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The discussion highlights the importance of data quality and the frequent issues surrounding it. Often, data quality is poor due to user laziness or lack of consequences for incomplete data entry. The conversation emphasizes that data quality should be prioritized because it saves time and effort in the long run. Regular monitoring and addressing issues at the source are crucial.

Incomplete data can lead to significant problems, such as invoice reconciliation challenges and over-engineered applications that frustrate users. A balance is needed between necessary data collection and ease of use. Training on system use and the impact of poor data, as well as allowing time for data correction, can drive better behavior.

Assigning data ownership can help improve quality, but it depends on the individual's commitment. Organizations need a culture that values data quality, with visibility on how data is used and its impact on decision-making and reporting.

Poor data quality can cost money and lead to inefficiencies. Data migration often reveals underlying issues, and treating data quality as an ongoing practice is essential. Some companies take data quality seriously by not hiding issues in reporting, which drives better behavior. However, achieving perfect data quality is rare.

The key takeaway is to identify and enforce critical data points at the front end without over-engineering. Understanding the importance of clean data and its impact can drive organizational change.

Next week's discussion will focus on the long-term maintenance of data in the platform and the long-term cost of ownership.

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