The basic predictive efficiency of AI models is defined by diversity, scale and quality of input data. -> Coverage & Availability ensure that the required coverage of data across customers ...

You need to give deliberate thought and engineering to the role and effects of data quality in whatever type of analytics solution you are planning. How then do we incorporate these aspects of ...

data quality is becoming a significant issue. Yet data is one of the most under-valued aspects of AI. This needs to change. One of the fundamental adages of computer programming has always been ...

Without accuracy, all other aspects of data quality, such as completeness and consistency, become less valuable since the foundational data itself is flawed. By signing up to receive our ...

It can be tough to manage data manually, and doing so can sometimes lead to errors or inefficiencies. Spreadsheets can get overly complex, and data quality ... one or a few aspects of the overall ...

But solving data quality problems requires a deep understanding of what the data means and how it’s collected. Resolving duplicate data is one issue, but when the data is just wrong, that’s ...