Dataset in Zluri

A Dataset in Zluri is a structured collection of data pulled from various modules across your organization’s Zluri account. Datasets serve as the foundation for both standard and custom widgets used in dashboards.

Think of a dataset as a logical grouping of information, typically generated via system queries or filters. It defines what data is available, while widgets determine how that data is visualized.

Relationship Between Datasets and Widgets

A widget is a visual layer (chart, table, or KPI) built on top of a dataset.
Multiple widgets can reuse the same dataset, each configured differently.
This promotes consistency and reduces redundancy across dashboards.

Using a Dataset to Create a Widget

              ┌──────────────────────────────┐
              │         Dataset A            │
              │ (Application - Flat/Expanded)│
              └────────────┬─────────────────┘
                           │
     ┌─────────────────────┼──────────────────────┐
     │                     │                      │
┌───────────────┐  ┌────────────────────┐  ┌────────────────────┐
│ Widget 1:     │  │ Widget 2:          │  │ Widget 3:          │
│ Recently Used │  │ Shadow AI Apps     │  │ Recently Used AI   │
│ AI Apps       │  │                    │  │ App                │
└───────────────┘  └────────────────────┘  └────────────────────┘

              ┌────────────────────────────┐
              │         Dataset B          │
              │ (Access Request History)   │
              └────────────┬───────────────┘
                           │
           ┌───────────────┴──────────────┐
           │                              │
┌─────────────────────────────┐  ┌─────────────────────────────┐
│ Widget 4:                   │  │ Widget 5:                   │
│ Big Number of              │  │ Pie Chart of Access Requests│
│ Total Access Requests      │  │ by Department               │
└─────────────────────────────┘  └─────────────────────────────┘

Example Use Case

You have a dataset (e.g., Application) that tracks monthly users across all AI apps. From this dataset, you can build:

A line chart for weekly usage trends
A KPI widget showing total active users
A table listing users by department or application

Dataset Structures: Flat vs. Expanded

When building widgets, you can choose between two dataset types:

1. Flat Dataset

Flat datasets contain one-to-one mapped fields. Each row represents a unique entity with no nesting.

Characteristics:

One row per record
No repeating or nested fields
Simple structure for visualization and aggregation

Best For:

KPI widgets (e.g., total users, avg. apps/user)
Bar charts grouped by role or department
Simple tables with summary data

2. Expanded Dataset

Expanded datasets include normalized or relational fields. One entity (like a user) may span multiple rows due to related data.

Characteristics:

One-to-many relationships across rows
Supports granular filtering and grouping
Requires DISTINCT or GROUP BY to avoid duplication

Example Table:

user_id	user_name	application	access_level
u001	Alice	Slack	Admin
u001	Alice	Notion	Member
u002	Bob	Zoom	Admin

Best For:

Access reviews (users with multiple apps or roles)
Multi-dimensional filters (user + app + access)
Heatmaps or breakdown charts

Dataset Selection Guide

Use Case	Recommended Dataset
Summary KPIs	Flat
Advanced filters (e.g., app + role)	Expanded
Simple counts per entity	Flat
Hierarchy or relationship analysis	Expanded

Prebuilt Datasets in Zluri

Zluri offers a rich catalog of preconfigured datasets across both IGA and SMP modules.

IGA Datasets

Name	Category	Description
Application User	Applications & Users	App users, their activity, and access levels
Access Request	Access Management	Request approvals, workflows, and provisioning records
Access Certification - Application	Access Management	High-level certification campaign records
Access Certification User-App	Access Management	Line-item records per user-app in certifications
Workflow Executions	Workflow & Lifecycle	Logs of workflow executions triggered via playbooks
Org User Onboarding Offboarding	Workflow & Lifecycle	Data on joiners, leavers, and transitions

SMP Datasets

Name	Category	Description
Applications	Applications & Users	All apps used across the org
Users	Applications & Users	All users (active and inactive)
Application User	Applications & Users	App-user relationships with access levels
Applications Cumulative Count	Applications & Users	Timeline of app discovery and onboarding
User Application	Applications & Users	All apps accessed by each user
User Application License	Applications & Users	Assigned licenses per user
Application Spends	Spends & Costs	Total app spend across billing cycles
Application Costs	Spends & Costs	Cost breakdown by license, feature, etc.
Application Spends Users	Spends & Costs	Spend per user by app usage
Department Spends	Spends & Costs	Spend grouped by department
Cost Center Spends	Spends & Costs	Spend by cost center
User Spends	Spends & Costs	Total SaaS spend per user
Application Optimization	Spends & Costs	Recommendations to reduce spend
Continuous Optimization	Spends & Costs	Live tracking of optimization opportunities
Contracts	License & Contract	App contract repository
Application Contract	License & Contract	Contracts tied to specific apps
Licenses	License & Contract	Inventory of purchased vs. consumed licenses
Similar Applications by Feature	Recommendations	Alternative apps with overlapping functionality

How to Use a Dataset to Create a Widget

To build a widget using any dataset:

Go to Analytics > Widgets
Click Create New Widget
Select a dataset from the dropdown
Note: Multi-dataset selection is not supported.
Configure filters, chart type, and other fields
Save the widget and add it to a dashboard

Best Practices

Use Flat datasets for fast rollups and KPI-level summaries
Use Expanded datasets for deep-dive visualizations and filtering
Always apply DISTINCT or GROUP BY in expanded datasets to avoid overcounts

Updated 2 months ago