"You can have all the tools, but if data quality’s bad, you're nowhere."–Veda Bawo[Director-Data,Raymond James] When facing tight deadlines and data quality tasks, start by focusing on the most critical data that directly impacts business decisions. Poor quality data leads to wrong outcomes and bad business decisions. Set practical standards for what’s “good enough” to meet immediate needs and break complex tasks into smaller, manageable parts. Use tools like Tableau, Power BIcor Pandas for profiling, cleaning, & dashboards. They help spot issues fast. Delegate non-urgent stuff to stay on top of priorities. Align stakeholders on data quality’s long-term value to stay organized & ensure decisions are accurate.

I prioritize by concentrating on the activities that will have the biggest influence on our data integrity and overall business outcomes when I am presented with a mountain of data quality chores and approaching deadlines. I set clear deadlines for each activity and divide more complex ones into smaller, more manageable ones. By keeping me focused and organized, this strategy makes sure that I take care of the most important problems first. In order to free up critical time for more intricate and strategic data quality projects, I also use automation technologies to expedite repetitive activities. I can effectively manage my workload and provide high-quality data that supports well-informed decision-making by combining these tactics.

I personally prioritize tasks based on urgency and importance, while delegating when necessary. For instance while working in the PMI Kinga malaria project, I was incharge of the entire sub-county with 5 operations sites. To ensure the campaign was successful and all the operations were running optimally, I had to prioritize tasks based on urgency and importance, while delegating when necessary. I had reports to write, meetings to attend, issues to resolve, data to sync and had to also conduct data quality audits. To achieve these tasks concurrently I had a detailed schedule that kept me organized and on my toes. I planned my activities the night before. This strategy enabled me to successfully complete the tasks in time.

Managing data quality tasks requires a mix of strategy and tools. I prioritize tasks by their impact on business outcomes, addressing critical issues like inaccurate or missing data first. Breaking tasks into smaller, actionable steps with clear deadlines helps me stay organized and meet tight schedules. I also leverage automation tools like Power Query or Python for repetitive processes, saving time for more complex problems. Collaboration is key—I communicate regularly with stakeholders to align on priorities and delegate tasks when possible. Lastly, I track progress using dashboards or project management tools to ensure nothing slips through the cracks.

Build a Long-Term Data Quality Strategy When you're buried in urgent data quality tasks, it’s easy to forget about the long-term. But investing time in preventive measures—like automating quality checks or improving data entry guidelines—can save you stress later. By building a proactive, scalable strategy, you’ll reduce firefighting and focus more on driving business value and data excellence in the long run.