They can also share and collaborate on data science projects, since subject matter experts can simply drag and drop data sets into machine learning models that are easily pushed to production by data scientists through a publishing wizard to predict better outcomes. Data scientists are more productive, since the collection and processing of big data has become automated. They were spending more time on acquiring, merging and making sense of data and less time building models that could help solve the critical issues the business was facing.īy embracing a flexible AI and analytics platform with a data science notebook, predictive modeling and flexible data visualization options, the organization broke down its silos and enabled cross-functional collaboration. The company’s data scientists were also getting bogged down with the ever-growing volumes of structured and unstructured data. The company’s data scientists created machine learning models but lacked the ability to share projects in a way that would resonate with subject matter experts. With a predictive modeling knowledge base, algorithm customization and customized dashboards, data scientists have what they need to deploy and collaborate on data science projects that add value to the organization.Īn organization has data scientists, subject matter experts and business users, all of whom excel in their roles but lack a collaborative tool for connecting across teams. It increases productivity by automating big data processing and enabling a deep dive into massive amounts of data for improved visibility. OpenText ™ Magellan ™ for Data Scientists empowers data scientists to create and share machine learning models across the organization.
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