Dataset in Zluri

A dataset in Zluri is a structured collection of data pulled from various modules across your organization's Zluri account. Datasets are the basis for both standard and custom widgets used in dashboards.

A dataset defines what data is available; a widget defines how it's shown.


Relationship Between Datasets and Widgets

  • A widget is a visual layer (such as a Bar Chart, Table, or Number) built on top of a dataset.

Using a Dataset to Create a Widget

Several widgets can use the same dataset. For example:

DatasetWidgets built on it
Applications-ExpandedRecently Used AI Applications (Table), Recently Discovered Shadow AI Applications (Table), Shadow AI Applications (Number)
Access Request-FlatTotal Access Requests Raised (Number), Access Requests by Status (Pie Chart)

Example Use Case

Suppose a dataset tracks monthly users of your AI apps. From it, you can build:

  • A Line Chart for weekly usage trends
  • A Number widget showing total active users
  • A Table listing users by department or application

Dataset Structures: Flat vs. Expanded

Every dataset name ends in -Flat or -Expanded, which tells you how its rows are structured.

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:

  • Number widgets (for example, total users or average apps per user)
  • Bar Chart widgets 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) can span multiple rows because of related data.

Characteristics:

  • One-to-many relationships across rows
  • Supports granular filtering and grouping
  • Requires grouping or a distinct count (the COUNT_DISTINCT aggregate under METRICS) to avoid counting a record more than once

Example Table (columns from App-to-User Mapping-Expanded):

User NameApp NameUser Application Role
AliceSlackAdmin
AliceNotionMember
BobZoomAdmin

Best For:

  • Access reviews (users with multiple apps or roles)
  • Multi-dimensional filters (user + app + access)
  • Breakdown charts (for example, a stacked Bar Chart)

Dataset Selection Guide

Use CaseRecommended Dataset
Summary KPIsFlat
Advanced filters (for example, app + role)Expanded
Simple counts per entityFlat
Hierarchy or relationship analysisExpanded

Prebuilt Datasets in Zluri

Zluri offers the following prebuilt datasets.

DatasetDescription
App-to-User Mapping-FlatUser application activity and access
App-to-User Mapping-ExpandedMapping users to applications showing activity and access levels for insight into behavior and permissions
Application Cost & Spend Summary-FlatMonthly overview of app cost and spend
Access Certification Records-FlatReviewed records in access certifications
Access Certifications-FlatUser access certification details
Access Request-FlatAccess requests approvals and workflows
App Cost Breakdown-FlatDetailed breakdown of application costs
App Spend Optimization-FlatSuggestions to optimize app usage
App Spend Summary-FlatTotal Application spend data by billing
App User Accounts-FlatMapped users to app with account and access info
Applications Cumulative Count-FlatCumulative applications onboarded over time
Applications-ExpandedDetailed overview of all active software applications used across departments, including their purpose and users
Applications-FlatApplications in use across organization
Continuous Optimization-FlatContinuous cost-saving recommendations tracking
Contract Repository-ExpandedDataset to fetch contract level details like date, type, and detailed information about the apps in that contract including owners, cost
Contract Repository-FlatDataset to fetch contract level details like date, type, and the apps in it
Employee/User directory-FlatActive and inactive organizational users
Licenses-FlatOverview of purchased consumed licenses
Org User Onboarding Offboarding-FlatOnboarding/offboarding workflows and automations
Spend by Cost Center-FlatApp spend grouped by cost center
Spend by Department-FlatAggregated app spend by department
User License Allocation-FlatUser licenses assigned across applications
User-Level Spend-FlatTotal SaaS spend per user
Vendor Cost and Spend Summary-FlatContracted cost and actual spend for each of your vendors, month by month. Every row represents one vendor in one month.
Workflow executions-FlatAutomations and workflows via playbooks and actions
Application Identity Access-FlatMaps each identity to the applications it can access and flags any privileged access.
Identity Account Relationship-FlatRelation between Identities and different states of Accounts
Identity Details-FlatCovers every identity in the org, with its type, status, lifecycle dates and secret details.
NHI Privilege and Ownership Details-FlatLists every non-human identity with its status, type, group, privilege flag and orphan flag.
Owned NHIs-FlatLinks each NHI to its owning user or group, with the owner's status, archival state and offboarding date.
Policy and Exemption Insights-ExpandedAnalyzes policy enforcement and exception usage
Policy Violation Insights-ExpandedCaptures policy violations, remediation activity, severity, and resolution timelines.

How to Use a Dataset to Create a Widget

To build a widget from any dataset:

  1. Go to Analytics → Widgets.

  2. Click Create New Widget.

  3. Under Choose a dataset, select a dataset. Each dataset shows a one-line description. You can select only one dataset per widget.

    The Choose a dataset list, with each dataset's description:

    Choose a dataset list in Add Widget
  4. Under Choose chart type, select the chart, then click Create new widget.

  5. Configure the widget. See Widgets.

  6. Save the widget and add it to a dashboard. See Create a dashboard.

Best Practices

  • Use Flat datasets for fast rollups and KPI-level summaries
  • Use Expanded datasets for deep-dive visualizations and filtering
  • In expanded datasets, always use grouping or the COUNT_DISTINCT aggregate to avoid overcounts. In the widget editor, click METRICS and, in the My metric panel, choose COUNT_DISTINCT under AGGREGATE. For example, to count unique users, use COUNT_DISTINCT on a user column such as User ID.

Did this page help you?