The Inevitability of Data Science Modernization During the Machine Learning and AI Revolution

The Inevitability of Data Science Modernization During the Machine Learning and AI Revolution

In the business world, machine learning (ML) and artificial intelligence (AI) are keys to the future of modern enterprises. A recent survey from ESIThoughtLab found that two-thirds of business leaders see AI as critically important for their future. However, only 25% of AI projects are in widespread deployment and 40% of all projects are generating zero returns — or worse, negative returns.  

The message is clear: there’s a massive gap between the acknowledged value of data science and how it’s being implemented in businesses looking to innovate. 

For ML and AI to reach their true potential, businesses must address several fundamental factors that potentially hold their modernization efforts back. Fortunately, tackling these challenges is possible with the right approach and a little guidance from the trusted industry experts at Maven Wave. 

Ready to get started? Download this comprehensive white paper to uncover strategies that will streamline your data science modernization efforts and catapult your business into the here and now (and beyond).

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An Overview of Data Science Modernization

Why should today’s businesses prioritize data science modernization? We’ll break down the definition and explain why it’s key to current and future business models.

Problems with Traditional, On-Premises Solutions

Getting an understanding of the limitations and drawbacks of the current state is a helpful way to look at what is possible with a modern approach.

A Roadmap for Data Science Modernization in the Real World

We provide a step-by-step strategy for tackling data science modernization in your business.

“Today’s enterprises must embrace data science modernization to not only take advantage of the increased insights, enhanced cadence, and improved innovation but also to keep up with the competition.”