 columns):
┌────────────────────────────────────────────────────────────────┐
│ ROW 1: TODAY'S PRIORITIES                                      │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐           │
│ │ Orders   │ │ Orders   │ │ Back-    │ │ Late     │           │
│ │ to Ship  │ │ Pending  │ │ orders   │ │ Shipments│           │
│ │   47     │ │   12     │ │   8      │ │   3      │           │
│ │ ▼ 5 vs   │ │ Approval │ │ ▲ 2 new  │ │ ⚠ Action │           │
│ │ yesterday│ │          │ │          │ │ needed   │           │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘           │
├────────────────────────────────────────────────────────────────┤
│ ROW 2: ORDER QUEUE                                             │
│ ┌──────────────────────────────────────────────────────────────┐│
│ │ Orders Pending Fulfillment (Table - Sortable)                ││
│ │ Order# | Customer | Items | Ship By | Status | [Fulfill]    ││
│ │ Color-coded by urgency (today=red, tomorrow=yellow)         ││
│ └──────────────────────────────────────────────────────────────┘│
├────────────────────────────────────────────────────────────────┤
│ ROW 3: INVENTORY ALERTS                                        │
│ ┌─────────────────────────────┐ ┌─────────────────────────────┐│
│ │ Below Reorder Point         │ │ Inbound Shipments           ││
│ │ Items needing restock       │ │ Expected this week          ││
│ │ Link to PO creation         │ │ PO# | Vendor | ETA          ││
│ └─────────────────────────────┘ └─────────────────────────────┘│
├────────────────────────────────────────────────────────────────┤
│ ROW 4: PERFORMANCE METRICS                                     │
│ ┌─────────────────────────────┐ ┌─────────────────────────────┐│
│ │ Order Cycle Time (Trend)    │ │ Fill Rate (Gauge)           ││
│ │ Target: < 24 hours          │ │ Target: > 98%               ││
│ └─────────────────────────────┘ └─────────────────────────────┘│
└────────────────────────────────────────────────────────────────┘
                        

Accounting Team Dashboard

ROLE: Controller / Accounting Manager
PURPOSE: Period close status, reconciliation, compliance
REFRESH: Daily
═══════════════════════════════════════════════════════════════════

LAYOUT (3 columns):
┌────────────────────────────────────────────────────────────────┐
│ ROW 1: PERIOD STATUS                                           │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐           │
│ │ Unposted │ │ A/R      │ │ A/P      │ │ Bank     │           │
│ │ JEs      │ │ Unappld  │ │ Unappld  │ │ Unrec'd  │           │
│ │   15     │ │ $42K     │ │ $28K     │ │ $156K    │           │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘           │
├────────────────────────────────────────────────────────────────┤
│ ROW 2: CLOSE CHECKLIST                                         │
│ ┌──────────────────────────────────────────────────────────────┐│
│ │ Month-End Close Tasks                                        ││
│ │ ☑ Revenue recognition                                        ││
│ │ ☑ Inventory valuation                                        ││
│ │ ☐ Depreciation run                                           ││
│ │ ☐ Intercompany reconciliation                                ││
│ │ ☐ Bank reconciliation                                        ││
│ │ ☐ Review trial balance                                       ││
│ └──────────────────────────────────────────────────────────────┘│
├────────────────────────────────────────────────────────────────┤
│ ROW 3: CASH POSITION                                           │
│ ┌─────────────────────────────┐ ┌─────────────────────────────┐│
│ │ Cash Flow Forecast          │ │ A/R Aging Summary           ││
│ │ (13-week rolling)           │ │ (Stacked bar by bucket)     ││
│ └─────────────────────────────┘ └─────────────────────────────┘│
├────────────────────────────────────────────────────────────────┤
│ ROW 4: APPROVALS QUEUE                                         │
│ ┌──────────────────────────────────────────────────────────────┐│
│ │ Pending My Approval (Journal Entries, Vendor Bills)          ││
│ │ Quick approve/reject from dashboard                          ││
│ └──────────────────────────────────────────────────────────────┘│
└────────────────────────────────────────────────────────────────┘
                        

Portlet Types Reference

Portlet Type	Use Case	Configuration Tips
Key Performance Indicators	Big number metrics with trend	Limit to 4-6 KPIs; show variance
Saved Search (List)	Tabular data, action lists	Limit rows; add inline edit if possible
Saved Search (Chart)	Visual trends, comparisons	Choose chart type carefully; less is more
Reminders	Upcoming tasks, follow-ups	Customize reminder types per role
Quick Search	Navigation shortcut	Position prominently for data entry roles
Shortcuts	Frequently used transactions	Limit to 5-8 most common actions
Analytics (Workbook)	Self-service drill-down	Embed focused workbook views
Custom Portlet (SuiteScript)	Custom UI/logic	Use for complex conditional display

Dashboard Layout Guidelines

LAYOUT BEST PRACTICES
═══════════════════════════════════════════════════════════════════

1. VISUAL HIERARCHY
   ┌─────────────────────────────────────────────────────────────┐
   │ TOP: Most critical metrics (KPIs, alerts)                   │
   │      User's eye starts here                                 │
   ├─────────────────────────────────────────────────────────────┤
   │ MIDDLE: Supporting context (trends, breakdowns)             │
   │         Answers "why" for top metrics                       │
   ├─────────────────────────────────────────────────────────────┤
   │ BOTTOM: Detail data (tables, lists)                         │
   │         Drill-down and action items                         │
   └─────────────────────────────────────────────────────────────┘

2. COLUMN USAGE
   1 column: Mobile/narrow views
   2 columns: Most common, balanced layout
   3 columns: Information-dense for wide screens

3. PORTLET SIZING
   Small: KPI tiles, shortcuts
   Medium: Charts, short lists
   Large: Data tables, calendars

4. WHITESPACE
   Don't fill every pixel
   Group related portlets
   Separate logical sections

5. REFRESH RATES
   Real-time: Operational dashboards
   Cached: Executive summaries
   Manual: Heavy analytics

ANTI-PATTERNS TO AVOID:
  ✗ Too many portlets (>12)
  ✗ Duplicate information in multiple portlets
  ✗ Charts without context (no targets, no comparisons)
  ✗ Scroll-heavy layouts
  ✗ Data without action path
                        

Publishing & Permissions

DASHBOARD PUBLISHING OPTIONS
═══════════════════════════════════════════════════════════════════

1. ROLE-BASED ASSIGNMENT
   Path: Customization → Centers & Tabs → Set Up Dashboard

   Options:
   - Set as default dashboard for role
   - Allow users to modify personal copy
   - Lock dashboard (no modifications)

2. CENTER TAB PLACEMENT
   Path: Customization → Centers & Tabs → Center Tabs

   Position dashboard as:
   - Home (first tab, default landing)
   - Additional center tab
   - Subtab within existing center

3. PUBLISHED DASHBOARDS
   Path: Customization → Publishing → Published Dashboards

   Create dashboard templates that:
   - Users can subscribe to
   - Automatically update when published
   - Maintain consistent reporting

4. PERMISSION CONSIDERATIONS
   □ User can view underlying saved searches
   □ User has access to record types displayed
   □ Subsidiary restrictions apply
   □ Role restrictions apply

TESTING CHECKLIST:
   □ Test with intended role (not Administrator)
   □ Verify data filters correctly by user
   □ Check load time with production data
   □ Confirm drill-downs work
   □ Validate mobile rendering (if needed)
                        

Implementation Checklist