Learn the differences between effective data quality and data governance practices and how to integrate them to enhance your data strategy.

Both quality and privacy of consumer data are tough problems to solve. What makes it even more difficult is that organizations today have scattered consumer data landscapes. Consumer data is captured ...

According to Jason Downie, vice president and general manager of data solutions for Lotame, here are three of the key problems holding back data quality and how CMOs can solve for them.

Learn the definition of data quality and discover best practices for maintaining accurate and reliable data.

It’s no longer how good your model is, it’s how good your data is. Why privacy-preserving synthetic data is key to scaling AI.

As data-centric AI, automated metadata management and privacy-aware data sharing mature, the opportunity to embed data quality into the enterprise’s core has never been more significant.

Data quality is the backbone of AI. It enhances the performance of algorithms and enables them to produce dependable forecasts, recommendations and classifications.

Inadequate data strategy, poor governance and a lack of collaboration between teams all contribute to companies relying on poor-quality data.

Everyone relies on data differently depending on their goals and interests, which means the implications of data quality varies depending on who’s using it.

Data structuring techniques vary based on the type and purpose of the data. For example, relational databases store data in tables with rows and columns, making them ideal for structured data.

Nevertheless, data preparation goes beyond standards to frequency of collection to data format consistency with the appropriate level of granularity. “The single best practice I see is to have proper ...