How do you create a data quality dashboard?

I am going to be counterintuitive here and raise a question: If your data quality metrics are based on the levels of what you expect to see in the data, why not focus on improving your processes to eliminate the introduction of known flaws? The most egregious data quality issues are "unknown unknowns" that cannot be measured proactively but insinuate themselves into the data without your knowledge. Measuring things you can fix only highlights flaws in your data strategy. If a known flaw exists and you didn't fix it, shame on you. If a known flaw existed and you fixed it, your measure will always be good.

Defining your data quality goals involves setting specific standards and expectations for your data that align with your business objectives, data strategy, and the needs of stakeholders. These goals might include attaining high levels of data completeness, accuracy, consistency, timeliness, and validity. Additionally, it's important to identify relevant data sources, domains, and elements that are crucial to achieving these data quality goals.

Defining data quality goals involve aligning them with business objectives and stakeholder needs. In my experience, setting specific standards for data accuracy, completeness, consistency, timeliness, and validity has been crucial. One thing I found helpful is incorporating the organization's overall data strategy into the data quality goal-setting process. This ensures that data quality efforts are not isolated but instead contribute to the strategic decision-making processes of the organization. By identifying key data sources, domains, and elements that are essential for achieving these quality goals, organizations can establish a strong foundation for reliable and actionable data.

Before creating a data quality dashboard, it’s essential to clearly define what you aim to achieve with it. This involves setting specific goals related to data accuracy, completeness, consistency, and timeliness. Understanding what aspects of data quality are most important for your organization guides the design and functionality of your dashboard, ensuring it provides relevant and actionable insights.

In our client projects, defining data quality goals is the first critical step. We align these goals with business objectives, data strategy, and stakeholder needs. For example, aiming for high levels of completeness, accuracy, and timeliness ensures data meets organizational standards. Using professional MDM software like zetVisions SPoT helps identify relevant data sources and elements, streamlining the goal-setting process to maintain top-notch data quality.

Once your goals are set, selecting the right metrics is crucial for your data quality dashboard. These metrics should effectively measure the aspects of data quality you've identified as most important, such as error rates, completeness percentages, or timeliness. Choosing appropriate metrics ensures that your dashboard will accurately reflect the health of your data and guide informed decision-making

Choosing your data quality metrics is a critical step in assessing the quality of your data. These metrics are essentially quantitative measures that help you evaluate the extent to which your data meets the goals you've set. Key data quality dimensions such as completeness, accuracy, consistency, timeliness, and validity can be measured with these metrics. To do this, you can use formulas, rules, or algorithms that compare your actual data against a reference standard or a predetermined threshold. This comparison allows you to objectively quantify the quality of your data.

Selecting the right data quality metrics is essential for evaluating performance. We use zetVisions SPoT to measure dimensions like completeness, accuracy, and consistency through advanced algorithms. This software calculates error rates, completeness rates, and accuracy rates, providing a clear overview of data quality. Tracking these metrics over time allows us to identify trends and address issues proactively, ensuring data integrity.

Designing a data quality dashboard requires understanding your audience, defining the purpose, and organizing dashboard elements logically. The dashboard should include a clear title, filters for customization, charts or tables for visual representation of metrics, and legends and labels for clarity. Aesthetic consistency and user feedback are also essential for an effective dashboard.

In my experience, we created a dashboard using PowerBI to visualize the output of data profiling tasks. This dashboard emphasizes all potential data quality issues or even data structure problems. For instance, the number of data quality issues is visualized per column using bar charts, while cards are used to visualize the occurrence of each type of data quality issue for each column.

Design your dashboard to be intuitive and user-friendly, ensuring that key metrics are easily accessible and understandable. Consider the needs of your audience and customize the visualization to highlight trends, issues, and improvements in data quality. Effective design facilitates quick identification of areas needing attention, driving actionable insights.

Understanding the audience for your data quality dashboard is crucial. Identify their needs, preferences, and technical proficiency to tailor the design and content accordingly. Consider adding filters and tooltips to increase user engagement.

When designing the dashboard, use metrics as the main focal point, then use drill down to show the violations. Anomaly detection is a good method to incorporate and alert the users. Also data drift is a new statistical method to showcase the variation that can tell a story.

Building a DQD involves using appropriate software to create a visual tool that communicates the state of your data quality. Choose Platform: Data quality software can provide functions like data profiling, cleansing, validation, and monitoring. Business intelligence software allows you to connect to data sources, create charts and tables, and publish your dashboard. Design Dashboard: Use web development languages like HTML, CSS, JavaScript, and Python to build your dashboard. These can help you code and deploy various elements and enhance functionality and aesthetics. Implement Dashboard: Once built, implement the dashboard in your organization. Ensure it's accessible to your team and provides actionable insights on data quality.

It is usually better to reuse the tools already used in the stack! If you are using a BI tool, it can be a good start. The open-source BI tools like Apache Superset and Redash (especially this) are really easy to set up and get going with. Many Quality assurance tools provide monitoring dashboard capabilities as well. However, the use-case of monitoring business-specific data quality trends fits the BI space better!

Starting with the existing BI tool in your organisation or Microsoft Excel is a practical approach for building a data quality dashboard. This ensures quicker adoption and acceptance among users. Begin with basic visualisations and gradually expand as needs evolve, adhering to the "start small, think big, and grow fast" mantra. Additionally, if senior stakeholders prefer Excel despite advanced BI tools, accommodate their preferences to ensure effective communication and decision-making.

In our experience, tools such as PowerBI, Tableau, and Apache Superset were used to create data-quality dashboards. Choosing the visualization tool depends on several factors, especially licensing. For instance, Apache superset can be used at no cost since it is open-source.

Utilize data visualization tools and software to bring your design to life. Integrate your chosen metrics and ensure the dashboard pulls the latest data automatically for real-time accuracy. Building your dashboard with scalability in mind allows for future enhancements as your data quality goals evolve.

Testing your data quality dashboard involves several steps: Data Verification: Ensure the data displayed is accurate, complete, consistent, timely, and valid. It should align with your data quality goals and standards. Usability Testing: Check that the dashboard is clear, informative, attractive, and user-friendly. It should be free of bugs, glitches, and errors. Collect User Feedback: Gather feedback from users about their experience with the dashboard. This can include their opinions, suggestions, and any questions or complaints they have. Iterate Based on Feedback: Use the collected feedback to make improvements to the dashboard, enhancing its effectiveness and usability.

In interactive dashboards, such as the ones created using PowerBI, users can add a table visualization to show the data source where profiling is executed. This gives the ability to check if the data profiling outputs are accurate or not from within the dashboard.

In a recent project, we built a data quality dashboard to monitor the accuracy and completeness of our customer data. During the testing phase, we discovered several discrepancies between the data displayed on the dashboard and the source data. This highlighted the importance of rigorous testing and verification. We conducted user acceptance testing (UAT) sessions with key stakeholders, which helped us identify usability issues and areas for improvement. By incorporating their feedback, we enhanced the dashboard's clarity and functionality, ensuring it effectively communicated data quality metrics and trends to its users. This process underscored the critical role of user feedback in refining and perfecting the dashboard.

Before rolling out your dashboard, conduct thorough testing with end-users to ensure accuracy and usability. Gather feedback on the dashboard’s functionality and clarity of the data presented. Testing helps identify any technical issues or areas for improvement, ensuring the dashboard effectively meets the needs of its users.

The Data Quality dashboard needs to be maintained once established. Needs to look for new business domains and/or data set that needs to be profiled. New features like showing historical changes, acceptable range of percentages, adding new business rules, and Enhancing existing business rules can be part of maintenance. Feedback from business stakeholders and data consumers could also help maintain the data quality dashboard.

Regular maintenance of your dashboard is essential for ensuring its continued relevance and effectiveness. This includes updating metrics and data sources as needed, as well as making adjustments based on user feedback and changing data quality goals. Ongoing maintenance ensures the dashboard remains a valuable tool for monitoring and improving data quality.

DQ dashboards should have trend charts, PDEs, CDEs, Datasets,, Data quality dimensions numbers and their quality based on DQ score calculations as good average and poor (for sample as green Amber and Red). Reds should be listed in separate tab to take quick actions by management.

Data quality dashboards should be periodically maintained and updated based on the user-experience and feedback. Also, it is good to implement new visualization and design best practices to improve the dashboard readability.

Maintaining the dashboard involves regular updates and improvements. We monitor data changes and update dashboards accordingly using zetVisions SPoT. Reviewing performance metrics and user feedback helps us keep the dashboard relevant and efficient. By adapting to evolving data sources and user requirements, we ensure the dashboard remains a valuable tool for maintaining high data quality standards.

Need to build a data quality warehouse where the results from any data quality assessments are accumulated. This database will serve as the base for your dashboard Also need to connect the platform to this data quality database to provide expected returns. Choose the relevant data quality metrics that align with the organization’s objectives and the intended goals. Also need to provide visual elements such as charts, graphs and tables to present the data effectively. Also intuitive dashboard would be better understood.

No matter what tool you use, the basics are to highlight the data issues and getting the buy in from the business to get them fixed. And then continue to enhance and monitor periodically. This is a journey and one that can save the money and resources in the long run. Also its a fun endeavor with so many visualizations and tools now-a-days that are at a developers bay.

One comment I heard from many user organizations WRT dashboards is the ability to reveal the actionable insights derived the dashboards. Too many pretty dashboards without meaningful and actionable insights can lead to "dashboard fatigue".

Beyond the technical aspects of creating a data quality dashboard, consider embedding principles of data governance and data literacy within your organization. Educating stakeholders on how to interpret dashboard insights and take corrective actions is crucial for fostering a culture of data quality. Additionally, explore integrating predictive analytics to anticipate data quality issues before they arise, enhancing proactive data management

Integrating data quality initiatives into your organisation's Software Development Life Cycle (SDLC) is paramount for ensuring robust data integrity and reliability across systems and processes. This approach entails embedding data quality considerations at every stage of the SDLC, from initial requirements gathering through to deployment and ongoing maintenance.