I’m retired now, but My first priority was always to identify the sources of the bad data first and brainstorm how to plug the quality leaks; the closer to the source the better. My second priority was to go after low hanging fruit… if there is data I can repair today, then get it done. Then I would prioritize data repairs according to the business importance/urgency.

In my experience managing a large volume of legal cases, prioritizing high-impact errors is essential to ensuring effective strategic decisions. Focusing on resolving critical issues allows resources to be allocated efficiently, minimizing risks to operational effectiveness. Automated tools are valuable allies, identifying recurring patterns and inconsistencies, while the team concentrates on more complex matters. Additionally, I continuously review data quality benchmarks, aligning them with the evolving needs of the business. This approach turns challenges into opportunities for continuous improvement, ensuring integrity and reliability in analyses.

Focus first on errors that have the highest business impact, such as those affecting critical processes, decisions, or customer satisfaction. Address issues in datasets that are frequently used or widely shared to minimize downstream disruptions. High-severity errors that lead to incorrect outcomes should take precedence, especially if they occur frequently or affect large volumes of data. Prioritize fixing source data issues that impact dependent systems to prevent cascading errors. Additionally, tackle compliance-related issues to mitigate legal or regulatory risks. Finally, aim for quick wins by resolving tasks that are easy to address but have significant impact, ensuring steady progress while maintaining efficiency.

When faced with data quality issues, I prioritize tasks based on their impact and recurrence. Automated tools are invaluable for spotting patterns and streamlining repetitive checks, freeing up time to tackle more complex problems. For example, I once identified recurring discrepancies in a dataset by leveraging automated profiling tools, which quickly flagged duplicates and missing values. This allowed me to focus on resolving the root causes while ensuring reliable data for analysis. Regularly revisiting benchmarks further helps in keeping the approach relevant to evolving needs.

In my experience, all decisions are business driven. I would prioritize those issues that have the highest business impact. Secondly, I would pick on ones that impact user experience, then solve them. After this, the next logical step would be to automate data integrity protection measures, so as to minimize the reoccurrence of these errors. At these stage, I would also highly consider regulations and compliance.

As per my experience, while developing any application or implementing any new feature to an existing application as well the quality plays a major role . If we in a situation to handle data quality issues of an application, with specific tools like sonarqube and other tools which will showcase the quality, and highly impacted areas. So we can able to identify and fix those high priority issues and make necessary steps to avoid these in future

You have to coherently and comprehensively provide non-technical users with one source of truth across the entire business. I will happily articulate how to accomplish that (contact me). No need to be a genius at integration or data cleansing as long as you can take all the sources and develop calculations/measures that will be used across the organization. If you cannot create one truth, AI will do that in your place hence, you lose your job to AI. I mostly create visualizations, but take part in integration processes. I focus heavily on measures and calculations. Creating that one truth and that is actually SAP’s selling point. I would love to hear your thoughts.

My top most priority to fix the current cycle data first and let the show go on. Then fix the source with which bad data is getting inserted so the we can stop the further leakage. And at last make perform a data check and correct the data what has already been corrupted and has never been caught yet. This will save us in the future data processing.

When dealing with too many data quality issues, focus on what matters most: 1. Impact: Address issues that affect critical business goals or decisions. 2. Frequency: Prioritize recurring or widespread problems. 3. Risk: Fix issues tied to compliance or legal risks. 4. Stakeholders: Solve problems impacting key teams or workflows. 5. Effort vs. Value: Start with quick wins that require less effort but add high value. 6. Root Cause: Focus on systemic issues to prevent recurring problems.