Data is key (quality, consistency, completeness) and even more today if we want to leverage AI capabilities. Data Quality Navigator provides tools to support data cleansing and enrichment before a new ERP implementation project, then offers a comprehensive cockpit to track quality over time in business as usual, and identify deviations to put in place corrective actions and streamline processes.

Leveraging integrations with ISO platforms (e.g. DUNs) and some AI checks are a big plus.

Implementation is quick. Review collected by and hosted on G2.com.

Not really something to dislike but as a potential improvement point if this is relevant, probably adding an MDM capability would prove beneficial Review collected by and hosted on G2.com.

What I like the most is the smooth user interface and the ability to automate data quality check rules. Once the inspection conditions are set, the program cleans the data and identifies errors automatically without continuous manual intervention. Also, the instant reporting feature that shows the percentage of data accuracy and purity gives me complete confidence in the data before moving on to the analysis stage. Review collected by and hosted on G2.com.

Despite the efficiency of the tool, I find that it consumes a significant amount of memory and RAM resources when processing very large data files, which causes noticeable slowdowns at times. Also, I hope for improved flexibility in customizing some complex inspection rules to make them easier to set up without the need to write advanced code, in addition to providing more educational documentation for advanced features. Review collected by and hosted on G2.com.

The data-cleansing logic and the user interface for addressing any potential issues are easy to use and genuinely user-friendly. Review collected by and hosted on G2.com.

With any product, it takes some initial work at the start of the project to clearly defined the data cleansing logic and rules to make the tool succssful. However, the DQN tool comes with standard 'out of the box' rules to make this process as smooth as possible to quickly jumpstart your data cleansing journey. Review collected by and hosted on G2.com.

I like that with Data Quality Navigator nothing gets lost. I especially appreciate the traffic light rules, which allow for action if necessary. The diagrams for executives are also useful and provide a current status. Review collected by and hosted on G2.com.

The recognition of item numbers could be improved. Currently, Data Quality Navigator only recognizes item numbers in a specific format, such as 81222229999, but not if they contain dots or dashes, like 81.22222-9999. When I copy item numbers from SAP and search in Data Quality Navigator, I have to manually remove these dots and dashes for the search to be successful. Review collected by and hosted on G2.com.

Data Quality Navigator makes it easy to identify and improve data quality issues across large datasets. The user interface is intuitive, allowing business users to get started quickly without extensive training.

I particularly appreciate the flexibility to define and automate quality checks, which significantly reduces manual effort and increases confidence in our data.

The onboarding process was smooth, and the support team has been very responsive whenever we had questions or new requirements. The dashboards provide clear visibility into data quality metrics, helping us monitor progress and quickly identify areas that need attention. Review collected by and hosted on G2.com.

One aspect I found less convenient was that data uploads were primarily handled in delayed batches.

This made it more difficult to immediately identify and track data quality improvements or deteriorations, as changes only became visible after the next upload cycle. Near real-time updates or more frequent synchronization options would provide greater transparency and make it easier to monitor data quality trends and react more quickly to issues. Review collected by and hosted on G2.com.

DQN stands out for its robust approach to data governance and quality management. The rule framework, automated checks, and monitoring capabilities help ensure consistent and reliable data across complex organizations.

It is particularly effective for large-scale data collection, enabling teams to standardize inputs, track performance over time, and gain clear visibility through structured dashboards and KPIs. Review collected by and hosted on G2.com.

Some areas could be further optimized, especially in terms of user experience and flexibility. Certain features such as filtering, data extraction, and data editing could be more intuitive and streamlined to improve efficiency.

Enhancements in performance during heavy usage and additional flexibility in dashboards and permissions would further strengthen the overall user experience. Review collected by and hosted on G2.com.

DQN essentially makes migration readiness measurable. Instead of relying on status calls and gut feeling, you get a fact-based dashboard showing red, yellow and green areas per data domain, which honestly changes how steering discussions run. Review collected by and hosted on G2.com.

Not every stakeholder enjoys seeing red areas on the dashboard, so you occasionally need to invest energy explaining that early visibility is a feature, not a problem. Review collected by and hosted on G2.com.

DQN helps to improve customer data quality without having to build everything ourselves. The built-in rules and best practices helped us identify duplicate customer records, incomplete information, and inconsistencies very quickly. We started seeing useful insights early on and could focus on fixing issues rather than finding them. Review collected by and hosted on G2.com.

Data quality improvements still require business involvement and decisions, especially when dealing with duplicate or conflicting customer records. DQN provides the structure and transparency, but some decisions cannot be fully automated. Review collected by and hosted on G2.com.

I like the ability to customize the tool according to our needs, such as integrating new feeds and creating import templates to use in other systems. The implementation of IN/OUT integration is also very useful. Moreover, the Data Quality Navigator team is very responsive in initiating developments or resolving incidents, which demonstrates their seriousness since 2023. Review collected by and hosted on G2.com.

Add AI advantages to the solution in order to correct the imported data even more simply and automatically. Review collected by and hosted on G2.com.

Great user experience overall. It was very quick to learn, and the onboarding support was genuinely helpful. Review collected by and hosted on G2.com.

We have some very complex quality checks in our existing systems. Some of these checks couldn’t be transferred, but a lot of it is on the roadmap. Review collected by and hosted on G2.com.