Has COVID accelerated the adoption of Artificial Intelligence (AI) and Machine Learning (ML)?

The trend I've been seeing, at least in the last year, is there was a period of time everyone was just trying to get all the data together, make sense of all of it. They wanted to have an overarching data catalog that you could put control and governance on top of. Just this idealistic version where you could put everything together and manage it in a centralized way. At this point after people have gone through some tests and probably used some data applications, machine learning models, it's more and more clear a lot of these tools are very useful to the specific use cases they're trying to drive and the teams that're working on top of them.  You have to at least understand the purpose for collecting certain types of data, what you are using it for, what type of application or analysis you're trying to use. It's like your tool chain to control the quality, govern, or process analyze that data. I think that's sort of the realization people started having. And that's where I think you can get more forensic about getting everything together, whether it's lake or house or lakehouse. I think that will become more sort of decentralizing to each team who are trying to have a clear use case and view things specific for their needs. So that's my take.

Anonymous Author
The trend I've been seeing, at least in the last year, is there was a period of time everyone was just trying to get all the data together, make sense of all of it. They wanted to have an overarching data catalog that you could put control and governance on top of. Just this idealistic version where you could put everything together and manage it in a centralized way. At this point after people have gone through some tests and probably used some data applications, machine learning models, it's more and more clear a lot of these tools are very useful to the specific use cases they're trying to drive and the teams that're working on top of them.  You have to at least understand the purpose for collecting certain types of data, what you are using it for, what type of application or analysis you're trying to use. It's like your tool chain to control the quality, govern, or process analyze that data. I think that's sort of the realization people started having. And that's where I think you can get more forensic about getting everything together, whether it's lake or house or lakehouse. I think that will become more sort of decentralizing to each team who are trying to have a clear use case and view things specific for their needs. So that's my take.
0 upvotes
Anonymous Author
COVID has just continued to accelerate the adoption of AI and machine learning. And the enterprise was already on track, but I think it's been exciting to see places in healthcare and finance become more data enabled and data driven. Bringing those legacy enterprises into a more digital first world, has actually been really exciting to me.
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