How can you improve your BI processes with data quality feedback?

1 What is data quality feedback?

Data quality feedback is the process of collecting, analyzing, and acting on information about the quality of your data sources, pipelines, and outputs. It involves identifying and resolving data issues, such as errors, inconsistencies, duplicates, or missing values, as well as measuring and improving data quality metrics, such as completeness, validity, timeliness, or relevance. Data quality feedback can be obtained from various sources, such as data owners, data consumers, data stewards, or data quality tools.

2 Why is data quality feedback important for BI?

Data quality feedback is essential for BI in order to ensure that data is fit for purpose and BI outputs are trustworthy and actionable. Collecting and responding to data quality feedback can help you enhance your data governance and compliance, as well as increase your data literacy and awareness. Additionally, it can optimize your data performance and efficiency by reducing data waste, rework, and costs, while also improving data availability, accessibility, and usability. Ultimately, it can improve the value of your data insights by enabling more accurate, reliable, and relevant data analysis, reporting, and decision making.

3 How can you collect data quality feedback?

There are various methods and tools to collect data quality feedback, depending on the data sources, pipelines, and outputs. Data quality audits and assessments review and evaluate the data against predefined criteria and standards. Data quality surveys and interviews are also used to gather feedback from data stakeholders about their expectations, needs, and experiences. Data quality dashboards and reports present visualizations and metrics of the data quality status and trends. Finally, data quality tools and platforms automate and streamline the collection and analysis of feedback, providing features such as data profiling, validation, cleansing, monitoring, or alerting.

4 How can you act on data quality feedback?

Collecting data quality feedback is not enough; you must act on it to improve your BI processes. Prioritize and classify data issues by assessing their impact, urgency, and root causes, and assigning them to the responsible parties or teams. Resolve and prevent data issues by applying the appropriate techniques or solutions, such as data cleansing, enrichment, standardization, or transformation, and implementing data quality controls or checks. Communicate and collaborate on data quality feedback by sharing findings, actions, and results with stakeholders and incorporating their feedback. Track and measure data quality feedback by monitoring the progress of your actions and reporting improvements.

5 How can you improve your data quality feedback cycle?

To improve your data quality feedback cycle, it needs to be a regular and consistent part of your BI processes. To ensure success, you should define and align your data quality objectives and expectations, involve and empower your data stakeholders, automate and integrate your data quality feedback, and learn and innovate from your data quality feedback. Establishing clear and measurable data quality criteria and standards is essential for aligning them with business goals. Engaging and educating stakeholders about the importance of data quality feedback is necessary for providing them with the roles, responsibilities, and resources to participate. Leveraging data quality tools can simplify and speed up the collection of feedback. Analyzing insights and lessons learned from the feedback can help you explore new solutions.

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?