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.

Search Components

Filter Types

Formula Patterns

Summary Types

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

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

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

Key Financial Reports

Report Customization Process

Report Scheduling

Schedule reports for automatic distribution:

SuiteAnalytics Workbook Overview

SuiteAnalytics Workbook provides a modern, Excel-like analytics experience within NetSuite with multi-dataset joins, advanced pivoting, and rich visualizations.

Dataset Components

Workbook Structure

Visualization Types

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  │
        

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!
        

NSAW Overview

NetSuite Analytics Warehouse provides a separate analytics database optimized for reporting workloads, eliminating the performance impact of complex queries on the production system.

NSAW Architecture

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

  NETSUITE ERP                     NSAW (Oracle Cloud)
+-------------------+          +----------------------------+
| Transaction Data  |          |  PRE-BUILT DATA MODELS     |
|  - Sales Orders   |  Data    |  +----------+ +----------+ |
|  - Invoices       | -------> |  |Financial | |  Sales   | |
|  - Payments       | Repl.    |  |  Cube    | |  Cube    | |
|  - Inventory      |          |  +----------+ +----------+ |
|                   |          |  +----------+ +----------+ |
| Master Data       |          |  |Procure-  | |Inventory | |
|  - Customers      |          |  |ment Cube | |  Cube    | |
|  - Items          |          |  +----------+ +----------+ |
|  - Vendors        |          |                            |
+-------------------+          |  ANALYTICS INTERFACE       |
                               |  - Dashboard Builder       |
                               |  - Reports Builder         |
                               |  - Query Direct            |
                               +----------------------------+
                                         |
                                         v
                               +----------------------------+
                               |  OUTPUT                    |
                               |  - Embedded Dashboards     |
                               |  - External BI Tools       |
                               |  - Export (Excel)          |
                               +----------------------------+
        

Pre-Built Subject Areas

Pre-Built Analytics Packs

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%  ││
│   └─────────────────┘    │   └─────────────────────────────┘│
└─────────────────────────────────────────────────────────────┘
        

When to Use NSAW

SuiteAnalytics Connect Overview

SuiteAnalytics Connect enables direct database connectivity from external BI tools like Tableau, Power BI, and custom applications using ODBC and JDBC drivers.

Connection Architecture

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

+-------------------------------------------------------------+
|                    EXTERNAL TOOLS                            |
|  +--------+  +--------+  +--------+  +--------+             |
|  | Excel  |  |Power BI|  |Tableau |  | Custom |             |
|  +---+----+  +---+----+  +---+----+  +---+----+             |
+------+----------+----------+----------+---------------------|
       |          |          |          |
       v          v          v          v
+-------------------------------------------------------------+
|              SUITEANALYTICS CONNECT                          |
|  +------------------------+  +------------------------+     |
|  |     ODBC/JDBC          |  |      SuiteQL           |     |
|  |      Drivers           |  |      REST API          |     |
|  +-----------+------------+  +-----------+------------+     |
|              |                           |                   |
|              v                           v                   |
|  +--------------------------------------------------------+ |
|  |              CONNECT SERVICE                            | |
|  |  - Authentication (Token-based)                         | |
|  |  - Query Processing                                     | |
|  |  - Role-based Data Access                               | |
|  |  - Governance Limits                                    | |
|  +--------------------------------------------------------+ |
+-------------------------------------------------------------+
                          |
                          v
+-------------------------------------------------------------+
|                    NETSUITE DATA                             |
|  +-------------+ +-------------+ +-------------+            |
|  | Transactions| |  Entities   | |    Items    |            |
|  +-------------+ +-------------+ +-------------+            |
+-------------------------------------------------------------+
        

Key Considerations

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 KPIs

Different industries require different key metrics. Configure reports and dashboards to highlight industry-relevant KPIs.

Manufacturing KPI Details

Wholesale/Distribution KPI Details

Software/SaaS KPI Details

Professional Services KPI Details

Nonprofit KPI Details

Healthcare KPI Details

Common Industry Reports

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
        

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

• NetSuite Analytics Warehouse SKU must be purchased
• Administrator role required for initial setup
• Initial data sync takes 24-48 hours
• Check sync status: Setup > Analytics > SuiteAnalytics Warehouse > Sync Status

NSAW Subject Areas

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

SQL Query Patterns

NSAW supports standard SQL via SuiteAnalytics Connect. 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 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

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

x 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';

x 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;

x 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

Custom Datasets in NSAW

Beyond pre-built subject areas, create custom datasets for specialized analytics needs.

Calculated Field Patterns

Performance Optimization

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
        

Pattern 2: Multi-Dataset Customer 360

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

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

Linked Dataset 1: Sales History
  Join: Transaction.Entity = Customer.ID
  Fields: SUM(Amount) as Total Revenue, COUNT(*) as Order Count

Linked Dataset 2: Open A/R
  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
  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

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
  Conditional formatting (heat map)
        

Chart Types and Best Practices

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           |
|    up 12% YoY     up 2.1pts     up $200K         down 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
        

Linked Datasets

Connect multiple datasets to create comprehensive analytical views.

Drill Paths

Configure drill-down navigation for interactive analysis:

Conditional Formatting

Dashboard Components

Role-Based Dashboards

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?"
        

• Less is more: 5-7 key metrics per dashboard
• Above the fold: Most important data visible without scrolling
• Consistent layout: Group related metrics together
• Clear labels: Titles that explain what users are seeing
• Actionable: Link to transactions and drill-downs

Role-Based Dashboard Templates

Executive Dashboard

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

LAYOUT:
┌────────────────────────────────────────────────────────────────┐
│ ROW 1: KPI TILES (Critical Metrics)                            │
│ [Revenue MTD] [Margin %] [Cash Position] [Pipeline Value]      │
├────────────────────────────────────────────────────────────────┤
│ ROW 2: TRENDS & COMPARISON                                    │
│ [Revenue vs Plan - Line] [Revenue by Segment - Pie]            │
├────────────────────────────────────────────────────────────────┤
│ ROW 3: EXCEPTIONS & ACTIONS                                   │
│ [Deals Requiring Attention] [Upcoming Commitments]             │
└────────────────────────────────────────────────────────────────┘
        

Sales Manager Dashboard

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

LAYOUT:
┌────────────────────────────────────────────────────────────────┐
│ ROW 1: [Quota Attainment by Rep - Bar] [Pipeline by Stage]     │
├────────────────────────────────────────────────────────────────┤
│ ROW 2: [Activities This Week] [Win Rate Trend - Line]          │
├────────────────────────────────────────────────────────────────┤
│ ROW 3: [Opportunities Closing This Month - Table]              │
├────────────────────────────────────────────────────────────────┤
│ ROW 4: [Create Lead] [Create Opp] [Pipeline Report] [Forecast]│
└────────────────────────────────────────────────────────────────┘
        

Operations Manager Dashboard

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

LAYOUT:
┌────────────────────────────────────────────────────────────────┐
│ ROW 1: [Orders to Ship] [Pending Approval] [Backorders] [Late]│
├────────────────────────────────────────────────────────────────┤
│ ROW 2: [Orders Pending Fulfillment - Sortable Table]           │
├────────────────────────────────────────────────────────────────┤
│ ROW 3: [Below Reorder Point] [Inbound Shipments Expected]      │
├────────────────────────────────────────────────────────────────┤
│ ROW 4: [Order Cycle Time - Trend] [Fill Rate - Gauge]          │
└────────────────────────────────────────────────────────────────┘
        

Accounting Team Dashboard

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

LAYOUT:
┌────────────────────────────────────────────────────────────────┐
│ ROW 1: [Unposted JEs] [A/R Unapplied] [A/P Unapplied] [Bank]│
├────────────────────────────────────────────────────────────────┤
│ ROW 2: [Month-End Close Tasks Checklist]                       │
├────────────────────────────────────────────────────────────────┤
│ ROW 3: [Cash Flow Forecast] [A/R Aging Summary - Stacked Bar] │
├────────────────────────────────────────────────────────────────┤
│ ROW 4: [Pending My Approval - Journal Entries, Vendor Bills]   │
└────────────────────────────────────────────────────────────────┘
        

Portlet Types Reference

Dashboard Layout Guidelines

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

1. VISUAL HIERARCHY
   TOP:    Most critical metrics (KPIs, alerts)
   MIDDLE: Supporting context (trends, breakdowns)
   BOTTOM: Detail data (tables, lists)

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

6. ANTI-PATTERNS TO AVOID:
   x Too many portlets (>12)
   x Duplicate information in multiple portlets
   x Charts without context (no targets, no comparisons)
   x Scroll-heavy layouts
   x 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)