use an example !! My favourite is when NASA lost its mars climate orbiter because someone failed to use the correct units. Also, if you do know which data points are incorrect, you can run a small excel sheet and show how a tiny error in data quality can become magnified when the erroneous data is transformed and used for inference.

I've actively championed the importance of data quality in fast-paced, result-driven environments. By showcasing the long-term benefits, sharing real-world cases, and engaging stakeholders in data validation processes, I’ve helped establish a shared understanding of its critical role. This approach has consistently ensured more reliable insights and better decision-making outcomes.

To convince stakeholders that data quality is non-negotiable, showcase the long-term benefits, explaining how high-quality data supports better decision-making and sustainable growth. Use real-world examples where poor data quality led to significant setbacks, underscoring the risks of cutting corners. Engage stakeholders in the process by involving them in data quality checks, helping them understand its importance firsthand. Share strategies you've used with clients to emphasize the critical role of data quality, even when stakeholders push for quick results. This ensures stakeholders recognize that investing in quality data upfront leads to more reliable outcomes and better long-term success.

When stakeholders demand quick results, I emphasize that data quality is the foundation of any meaningful outcome. I illustrate the long-term advantages of accurate data, showing how it minimizes risks, improves decision-making, and drives sustainable success. Sharing real-life examples of failures caused by poor data helps demonstrate the cost of rushing quality. To build trust, I involve stakeholders in the process, explaining the measures taken to balance speed and precision, ensuring their goals are met without compromising integrity. This approach fosters understanding that quality isn’t a delay-it’s a necessity for reliable results.

To help stakeholders understand that data quality is crucial, highlight its impact on outcomes. Share real-world examples of failures caused by poor data and emphasize how accurate data drives reliable decisions and long-term success. Involve them in the process to build trust and alignment.