Work more efficiently with the Data Quality Accelerator (DQA)

Many organizations face a growing challenge when it comes to managing data within their project and portfolio management systems. Tools like Jira, Azure DevOps, and ServiceNow are widely used to streamline workflows and processes, but the quality of data within these systems often leaves much to be desired. Incomplete, incorrect, or inconsistent data not only frustrates teams, but also directly impacts decision-making and operational effectiveness.

In addition, portfolio and product managers spend a significant amount of their time on manual checks and adjustments to solve these data problems. This time-consuming work distracts from more important strategic tasks and causes inefficiencies within organizations. Xeleron responds to this problem with a new solution: the Data Quality Accelerator (DQA)This tool automates data validation and management, with the aim of improving data quality and saving valuable time.

Challenges in data quality

Inconsistencies in data
Incomplete or incorrect data often arises due to time pressure or unclear processes. This can lead to inefficient workflows and incorrect reporting, which seriously hampers decision-making within organizations. Finding and correcting such inconsistencies is not only time-consuming, but also error-prone when done manually.

Manual work
Portfolio and product managers spend a lot of time checking and correcting data. This work takes them away from strategically important tasks and leads to lower productivity. Manual processes also make it difficult to ensure consistency, which can result in further data quality issues.

Lack of ownership among data owners
A common problem in data management is the lack of ownership by those responsible for the data. When data owners do not fully take their responsibilities, fields remain incomplete or incorrectly filled in. This not only leads to frustration within teams, but also results in portfolio and product managers having to spend more time correcting these errors. This lack of ownership directly impacts the reliability of reporting and operational effectiveness.

How does the Data Quality Accelerator work?

Automatic data checking
The DQA systematically checks for missing or inconsistent data. This includes verifying ownership and checking start and end dates. These automated checks ensure that data quality is maintained in a consistent manner.

AI-driven validation
Using AI, DQA analyses content fields based on organization-specific requirements. This can relate to acceptance criteria, project descriptions or other essential data elements. This validates the content of fields and aligns it with the needs of the organization.

Automated notifications
Work item owners receive automated notifications when improvements are needed. These notifications provide clear instructions and allow data to be updated immediately, without additional manual intervention.

Regular reports
The DQA generates monthly or bi-weekly reports with insights into data quality scores. This provides organizations with an overview of their progress and helps identify potential bottlenecks in data management.

Advice for improvements
In addition to identifying problems, the DQA also provides advice on how to further improve data quality. This includes targeted recommendations for supplementing or correcting specific fields, based on the quality criteria set by your organization. This makes the tool not only a control instrument, but also a partner in the continuous optimization of data.

Benefits of the DQA

Time saving
By automating repetitive and time-consuming tasks, portfolio and product managers can save up to 20% of their time. This time can be reallocated to strategic initiatives, contributing to a more efficient way of working. 

Better data quality
The DQA ensures consistent and correct data. This has a direct impact on the reliability of reporting and the quality of decision-making within the organization. 

Integrated approach
The tool is designed to work seamlessly with existing systems such as Jira and Azure DevOps, allowing organizations to benefit from improved data quality without major changes to their workflows.

Reduced workload
Automation takes routine tasks off the hands of employees and prevents manual errors. This reduces workload and improves collaboration between teams.

What does this mean for your organization?

With the Data Quality Accelerator you improve the management of data within your organization on multiple fronts. By automating repetitive tasks you save up to 20% of your time, which you can use for strategic activities. You also benefit from higher data quality, which leads to more reliable reporting and better decision-making. The tool works seamlessly with systems such as Jira and Azure DevOps, so you do not have to make major adjustments to your current workflows. In addition, the DQA reduces the workload by automating routine tasks and preventing manual errors. This makes the Data Quality Accelerator an efficient and effective solution for improving your data management.

Would you like to know more about this solution? Please contact Martin van Langen for more information or a demo.