For decades, data science emphasized the importance of cleaning data before analysis. However, recent advances in AI and language models have redefined this approach. Now, even unstructured and messy data can be effectively utilized. Various data management systems have been implemented, focusing on obtaining clean data for analytical purposes. However, with the rise of large language models, the ability to extract meaningful insights from otherwise chaotic data sources has evolved, allowing for new opportunities in data analysis without the necessity for extensive cleaning.
The recent advances in AI and language models indicate that even the messiest, most neglected data sources are becoming surprisingly easy to work with.
For decades, the rule in data science was simple: clean your data or don't bother, but that rule is starting to break.
LLMs' ability to extract meaning from messy data showcases how our ability to work with unstructured data has transformed recently.
The shift in data management approaches facilitates handling unstructured and dirty data in ways that were unthinkable just a few years ago.
Collection
[
|
...
]