How are Machine Learning tools impacting organizations running data warehouses?

I don't know what percentage, but a majority of companies in the last two years were either built on top of data or around data. Data governance is helping people organize data. So that has been a trend where interest is driven by a couple of factors. One is everything moving into the Cloud. I think Cloud data warehouses have existed for the last five years since Redshift started up and there was Snowflake and BigQuery. That's one of the big drivers of the tool chain changing. So, up and down the stack of how do you get data in or where do you get data from to how do you actually make sense and analyze it? Machine learning is a big driver of that. These two I think are the biggest wins behind all of the trends we're seeing and the revamping of the data engineering tool chain.

Anonymous Author
I don't know what percentage, but a majority of companies in the last two years were either built on top of data or around data. Data governance is helping people organize data. So that has been a trend where interest is driven by a couple of factors. One is everything moving into the Cloud. I think Cloud data warehouses have existed for the last five years since Redshift started up and there was Snowflake and BigQuery. That's one of the big drivers of the tool chain changing. So, up and down the stack of how do you get data in or where do you get data from to how do you actually make sense and analyze it? Machine learning is a big driver of that. These two I think are the biggest wins behind all of the trends we're seeing and the revamping of the data engineering tool chain.
2 upvotes
Anonymous Author
It's interesting that IT organizations running data warehouses for business reporting purposes have devoted significant effort to improving the quality and integrity of their data feeds. These efforts have generally produced mixed results. I think machine learning modeling activities have focused attention on the poor quality of data in large companies in ways that conventional warehousing teams were never able to achieve.
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Anonymous Author
My research is on how you adapt to unexpected change. The most brutal form of unexpected change is kaleidoscopic change. And that's what we've gone through this last year. The role of the CIO in enterprise software has become the lifeline that's kept our economy alive and not just alive, but thriving. Legacy companies have been forced to make this transition to this digital world that we all live in now. These legacy companies have data that's in silos, in different formats. And then they look at these newer companies and see it's all digital and think, "Well, how on earth are we going to compete? What's going to happen when AI and edge come down? Are we going to have the right stuff there?" When you look at this whole Edge AI kind of area that's coming up, where operations and administration kind of become one, we're going to see a very different environment.
2 upvotes