A guide to Service Collection Assets
Before you start, this guide covers:
What Service Collection Assets is, why it matters, how Assets works with Jira Service Management, why trusted data matters for AI, and where to go deeper by use case.
What Service Collection Assets is and how it functions as a flexible data repository.
The fundamentals of asset management, including ITAM, HAM, SAM, and more.
Why trusted data is the foundation for AI and faster incident response.
How to get started with object schemas, templates, and the Common Data Model.
Best practices for building a scalable service-centric model.
Service Collection products referenced: Assets, Jira Service Management, Customer Service Management, Rovo
Other Atlassian products referenced: Confluence
Reading time: 8 minutes
Service Collection’s Assets helps organizations connect the people, services, and technology behind their work. By linking asset data to operational workflows, teams can work from a shared, trusted view instead of scattered spreadsheets, siloed tools, and tribal knowledge.
Teams need more than a list of devices or systems. They need to know what exists, who owns it, what depends on it, what has changed, what is affected, and what the business impact of the assets (physical or logical items) could be.
When that context is connected to requests, incidents, changes, and operational work, teams can move faster and make better decisions on asset lifecycle, traceability, and financial aspects.
What is Service Collection Assets?
Service Collection’s Assets is the asset and configuration management app that provides teams with a flexible data repository to track the lifecycle of any asset or foundational configuration item (from laptops and software to contracts and services to dogs 🐶), and link them to requests/incidents/changes, and visualize relationships and dependencies for better decision-making.
Assets is embedded and essential to both ITSM and ESM use cases. Assets can be linked to projects, tasks, or any type of work, from strategy to operational processes.
What is asset management?
Asset management is the discipline of tracking, maintaining, protecting, and maximizing the value of the assets an organization owns or uses throughout its lifecycle. Asset management is about having a single, accurate view of these items.
This tells you what they are, where they are, who uses them, how they’re configured, how much they cost, and how they’re changing over time. Now you can make better decisions and reduce risk.
In a business/IT context, “assets” usually means hardware (laptops, servers, phones), software (licenses, SaaS subscriptions), and other Non-IT resources (services, facilities, contracts).
Data management covers everything an organization (or person) does to treat data as a reliable asset — from the moment data is created or collected until it’s archived or deleted.
Main components
Collection & ingestion - Gathering data from sources like apps, sensors, transactions, or forms.
Storage - Deciding where data lives (databases, data warehouses, data lakes, cloud storage) and how it’s structured.
Organization & modeling - Defining schemas, formats, and relationships so data is consistent and findable.
Data quality - Keeping data accurate, complete, and free of duplicates or errors.
Governance - Setting rules for who owns data, who can access it, and how it’s classified. This includes compliance with regulations like GDPR or HIPAA.
Security & privacy - Protecting data from breaches, controlling access, and handling sensitive information responsibly.
Integration - Combining data from different systems so you have one source of truth
Lifecycle management - Backups, retention policies, archiving, and eventual deletion.
Why data management matters? Good data management is what lets organizations trust their data for reporting, analytics, and increasingly for training AI/ML models. Poor data management leads to bad decisions, wasted effort, compliance risk, and security exposure.
Why is asset management critical for modern teams?
Asset management is critical because modern IT, operations, and business teams depend on fast access to accurate context. Without it, teams lose time searching for ownership, validating inventory, checking dependencies, and piecing together impact from multiple systems.
That slows response, increases risk, and weakens decision-making. With accurate asset data connected to Jira Service Management workflows, teams can:
Resolve incidents and requests faster by seeing affected assets, related services, dependencies, history, and likely owners in one place
Reduce operational risk by understanding service dependencies and impact before making changes
Improve purchasing and optimize costs with better visibility into stock, usage, ownership, duplicate purchases, renewals, and allocation
Strengthen lifecycle management, audit, and compliance by tracking ownership, status, warranties, contracts, usage, and what each asset supports over time
Make AI more useful by giving AI systems access to cleaner, better-governed, connected operational data
Simple test: If your team can't quickly answer questions like “What is affected?” “Who owns it?” “What depends on it?” Or “what is the business impact?” - you likely have blind spots in asset and service configuration.
Why AI is only as good as the data you trust and manage
AI can accelerate triage, summarize incidents, recommend actions, and help teams find answers faster. But AI does not create truth on its own. It reasons from the data it can access. If that data is incomplete, stale, duplicated, inconsistent, or disconnected, the output will be less trustworthy.
That is why asset and configuration management matters even more in an AI-powered operating model.
If service ownership is unclear, dependency data is missing, or asset records are outdated, AI can't reliably answer questions such as which services are affected by an outage, who should approve a change, which systems support a business capability, or which device is assigned to an employee.
Trusted data is what turns AI from interesting assistance into an operational advantage. When the underlying data is accurate, structured, and connected to real workflows, AI can help teams act faster with more confidence.
When the data is weak, AI may amplify confusion rather than reduce it.
How Atlassian adds value with Teamwork Graph and Assets
Atlassian provides a structural advantage because Assets is not an isolated asset database. It is part of a connected platform where people, work, services, knowledge, and assets can be understood as a whole.
Teamwork Graph is Atlassian’s connective data layer across the platform. It links teams, work items, knowledge, services, and assets, so information is not trapped in separate tools. When asset and configuration data is part of that connected model, it becomes more useful everywhere work happens.
This creates important advantages:
Shared operational context across Jira Service Management, Jira, Confluence, and other Atlassian experiences
Better AI grounding because AI can reason over connected relationships between people, services, work, and assets
Less context switching because the right asset information appears in tickets, incidents, change workflows, and documentation
More actionable automation because workflows can respond to asset state, ownership, dependencies, and lifecycle conditions
More trustworthy decision-making because reporting and analysis are based on a connected operational model instead of fragmented records
With Assets and the Teamwork Graph, a responder can open an incident and quickly understand what service is affected, what infrastructure supports it, who owns the components involved, and whether related changes or requests are already in motion. It also means AI experiences across Atlassian can provide more relevant answers because they are grounded in the data teams actively trust and manage.
Bottom line: Teamwork Graph connects Assets with the people, work, services, and knowledge teams use every day, making asset context more actionable across Atlassian.
Who is asset management for?
Asset management is not limited to traditional IT inventory. It supports multiple operational use cases across the organization.
Use case | What teams manage | Why it matters |
Hardware, software, network devices, cloud resources, service desk context, lifecycle data | Provide service context, support change impact analysis, and manage lifecycle and cost across hardware, software, and cloud resources | |
Business services, applications, environments, APIs, databases, ownership, dependencies | Assess change impact, improve incident triage, strengthen service ownership, reduce coordination overhead | |
Facilities assets, inventory, contracts, vendors, locations, workplace resources, business services | Improve operational visibility, support approvals and renewals, reduce manual work, and improve reporting |
If you want a deeper walkthrough tailored to a specific audience, start with one of the linked guides above.
How Assets works
Assets uses a flexible object model, allowing teams to represent almost anything they need to track. The structure is simple enough to start quickly and flexible enough to expand over time.
Core concept | Meaning |
What is an object schema? | It is a top-level container or blueprint structure to store assets. It contains object types (categories) and objects (records). |
What is an object type? | A category that groups similar objects, such as Laptops, Business Services, Vendors, or Employees |
What is an object? | A single asset or configuration item, such as a laptop, server, application, contract, or location |
What are attributes? | The fields that describe an object, such as owner, model, warranty end date, status, cost center, or environment |
What are references? | The relationships between objects that show dependencies, assignment, ownership, or support links |
Those building blocks support a wide range of use cases, from employee hardware tracking to service dependency mapping to supplier and contract visibility.
How to get started with Assets
A practical rollout usually works best when teams start with a specific problem, build a focused model, and expand from there.
Define the problem you want to solve, such as slow incident triage, unclear ownership, weak service dependency visibility, duplicate purchasing, or poor contract tracking
Create an object schema for the assets or services involved in that problem area. Start with our out-of-the-box templates and common data model, or create your own
Define object types and attributes such as laptops, business services, vendors, environments, contracts, or employees, and define relationships.
Ingest data, or add new objects manually, or import them from other sources
Connect Assets to Jira Service Management workflows using Assets custom fields in requests, incidents, changes, and other work
Automate updates and actions so ownership, status, notifications, and follow-up work stay current
Improve data quality continuously with imports, discovery, reviews, and governance
Best practice: start with one or two high-value services or asset domains. A lean, service-centric model is usually more useful and easier to maintain than a large inventory nobody trusts. Some of these steps can be done using Rovo.
Ways to populate and maintain your data
Assets supports multiple ways to get data into your model and keep it current over time.
Templates help teams start with prebuilt schema structures for common use cases, such as IT asset management, people, and facilities
CSV and JSON imports make it easier to bring in spreadsheet and system data
Assets Discovery helps identify network assets and keep records updated on a schedule
Clean & manage with Atlassian’s Asset Data Manager for accurate, aligned data
Automation can update ownership, status, routing, renewals, and lifecycle events based on workflow activity
Services integration helps connect service records and supporting assets into a service-aware model
What is Assets Data Manager?
Assets Data Manager helps teams ingest, cleanse, normalize, and reconcile asset data from multiple sources into a more reliable source of truth. This is especially important when records come from procurement systems, spreadsheets, endpoint tools, cloud platforms, discovery sources, or vendor files that don't always agree.
Assets Data Manager helps organizations:
Bring together records from different systems
Identify duplicates, gaps, and inconsistencies
Normalize and reconcile records before they affect downstream workflows
Improve trust in reporting, service mapping, and operational decisions
Data quality is not just an admin concern. It directly affects incident response, approvals, reporting, automation, and the usefulness of AI.
What is the Common Data Model?
The Common Data Model gives teams an opinionated starting point for structuring asset and configuration data. Instead of beginning from a blank page, teams can use a more standardized approach to object types, attributes, and relationships, then tailor it to their environment over time.
This can shorten setup time, improve consistency, and make it easier to grow your model responsibly.
Best practices
Start where you are - Begin with one or two high-value services or workflows rather than trying to model everything at once. Focus on a pressing challenge, then expand as the model proves its value.
Connect across teams - The greatest benefits emerge when asset and configuration data flow seamlessly between teams. Break down silos between IT operations, development, security, and business units to create a unified view of your technology landscape.
Focus on data quality - The value of your asset and service configuration management practice depends entirely on the reliability of your data. Invest in automation, governance, and regular validation to ensure your information remains accurate and trustworthy.
Balance structure with flexibility - Your asset management approach should be structured enough to ensure consistency but flexible enough to adapt to changing business needs.
Measure what matters - Define clear success metrics tied to business outcomes. Whether it's reduced incident resolution times, improved compliance scores, or optimized resource utilization, demonstrating tangible value will ensure ongoing support for your initiatives.
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Frequently asked questions
What types of assets can I manage in Assets?
Assets can manage everything from hardware, software, licenses, cloud resources, applications, business services, vendors, contracts, inventory, facilities equipment, locations, people, and many other object types. The model is flexible enough to support use cases for IT, development, and business teams.
Should we start with IT asset management or service configuration management?
Most teams benefit from starting with the problem they need to solve. If the challenge is inventory, lifecycle tracking, or assignment, start with IT asset management. If the challenge is incident impact, service dependency visibility, or change risk, start with service configuration management. In practice, the two usually become more valuable together.
What is a CMDB?
A CMDB (Configuration Management Database) stores information about an organization’s IT environment and the relationships between its configuration items. See the IT asset management section for more details.
What is Asset management?
Asset management tracks and manages an organization’s assets throughout their lifecycle to improve efficiency, compliance, and value while reducing financial and security risks. See the asset management section for more details.
How long does it take to get value?
Teams can often start seeing value within days to weeks when they use our Common Data Model templates and connect the data to active workflows.
Can Assets support AI use cases?
Yes. Assets helps provide the trusted operational data that AI depends on. The more accurate, connected, and well-governed your asset and configuration data is, the more useful AI can be for triage, search, recommendations, and operational assistance.