When overwhelmed with data discrepancies, prioritize by focusing on business-critical data first. Identify key metrics and datasets that directly impact decision-making, revenue, or compliance. Rank discrepancies based on their potential risk or impact on operations. Address issues that pose immediate risks, such as those affecting financial reporting, customer data, or compliance. For less critical discrepancies, assign them to a backlog for later resolution. Efficient prioritization also involves delegating tasks to the appropriate team members and leveraging automated tools for data validation, reducing manual effort and ensuring timely resolutions. Clear communication with stakeholders ensures transparency throughout the process.

When faced with data discrepancies and short of time to deliver the insights, it's better to go for feature engineering and focus on the attributes that are critical to decision making. Try to clean and validate those particular attributes and move ahead with analysis. Ensure that the stakeholders know about the activities performed on the data before it is presented to establish transparency and open channel for feedback. If the above is not possible, it's wise to request the concerned user for time. I am of the opinion to take time and present the correct data and insights rather than moving ahead in a hurry and give wrong picture to the management.

1. Assess impact of each discrepancy 2. Consider timeline criticality and prioritize discrepancies that could affect deadlines or milestones. 3. Group similar issues by type or source. 4. Focus on discrepancies in critical data points and with a history of data quality issues. 5. Communicate clearly and Keep stakeholders informed 6. Document decisions: Maintain a clear record of prioritization decisions and rationales for future reference and potential audits. 7. Seek additional resources/ help if required. By following these steps, we can ensure that the most critical data discrepancies are addressed first, maintaining data integrity and quality even under time pressure.

From experience, when faced with data discrepancies and limited time, the first step is to prioritize based on impact. I focus on discrepancies that affect key metrics or critical decisions. Start by identifying any patterns—grouping similar issues can help tackle multiple problems at once. Then, delegate tasks if possible, especially smaller, less urgent discrepancies. Ideally, I use automated tools and validations to speed up detection and resolution wherever possible. Lastly, it's important to communicate with the stakeholders, keeping them informed of any delays or critical issues, so expectations are managed while we work through the priorities.

When dealing with data discrepancies, it’s like the classic scenario where the boss wants the report "yesterday," but you just received the data 5 minutes ago! Often, the data I receive makes me wonder if a 4th grader was in charge of data collection. With tight deadlines and messy data, the key is to quickly assess what’s critical, build the best possible report, and clearly highlight any gaps. I never shy away from flagging missing or inconsistent data—honesty is the best policy when you're racing against the clock and trying to prevent future headaches.