How do you test and validate your dashboards before launching them?

Launching a dashboard without rigorous testing is like setting sail without a map. Start by clearly defining your dashboard's objectives and target audience. This forms the foundation for measuring its success and identifying potential issues. What data should it present? What user actions do you expect? 1. Data Timeliness: Confirm data is regularly updated and reflects the current state. 2. Visualization Functionality: Test charts and graphs for clarity and functionality. 3. Filter Functionality: Ensure filters and drill-down work as expected. 4. User Interface: Evaluate navigation and UI for ease of use and accessibility. 5. Security Testing: Protect sensitive data from unauthorized access and breaches.

Testing and Validating Dashboards:- 1. Define Goals & Metrics - What questions should the dashboard answer? What success metrics will be used? 2. Choose Testing Methods - Unit tests: Validate individual components like charts & filters. - Integration tests: Ensure data sources & interactions work together. 3. Validation - Check accuracy & consistency with source data. - Test calculations & aggregations. - Verify filtering & sorting functionality. 4. Functionality - Ensure all features work as intended. 5. Performance Testing - Check responsiveness under high data volume. 6. Security Testing - Verify data access permissions. 7. Feedback - Gather feedback from users & stakeholders. - Refine and iterate based on feedback. - Repeat Above

Testing and validating dashboards before launching them involves a clear definition of objectives and requirements. Objectives outline what the dashboard aims to achieve, such as improving decision-making or monitoring specific metrics. Requirements detail the dashboard's necessary features, data sources, visualizations, and user interactions to meet these objectives. Objectives: Set clear, achievable goals for your dashboard. Understand what success looks like for your users and how the dashboard will serve their needs. Requirements: Detail the technical and functional specifications based on your objectives. This includes data accuracy, update frequencies, visualization types, and security protocols.

I've learned that clear objectives and well-defined requirements are the foundation of effective dashboard design. Before testing and validation, I ensure each dashboard has a defined purpose, directly addressing specific business goals and user needs. This clarity guides the selection of relevant data sources and KPIs, ensuring that the dashboard is not just a collection of data visualizations, but a tool for informed decision-making. By setting these parameters early, it's easier to evaluate the dashboard's effectiveness during testing, ensuring it meets both functional and business objectives without extraneous features that could detract from its utility.

1. Define objectives clearly 🎯: Know what you want to achieve. 2. Understand your audience 👥: Tailor dashboards to users' needs. 3. Keep it focused 📊: Avoid unnecessary features. 4. Test thoroughly 🧪: Ensure accuracy and usability. 5. Iterate with feedback 🔄: Continuously improve.

When it comes to design and layout, I like to use a so-called report profile. All the important points are noted in this, such as - Report and data owner - Target group of the report - Possible end devices on which the report is to be consumed and so on. In addition, I always organise a design session with the departments to get a feel for what the department would like. I also create templates on which the departments can then build their own mock-ups.

Thorough testing and validation of dashboards is crucial in BI to ensure accuracy, functionality, and user satisfaction. This process involves multiple stages of verification to identify and resolve issues early. 1.Data Validation: Verifying source data integrity and KPI accuracy 2.Functionality Testing: Conducting UAT and checking all interactive features 3.Performance Optimization: Ensuring fast query times and efficient data handling 4.Security Checks: Implementing proper access controls and data protection 5.Cross-platform Compatibility: Testing across devices and creating documentation This process minimizes issues, enhances user adoption, and maximizes the value of our BI solutions, supporting robust data-driven decision-making.

The layout should intuitively guide the user through the most critical data insights, ensuring a logical flow. For visuals, it's about choosing the right types of charts and graphs that best represent the underlying data, avoiding any that might lead to misinterpretation. Utilizing BI tools effectively, I adhere to design principles like grid systems and element alignment to maintain visual harmony. Grouping related information aids in storytelling, while a thoughtful selection of colors, fonts, and icons enhances readability. Additionally, utilizing templates and themes can greatly aid in maintaining a consistent and professional aesthetic across all dashboards, crucial for brand integrity and user experience.

Design the layout and visuals as if you are telling a story. To tell a data story visually, start by defining the key message you want to convey. Select visuals like charts and graphs that align with your narrative and are easy to understand. Use color and size to highlight important data points and guide the viewer’s attention. Arrange your visuals in a logical sequence that flows naturally, telling the story step by step. Finally, ensure that your design is clean and uncluttered, allowing the data to speak for itself and tell the story effectively.

1. 🎨 Purposeful Design: Ensure dashboards meet users' needs. 2. 📊 Visual Consistency: Maintain uniform visuals for clarity. 3. 🛠️ Tool Utilization: Use BI tools wisely to enhance, not overpower, dashboards. 4. 🖼️ Design Principles: Follow alignment, grouping, and color/font rules. 5. 📏 Logical Layout: Organize content logically for easy navigation. 6. 🎭 Uniformity: Employ templates for a professional look across all dashboards.

To ensure your dashboard's reliability: Verify Data Sources: Ensure they are credible and current. Check Data Quality: Look for and fix inconsistencies or errors. Test Calculations: Confirm accuracy and relevance to objectives. Update Regularly: Keep data fresh and relevant. Thorough Testing: Include edge cases to guarantee robustness.

re-calculating the numbers of dashboards in Excel is still common... you cannot avoid this until your data product gets the ultimate trust of users. However, if possible automation shall be used in data validation instead of manual exports and copy/paste. Even if it is not the ultimate visualization, I would reccomend to additionally include table or matrix views of the data in the dashboards, at least as drill-down-pages. Trust is more important than fancy visuals.

1) Confirm that data sources are accurate, up-to-date, and aligned with the report's objective. 2) Check for any inconsistencies, missing values, or outliers in the data. Address data quality issues to ensure accuracy. 3) Verify the accuracy of calculations, formulas, and aggregations used in the report. Cross-reference with the source data if needed. 4) Ensure that visual representations accurately reflect the underlying data. Check axis scales, labels, and legends for clarity. 5) Validate any benchmarking or comparisons against industry standards or predefined metrics to ensure relevance and accuracy.

Rigorous review of data and calculations is crucial for trustworthy dashboards. It's essential to ensure data accuracy and completeness, as even minor discrepancies can lead to significant misinterpretations. I employ BI tools for meticulous data transformation and validation, ensuring each calculation and aggregation is both correct and relevant to the dashboard's objectives. Implementing data quality checks and profiling is key to uncovering inconsistencies or anomalies, while data lineage helps trace data back to its source for verification. This thorough review process is vital not just for the integrity of the dashboard but also for maintaining user confidence in the BI system as a whole.

1. Ensure data accuracy, completeness, and currency for reliable insights 📊. 2. Validate calculations for consistency and relevance in decision-making 📐. 3. Leverage BI tools for efficient data import, transformation, and validation 🛠️. 4. Utilize data quality checks and profiling to spot anomalies and ensure reliability 🧐. 5. Maintain data lineage to trace back and resolve any issues promptly 🧬. 6. Regularly review and update your data processes for ongoing optimization 🔄. 7. Stay proactive in addressing data discrepancies to uphold credibility 🛡️.

There are two pillars of functional dashboards: - the first is compliance to standards and templates that ensure a common language in an organization. IBCS is a good choice for reporting and monitoring dashboards, but even for the more flexible analytical dashboards standards are necessary - the second pillar of user acceptance is continuous communcation with the users after the release of the first version. In reporting, you never know all requirenments and problems from the start, so agile development is not optional, but an absolute necessity. Fast support and service are more important than a complete definition and documentation

In validating dashboards, I prioritize functionality and usability testing. Leveraging BI tools, I conduct thorough tests across various devices and browsers to ensure responsiveness and compatibility. Performance metrics like loading times and security are scrutinized for optimization. But beyond technical testing, incorporating user feedback is crucial. I engage with actual users for their insights, as this often reveals practical enhancements that aren't immediately apparent. Monitoring user interaction patterns through analytics also offers valuable insights into usability and user satisfaction, guiding iterative improvements to ensure the dashboard effectively meets user needs.

Creating comprehensive documentation not only serves as a guide to the dashboard’s functionalities and data sources but also elucidates the design rationale and analytical insights. This transparency is crucial for stakeholder trust and user adoption. Utilizing BI tools, I ensure that reports and presentations derived from the dashboards are clear, concise, and informative, effectively communicating key findings and recommendations. Moreover, incorporating annotations, tooltips, and captions directly within the dashboards enriches the user experience, providing context and guidance that facilitate deeper understanding and engagement. This thorough approach in documenting and communicating enhances its overall impact and utility.

A test case document is a good way to seal the deal. Capture on all transformations that are happening on the BI tool once the data leaves the source. Build a test case to test each of the transformation step with the input and expected output. Document executing the report and generating the expected output. For an added user trust, enable end users to provide you with test case samples to test edge cases and complex business logics which might have introduced bugs or loopholes during development. Afterall they know the business better than a BI developer.

Another aspect that has not been mentioned is that dashboards should be continuously monitored for the accuracy of the information they display. The methods used to modify the data might become obsolete if the data structure changes or if unexpected data is entered. For instance, if a zero value is entered and it is used in a denominator, it is essential to establish a condition to handle such data.