1. Automated Data Integration: Efficient and Seamless

The Assets Data Manager currently supports over 30 connectors for automated data integration, with more on the way. This seamless connection to databases, CSV files, and other data sources allows organizations to ensure that their data is always up to date. The ability to set up scheduled imports minimizes manual steps and reduces sources of error. 

Previously, importing data required the use of third-party applications or integration solutions at additional cost. Now, connectors eliminate this challenge.

The following connectors are currently available:

2. Data cleansing and normalization: always up-to-date

One of the core elements of Data Manager is the ability to merge, cleanse, and normalize data from multiple sources. Duplicates are removed and inconsistencies are reconciled to create a single, trustworthy “golden data set” that businesses can rely on. 

This process makes it easier for you and your organization to make confident decisions and ensure that information is accurate and up-to-date.

3. Comparative data analysis: improve data quality and security

Atlassian Data Manager’s cross-comparison data analysis allows you to merge and compare data from multiple sources to identify inconsistencies such as duplicate or outdated entries. This enables organizations to identify vulnerabilities in their IT assets early. This feature not only helps close security gaps, but also improves data quality.

In addition, Data Manager offers a new reporting feature that allows you to create dashboards based on real-time data. This enables organizations to quickly visualize current information and make informed decisions.

4. Jira Assets integration: all asset data at a glance

The Data Manager integrates seamlessly with Jira Service Management Assets to link asset data with configuration data. 

This gives teams a comprehensive view of their IT landscape and makes it easier to manage assets across departments, from IT to compliance to finance.

5. Improve auditing and compliance: easily meet standards

With complete visibility into all assets and their changes, Assets Data Manager makes it easier to comply with regulations such as GDPR. It not only supports your organization in meeting compliance standards, but also ensures a smooth audit process. 

This function helps improve risk management and prevent costly compliance violations.

Data Manager as part of Jira Service Management

Specifically, the Data Manager is an extension for Atlassian Assets, which is a native asset and configuration management tool that is part of JSM in the Premium and Enterprise licensing. 

So if you are using Jira Service Management in the cloud and on either of these licensing plans, you can use the Data Manager at no additional licensing cost! The tool is integrated as an opt-in feature in Assets and can be activated there via the general configuration screen.

Why we think Data Manager is essential

Atlassian’s Data Manager enables organizations to streamline IT processes by providing complete and up-to-date data. By automating data import and cleansing, the administrative burden is significantly reduced and businesses can focus on improving service quality. 

As Atlassian Platinum Solution Partner, we have been able to test the Data Manager extensively as part of an early access program.

Our conclusion:

The Data Manager can merge and cleanse data while providing powerful analytics. This combination of capabilities will quickly make it an indispensable tool for data-driven business decisions.

Problem: Resource consumption due to repetitive classification work

As soon as the relevant emails are received by the responsible persons, they are usually processed and classified manually before they can be handled in ticket systems such as Jira from Atlassian. This process ties up valuable personnel resources that could be used more efficiently, for example for strategic work.

An understanding of the context is required to categorize the issues and fill the fields in the ticket system appropriately. Until now, there were no technical options for automating these workflows. Thanks to the further development of artificial intelligence (AI), these use cases can now be looked at in a new light.

Solution: Automation with Jira Automation in conjunction with LLMs

Large Language Models (LLMs) are a special type of AI that are trained to understand, process and generate human language. The ability to recognize patterns and relationships in a context that makes these models so exciting for the scenario described: With the integration of LLMs in Jira Automation, recurring tasks can not only be automated, but also made more intelligent.

One exciting use case is the processing of incoming security updates. Thanks to the power of LLMs, the context of these updates is automatically recognized and relevant custom fields in Jira are filled accordingly.

Imagine the IT security team regularly receives security-critical information about the systems used in the company. Until now, the workflow for processing these messages in Jira tickets has been as follows: The messages arrive in Jira via interfaces / the connection of an email inbox, the messages are then classified by a 1st-level team and the information is finally transferred manually to the corresponding fields within Jira.

By using an LLM in conjunction with Jira Automation, it is possible to automatically recognize which systems are affected and check whether they are available in the company. In addition, prioritizations can be made and important information such as the affected group can be derived from the context. Automated assignment to the relevant teams is also possible.

This solution significantly reduces manual effort and ensures that no relevant information is overlooked.