Data quality is critical for successful AI projects, but you need to preserve the richness, variety, and integrity of the original data so you don’t sabotage the results.

Mary Branscombe's avatar
1y

my editor keeps going to workshops about getting your data ready for enterprise AI and came back with this question: can your data be *too* clean to be useful and yes, in several ways! It was interesting to see the shift from old-school data cleansing to 'the noise might be signal'

When is data too clean to be useful for enterprise AI?

When is data too clean to be useful for enterprise AI?

Data quality is critical for successful AI projects, but you need to preserve the richness, variety, and integrity of the original data so you don’t sabotage the results.

www.cio.com

Mary Branscombe's avatar
1y

it was interesting to find a couple of places where the problem with some known genAI failures was the data (or the benchmark) being too clean so important signal got lost (like this training data is satire)

When is data too clean to be useful for enterprise AI?

When is data too clean to be useful for enterprise AI?

Data quality is critical for successful AI projects, but you need to preserve the richness, variety, and integrity of the original data so you don’t sabotage the results.

www.cio.com

When is data too clean to be useful for enterprise AI?
Data quality is critical for successful AI projects, but you need to preserve the richness, variety, and integrity of the original data so you don’t sabotage the results.
https://www.cio.com/article/3611247/when-is-data-too-clean-to-be-useful-for-enterprise-ai.html

  • AI

  • data quality

  • data cleansing

  • context

  • data hoarding

  • signal and noise

  • bias