According to a recent Gartner blog about analytics and BI solutions, only 20% of analytical insights will deliver business outcomes through 2022. Another article by VentureBeat AI reported that 87% of data science projects never make it into production. And a global survey by Dimensional Research concluded that 78% of their AI/ML projects stall at some stage before deployment. Even in 2022, as many as 68% of data scientists admit to abandoning 40% to 80% of their Data Science projects. These results indicate an exceptionally high failure rate across analytics, data science, and machine learning projects. There are many reasons why so many projects fail to meet their business objectives. In this blog, we look at the top practical challenges that enterprise AI projects face and how you can mitigate them:
See the power of Feature Engineering Automation with a personal demo.
Introduction Today, we announced the launch of dotData Insight, a new platform that leverages an…
Introduction Time-series modeling is a statistical technique used to analyze and predict the patterns and…
Introduction Time series modeling is one of the most impactful machine learning use cases with…
Introduction Building robust and reliable models in machine learning is of utmost importance for assured…
The past decade has seen rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML)…
The world of enterprise data applications such as Business Intelligence (BI), Machine Learning (ML), and…