Insights on Top Challenges

From 100+ Data Professionals

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As more companies make the leap into Enterprise AI, learning best practices from others who have been there before becomes even more critical.
 
We surveyed more than 100 data professionals at the EGG Conference in New York City to find out more about the challenges they are currently facing, how their larger organization approaches data science and machine learning, and how these approaches ultimately affect their ability to execute (as an individual, as part of a team, and as a company).
 
This report uncovers some of the top insights from this survey paired with suggestions for further reading or for addressing the particular challenges.
 

What's Inside the Report

In the age of Enterprise AI, organizations across industries are rapidly working to expand their capabilities and move from traditional BI and advanced analytics to machine learning technologies (including deep learning and more). But that doesn’t mean there aren’t some growing pains along the way.

 

At the end of the day, it’s no wonder that businesses are meeting roadblocks along their path to Enterprise AI, because - well - doing AI is hard.

 

We surveyed more than 100 data professionals at the EGG Conference in New York City to find out more about the challenges they are currently facing, how their larger organization approaches data science and machine learning, and how these approaches ultimately affect their ability to execute (as an individual, as part of a team, and as a company). This report uncovers some of the top insights from this survey paired with suggestions for further reading or for addressing the particular challenge.

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See How You Stack Up

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Get the Survey Results:
Insights from 100+ Data Professionals