Inside Analysis

The Top Ten Challenges to Operationalizing Data Science

Data science provides today’s businesses with an unprecedented opportunity to increase revenues and lower costs by leveraging existing data assets. And companies that seamlessly integrate data science into their operations are taking market share from those that don’t. But data science is not a panacea. It is complex and possesses many challenges. Far too often, models created by data scientists are never deployed and powerful models are incorrectly created or misapplied. This can lead to expensive mistakes.

To better understand these challenges and their solutions, we conducted more than thirty in-depth interviews with data science experts from a variety of industries. From those interviews, we compiled the top ten challenges and the corresponding best practices in technology, process and organization. This interactive webcast will review this groundbreaking research and help you understand:

-How to spot these challenges in your company across data prep, model development, DevOps, and business delivery

-Key insights and practical ideas for best practices to overcome these challenges

-What to look for in product features that support these best practices

-Building the business case for automating and operationalizing your data science initiatives

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