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In the early days of the current dataanalytics revolution, one would often hear business owners say that they need their data to move at the speed of business. Well, it soon became clear that the real problem was the reverse: how can we have our business move at the speed of our data? Source: [link]
Combined, it has come to a point where dataanalytics is your safety net first, and business driver second. AI Adoption and DataStrategy. Lack of a solid datastrategy. In order to adopt AI solutions for your business, the best way forward is to first ensure that you have a strong datastrategy in place.
Assisted Predictive Modeling and Auto Insights to create predictive models using self-guiding UI wizard and auto-recommendations The Future of AI in Analytics The C=suite executive survey revealed that 93% felt that datastrategy is critical to getting value from generative AI, but a full 57% had made no changes to their data.
This was alongside keynotes by: Rebecca Williams from OMB at the Whitehouse—who helped develop the US federal datastrategy and year-1 action plan —check out her slides for the “Federal DataStrategy” keynote. Monica Youngman, director of data stewardship—check out her slides for the “Data Archiving at NOAA” keynote.
The ML models include classic ML and deeplearning to predict category labels from the narrative text in reports. The IT department also used the Hugging Face online AI service and PyTorch, a Python framework for building deeplearning models. Azure Databricks is also employed for dataanalytics as part of the solution.
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