Advanced analytics and predictive modeling are now essential to businesses looking to optimize decision making and deliver value to customers. But even as organizations invest more resources in big data and data science, executives are not yet realizing their anticipated return on investment.
The reason is a less-than-ideal data science workflow where data scientists are working across many disconnected tools while grappling with an excess of data management tasks, a lack of engineering support, and an inability to operationalize their output. The solution is a platform that puts the tools needed to create, socialize, and deploy data science projects all in one place. Here’s why.
Author: Tim Rizzi