Скачать или смотреть Data Quality: The Hidden Cost in Business Intelligence (That No One Wants to Talk About)

In this conversation, Juan Jose Keena and Paul McLeod discuss the pervasive challenges of data quality across organizations.

They explore the various types of data quality issues, the importance of proactive management, and the role of tools and governance in ensuring data integrity.

The discussion also touches on the potential of AI in addressing data quality problems and the significance of measuring ROI related to data quality improvements.

Ultimately, they emphasize the need for collaboration between IT and business teams to effectively manage data quality and build trust in data-driven decision-making.

Takeaways

Data quality is a universal challenge for organizations.
Poor data quality can lead to significant financial losses.
AI can exacerbate data quality issues if not managed properly.
Data quality issues often stem from human error in data entry.
Establishing ownership of data quality processes is crucial.
Tools are essential for automating data quality checks.
Data governance is integral to maintaining data quality.
Trust in data is built through consistent quality management.
Measuring ROI on data quality improvements can be complex.
Collaboration between IT and business is necessary for effective data quality management.

Chapters

00:00 Introduction to Data Quality Challenges
03:00 Understanding Data Quality Issues
05:56 Proactive Approaches to Data Quality
09:06 Establishing Ownership and Processes
11:58 The Role of Tools in Data Quality Management
15:08 Integrating Data Quality into Data Strategy
18:00 The Impact of AI on Data Quality
20:51 Measuring ROI in Data Quality
24:04 Building Trust in Data Quality
27:04 Final Thoughts on Data Quality Solutions