The true cost of (poor) quality: How fraud and bad data impact the bottom line and send clients packing

Discussions around data quality in the research industry often overlook a crucial aspect: the true cost of bad data. This session directly addresses this overlooked issue. It delves into the real cost of bad data on two critical fronts: the tangible, direct impact on project costs and margins, and the less tangible but even more significant effects on client retention and growth.

This session provides a deep dive into the quantifiable costs associated with fraud and low-quality data, illustrating how they directly affect a business’s bottom line. We explore how these costs manifest, not just with individual projects, but also over time. The discussion then examines the less quantifiable, yet more impactful, consequences of poor data quality on client trust and business growth. We delve into how clients think about data quality and fraud – and how this impacts partner selection and loyalty.

Recognizing these challenges, this session presents a proactive approach to tackling fraud and ensuring data integrity. Further, we outline a game plan that leverages the latest technology and best practices to preempt fraud and bad data. This approach is not just about damage control; it’s about setting you up to prevent issues before they arise, thereby protecting and enhancing your client relationships.

Key Takeaways:

1. An understanding of the direct financial impact of fraud and poor quality data on research projects and business operations.
2. Insights into the effects of bad data on client retention and business growth.
3. Practical strategies and tools for proactively managing data quality, using the latest technology and industry best practices in fraud protection.