Data quality management efforts — tied to disrupting innovations, rapid market shifts and regulation pressures — will continue to grow in 2023 and take on a more dominant role in the data ...

In this article, we'll look at the challenges of traditional data quality management and how you can get started with augmented data quality. Machine learning models identify and correct data ...

Poor data quality costs organizations an average of $12.9 million a year. Organizations are beginning to recognize that not only does it have a direct impact on revenue over the long term, but ...

Image: WrightStudio/Adobe Stock. Data quality management is a crucial aspect of any business’s growth and operational strategies. But achieving high data quality requires that specific roles ...

Total Data Quality Management. If actual data quality improvement is not an option in the short term for reasons of technical constraints or strategic priorities, it is sometimes a partial solution to ...

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.

Powered by Monte Carlo’s data profiling engine Data Profiler, GenAI Monitor Recommendations uses a generative AI model to determine relationships between columns and suggest data quality rules.

Soda, which has raised more than $14 million in funding to date, plans to mitigate data incidents by using generative AI to shift data quality management left to nontechnical staff and insight ...

Data is a cornerstone of strategic decision-making and operational efficiency for businesses across all industries in today’s digital landscape. However, as the volume of data continues to expand ...

As they address data-management challenges while shoring up student retention and boosting enrollment, they need clear, well-organized data to avoid “garbage in, garbage out,” in which poor quality ...