What are the most common data validation issues in IBM Cognos Analytics?

1 Data source connection errors

Data source connection errors are a common issue when working with IBM Cognos Analytics. These errors happen when the tool cannot access or communicate with the data sources configured in your project, and can be caused by incorrect credentials, network issues, or firewall settings. To avoid or fix these errors, you should verify that the data source credentials are valid and up to date, test the data source connection before using it in your project, check the network connectivity and firewall settings between the tool and the data source, and monitor the data source status to update the tool if any changes occur.

2 Data model inconsistencies

Data model inconsistencies in IBM Cognos Analytics can lead to incorrect or missing data, calculation errors, or performance issues. To avoid or fix these issues, it is important to review the data source schema and metadata before creating or importing the data model. Additionally, you should validate the data model against the data source using the test query feature, refresh the data model if there are any changes in the data source, and use consistent naming conventions and data types for the data model elements.

3 Report design errors

Report design errors are a common issue in IBM Cognos Analytics, which can affect the usability, readability, or functionality of the report. To avoid or fix these errors, it is important to understand the business needs and objectives of the report. Furthermore, one should follow best practices and guidelines for report design in the tool, preview and test the report before publishing or sharing it, and solicit feedback and suggestions from end users and stakeholders.

4 Dashboard layout issues

Dashboard layout issues are a common data validation issue in IBM Cognos Analytics, which can compromise the user experience, interactivity, or responsiveness of the dashboard. To avoid or fix these issues, you should use the responsive design feature to adjust the dashboard layout for different screen sizes and orientations. In addition, optimizing the dashboard performance by reducing the number of widgets, filters, or calculations can help. Before publishing or sharing the dashboard, you should also preview and test it on different devices and browsers. Finally, applying the latest patches and updates for the tool and browser is essential.

5 Data quality problems

Data quality problems can arise in IBM Cognos Analytics when the data used or generated is incomplete, inaccurate, inconsistent, or outdated. This can affect the validity, reliability, or relevance of the analysis and insights that you derive from the data. To avoid or fix data quality issues, you should verify the source and origin of the data before using it in your project. Additionally, you should cleanse and transform the data with the data preparation and quality features in the tool. Further, compare and reconcile the data with other sources or systems with the data lineage and audit features in the tool. Finally, keep the data up-to-date by using the data refresh and scheduling features in the tool.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?