Thanks for joining us! Asset & Configuration Management Webinar Recording + Q&A

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Hi Asset and Config Management Champions!

Thanks to everyone who joined our "Tame IT Chaos: Mastering Asset & Configuration Management" webinar! Your engagement and thoughtful questions made the session truly dynamic!

Missed it? No worries! You can find the recording here.

We received tons of great questions that we couldn't address during the session. As promised, our experts have provided the answers below.

Special thanks to Jennifer Fish from Atlassian and Jeff Dean from Kyndryl for sharing his expertise and real-world experiences in building a "golden record" and managing complex IT environments!

Let's keep the conversation going - share your asset and configuration management challenges and wins in the comments below. We're here to support your journey in taming IT chaos!

Want more? Check out our Jira Service Management Assets product guide and resources page for additional information and best practices.

Stay organized!

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If I want to use the Services of CMDB - what's the best practice for prioritization (Services get Tiers?) What is the leading object for prioritisation (application, business service, capability?)

Let me break down the best practices for service prioritization in Jira Service Management Assets

1. Business Services should be your leading object for prioritization because:

• They directly tie to business outcomes

• Are more stable than individual applications

• Provide clear visibility into business impact

2. Recommended Tier Structure:

• Tier 1: Mission-critical business services (Revenue-generating, customer-facing)

• Tier 2: Important internal business services (Core operations)

• Tier 3: Supporting business services (Administrative, non-core)

• Tier 4: Nice-to-have services (Convenience, optional)

The relationship flow should be:
Business Service → Supporting Applications → Infrastructure Components

Think of it as a pyramid where business services sit at the top, driving the prioritization of everything below them. This ensures your asset management aligns directly with business value and impact.

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When is Data Manager (airtrack.io) coming out of beta?

While we don't have an exact date for the Assets Data Manager General Availability release, Atlassian is actively gathering feedback during the Open Beta phase to ensure a smooth transition to the full release. We encourage customers to participate in the Open Beta and provide feedback to help shape the final product.

Please stay tuned for more specific information about the General Availablity release timeline.

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Small organisations (150 users) don't want to invest in automation and insist on building a cmdb based on manual input. Is that viable? Can a manual approach lead to reliable cmdb?

For a small organization of 150 users, a manual CMDB approach is possible but comes with significant risks and limitations:

Viable scenarios:

1. If your environment is relatively static (low change rate)

2. If you have a small, well-defined set of critical assets

3. If you have staff responsible for data maintenance

4. If your compliance requirements are minimal

However, there are critical considerations:

1. Resource Impact

• Manual updates can take 5-10 minutes per asset

• Even with just 300 configuration items, that's 25-50 hours for a complete refresh

• You'll need staff time for ongoing maintenance

2. Data Quality Issues

• Human error in data entry

• Inconsistent naming conventions

• Delayed updates

• Missing relationships between CIs

• Incomplete or outdated information

3. Better Alternative Approach

Instead of fully manual, consider a hybrid approach:

• Start with a simple spreadsheet import for initial population

• Use basic automation tools already available (like network discovery tools)

• Focus on critical assets first

• Implement simple workflows for change management

The reality is that while a manual CMDB is technically possible for 150 users, it's rarely reliable or sustainable long-term. Even small organizations would benefit from basic automation to ensure data accuracy and reduce maintenance overhead.

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What are some of the trickier asset and config management challenges from a technical perspective?

While AI in asset and configuration management is still emerging, here are some promising applications:

1. Asset Identity and Uniqueness

• Determining reliable unique identifiers when serial numbers aren't unique

• Maintaining consistent asset identification across multiple systems

• Managing asset mergers and splits

• Handling duplicate records and reconciliation

2. Lifecycle Status Synchronization

• Keeping asset and configuration status aligned throughout the lifecycle

• Managing state transitions across different systems

• Maintaining historical status information

• Reconciling conflicting status updates from different sources

3. Asset Classification Accuracy

• Ensuring consistent categorization of assets (server vs. storage device)

• Handling reclassification

4. Dependency Mapping

• Accurately tracking complex relationships between services, applications, and infrastructure

• Keeping dependency maps current in dynamic environments

• Visualizing dependencies meaningfully for different stakeholders

5. Cloud Resource Management

• Tracking ephemeral resources in containerized environments

• Managing hybrid cloud configurations across multiple providers

6. Data Normalization

• Standardizing asset data from multiple discovery tools

• Reconciling conflicting information from different sources

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How can AI be used to address some of these pitfalls?

AI may be able to tackle some asset and configuration management challenges in the following areas:

1. Data Quality

• Automated data normalization across multiple sources

• Anomaly detection for incorrect asset data

• Pattern recognition for identifying duplicate records

• Smart prioritization of what needs updating

2. Dependency Mapping

• Automated discovery of complex service relationships

• Smart validation of asset relationships and dependencies

• Impact analysis prediction for changes

3. Configuration Drift

• Real-time drift detection using pattern analysis

• Automated remediation recommendations

4. Asset Classification

• Automated categorization of new assets

• Identification of miscategorized assets

5. Lifecycle Management

• Predictive end-of-life forecasting

• Optimal replacement timing recommendations

• Usage pattern analysis for optimization

Keep in mind these applications are in various stages of development, and their effectiveness will depend heavily on data quality and specific use cases. Organizations should carefully evaluate and pilot AI solutions before full implementation.

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Do any common KPIs come to mind?

Here are key KPIs for asset and configuration management:

Data Quality KPIs:

• Asset record accuracy rate

• Data currency rate

• Configuration item completeness

• Data duplication rate

Operational KPIs:

• Mean time to CMDB record update

• Asset discovery success rate

Financial KPIs:

• Software license utilization rate

• Hardware utilization rate

Service Impact KPIs:

• Incidents due to inaccurate CMDB data

• Configuration-related outages

• Service availability impacted by asset issues

Compliance KPIs:

• Audit findings related to asset records

• Compliance violation rate

Each metric should have defined targets and regular review cycles.

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How do you measure the success of an asset management strategy?

Success in asset management is measured across five key dimensions:

Financial Impact

• Cost reduction through optimization

• Reduced waste from unused licenses

• Clear ROI from asset investments

Operational Efficiency

• Reduced incident resolution time

• Faster asset deployment cycles

Data Quality

• Asset record accuracy

• Data currency rates

• Configuration completeness

• Low duplication rates

Risk Management

• Reduced security incidents

• Improved audit outcomes

• Better compliance scores

Business Alignment

• Service availability improvements

• Faster project delivery

• Better capacity planning

Each metric should align with specific business objectives and have clear baseline and target values.

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What's your strategy for handling shadow IT assets that pop up across departments without going through official procurement?

Here's a structured approach to managing shadow IT:

1. Engagement Process

• Work with departments to understand their needs

• Create easy procurement paths for validated tools

• Educate on risks and compliance requirements

2. Discovery Strategy

• Implement automated network discovery

• Enable endpoint detection and regular security scans

3. Policy Framework

• Create clear procurement policies

• Define "amnesty periods" for declaring shadow IT

4. Integration Path

• Assess security and compliance risks

• Integrate valuable tools into official systems

The goal isn't just control, but understanding why shadow IT emerges and addressing those root causes.

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How does asset management fit with Jira?

Asset management seamlessly integrates into Jira, enhancing its capabilities beyond project tracking and issue management. It allows organizations to efficiently manage their assets, which can be physical items like laptops, virtual items like software licenses, or even people like employees, across their existing Atlassian ecosystem.

Here are some key aspects of how asset management fits into Jira:

1. Configuration Management Database (CMDB): Assets in Jira Service Management provide CMDB capabilities, allowing you to store information about configuration items (CIs), create relationships and dependencies between them, and maintain a history of changes. This helps in understanding the asset ecosystem within your organization.

2. Assets in Jira Service Management integration with Jira Issues: You can link asset details from Jira Service Management directly to Jira issues, providing crucial context and speeding up decision-making and resolution times. This is done by setting up custom fields for Assets objects in your Jira projects, making asset information visible and accessible within issue views.

3. Automation and Reporting: Assets can be automated to perform tasks like creating Jira issues or modifying object attributes. Additionally, you can generate reports to analyze asset data, which aids in decision-making and monitoring the health of your asset data.

4. Use Cases: Assets can be used for various purposes, including IT service management (ITSM), customer relationship management (CRM), and human resources (HR). For ITSM, it helps manage IT assets and service requests. In CRM, it captures and stores information about leads and customers. For HR, it streamlines service delivery and interaction between HR and employees.

5. Data Import and Integration: Assets provides a framework to import data from various sources like CSV, JSON files, LDAP servers, and other databases, allowing for structured data management.

Overall, asset management in Jira enhances the ability to track, manage, and report on assets, providing a comprehensive view of an organization's resources and their interdependencies.

Additionally, feel free to check out our demo as well for more details on this! You can skip right to the asset management section in the on-demand version Jira Service Management Demo.

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Does the Jira Asset Management system tie into other systems, such as Entra, Intune, and Jamf, to keep track of device information?

Yes, Jira Service Management can integrate with various systems to manage and track device information effectively. Jira Service Management Assets capability supports various integration methods to keep track of device information:

1. API-based integrations: Assets provides REST APIs that allow developers to create custom integrations with external systems

2. Marketplace apps: There are several apps available on the Atlassian Marketplace that facilitate integrations with various asset management systems

3. Import functionality: Jira Service Management offers import features that allow bringing in data from external sources, which can be automated to keep asset information current

4. Custom development: Organizations can develop custom integrations using the available APIs to connect Jira Service Management Assets with their specific device management systems.

By leveraging these integration capabilities, Jira Service Management Assets can serve as a central hub for device information, pulling data from various sources like Entra ID, Intune, and Jamf to provide a comprehensive view of an organization's IT assets.

Additionally, Assets Data Manager in Jira Service Management (which is in open beta) is designed to handle data collection, cleansing, and merging from various sources. It supports data mapping, normalization, and reconciliation, which ensures that the data from different systems like Intune and Jamf is consistent and accurate.

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Can you expand on the discovery tool? Is there an API connection, or do we have to install something?

The Jira Service Management Assets Discovery tool is a network scanning tool used to detect hardware and software connected to a local network. It extracts detailed information about each asset, which can then be imported into Jira Service Management to manage devices and configuration items within the network.

Key Features:

1. Agent-less and Agent-based Scanning:

   • Agent-less Scanner

   • Agent-based Scanner (Windows only)

2. Assets Discovery Collector:

   • Allows running multiple instances of Assets Discovery in parallel.

   • Can scan a network remotely and transfer the resulting data to a different location.

3. Data Transfer:

   • Discovery Agents can transfer local scan results to Assets either directly if the IP addresses and ports are accessible or by exporting files to a shared location for later collection.

4. Installation and Configuration:

   • Available for download from the Atlassian Marketplace.

   • Requires installation on a local machine in your network and configuring settings such as patterns, scan settings, and credentials before running the tool.

5. Integration with Jira Service Management:

   • The data collected can be imported into Jira Service Management to enhance asset management capabilities.

   • Supports integration with various data types and external systems.

The tool is available for both Windows and Linux systems and is designed to help organizations maintain control over their infrastructure by providing a comprehensive view of their assets and configuration items. 

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Where would you start when you are just about to migrate to Jira SM? Approach a partner with assistance with setting everything up or everything can be achieved using online training?

When preparing to migrate to Jira Service Management, you have two main options: approaching a partner for assistance or utilizing online training resources. The best approach depends on your organization's specific needs, resources, and complexity of the migration. Here's a breakdown of both options:

Engaging a Partner

Partnering with an Atlassian Solutions Partner can be beneficial for several reasons:

1. Expertise: Partners have Jira Service Management-accredited and ITIL-certified consultants with years of experience in multiple implementations.

2. Complex Migrations: For large-scale or complex migrations, especially from on-premise to cloud solutions, partner assistance can be invaluable.

3. Customized Solutions: Partners can tailor the migration and setup process to your organization's unique requirements.

4. Risk Mitigation: Experienced partners can help avoid common pitfalls and ensure a smooth transition.

Utilizing Online Training

For smaller organizations or those with in-house expertise, online training can be a cost-effective option:

1. Self-Paced Learning: Atlassian offers comprehensive guides and tutorials for getting started with Jira Service Management.

2. Flexibility: Online training allows team members to learn at their own pace and focus on specific areas of interest.

3. Cost-Effective: Many resources are available for free or at a lower cost compared to hiring a partner.

4. Continuous Learning: Online resources can be referenced throughout the migration process and beyond.

Recommended Approach - For most organizations, a hybrid approach is often the most effective:

1. Start with Online Training: Begin by utilizing Atlassian's getting-started guides and tutorials to familiarize your team with Jira Service Management basics.

2. Assess Complexity: Evaluate the scope and complexity of your migration. Consider factors like data volume, customizations, and integrations.

3. Identify Gaps: Determine areas where your team lacks expertise or where additional support might be needed.

4. Consult a Partner: Even if you choose to handle most of the migration internally, consider a consultation with a partner to validate your approach and address any complex issues.

5. Prepare Your Team: Regardless of the approach, ensure your team is well-prepared by taking advantage of available training resources.

Remember, the goal is to achieve a successful migration while setting up Jira Service Management to meet your organization's specific needs. By combining online resources with targeted partner assistance when necessary, you can optimize both the migration process and your long-term use of Jira Service Management.

You can find a solution partner here Atlassian Partners.

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We have Data Manager in JSM Assets. We also have appsMicrosoft Intune for Assets from companies like Pio. So which one do we start to use now. Apps from Pio fetch every data that we need; Assets Data Manager needs a huge update in order to pull in all the data that is required. So what is the best way to manage the assets now?

Each environment has its own variety of tools and sources for data, so start with aggregating the data from available sources into a single, focused platform for the enrichment of the data and reconciliation of that data. No single source is perfect, so the cross-pollenation of data across sources, mapping them to a common taxonomy, and reconciling them to the best available values at the attribute level is a progressive and valuable approach. Starting small, focusing on the key attributes that would make the most difference and deliver value is a good place to start.

Using your current tools while progressively adopting Jira Service Management Assets Data Manager's capabilities will ensure a smooth transition without compromising data integrity or operational efficiency. You can learn more about Assets Data Manager and the integrations we have here Jira Service Management Assets Data Manager.

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If we use Jira for Agile Sprint management to apply configuration changes to an external system, can we use Atlassian asset management to track those released sprints being applied to external systems?

Yes, you can use Jira Service Management Assets to track released sprints being applied to external systems when using Jira for Agile Sprint management. Here's how you can approach this:

1. Use Jira for Agile Sprint Management: Continue using Jira to manage your sprints and configuration changes for the external system.

2. Leverage Jira Service Management Assets: Jira Service Management Assets can be used to track the external systems as configuration items CIs.

3. Create Asset Schema: In Jira Service Management Assets, create a schema for your external systems, including fields to track the applied sprint versions.

4. Link Sprints to Assets: After each sprint release, update the corresponding asset (external system) in Jira Service Management Assets with the new sprint version information.

5. Use Custom Fields: Add custom fields to your asset schema to store specific details about the applied sprints, such as sprint number, release date, and key changes.

6. Automation: Consider setting up automation rules in Jira to update the CI information in Jira Service Management Assets automatically when a sprint is completed and released.

7. Reporting: Utilize Jira Service Management Assets' reporting capabilities to generate reports on which external systems have specific sprint versions applied.

By integrating your Agile Sprint management in Jira with Jira Service Management Assets, you can maintain a clear record of which sprint versions are applied to each external system, enabling better tracking and management of your configuration changes across your infrastructure.

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Enhancing the AQL documentation with a variety of examples and instructional videos would improve its usability and effectiveness.

Thank you for your valuable feedback. We appreciate your feedback as it helps us improve our products and documentation. Our goal is to make AQL more accessible and easier to learn for all users.

Atlassian Community - can you share your AQL queries, particularly those that:

• Solve complex asset management challenges

• Demonstrate clever use of AQL functions

• Provide practical examples across different object types

Whether you're managing IT infrastructure, tracking device inventories, or creating sophisticated asset relationships, your real-world examples can help the entire Atlassian community level up their AQL skills.

Thank you for helping us enhance the Jira Service Management Assets experience for everyone.

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Will Assets Cloud have built in automation capabilities similar to what was in Jira Data Center? If so, when?

Assets Cloud seamlessly integrates with existing automation capabilities rather than having a dedicated automation engine like in Jira Data Center. In the Cloud version, the automation functionality allows for various types of integrations via third-party apps. Additionally, we have ongoing developments to enhance automation capabilities in Assets Cloud.

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Do you have demonstrations of different types of applications and configurations of this Asset Management system for different common use cases?

Yes, there are several demonstrations and resources available showcasing different applications and configurations of Jira Service Management Assets for common use cases:

1. Atlassian offers a comprehensive video tutorial on setting up an asset repository using Jira Service Management Assets, covering topics like creating schemas, asset management reporting, and practical examples.

2. There are pre-configured templates available in Jira Service Management Assets for common use cases, including IT asset management, people management, and facilities management. Check out this link for more info.

3. A deep dive demo of Jira Service Management showcases how assets can be leveraged in service requests, allowing employees to easily report issues with specific assets.

4. Isos Technology has presented five innovative asset management use cases, including:

• People: Leveraging Active Directory employee data

• Clients: Providing unique service portal experiences

• Products: Creating feature and app matrices

• Departments: Enabling company-wide audits and reports

• Databases: Turning any data into assets