You're Doing It Wrong: How to Get Data Science Right

Forrester has reported that "while 74 percent of enterprise architects aspire to be data-driven, only 29 percent say their firms are good at translating the resulting analytics into measurable business outcomes." Part of the problem is that when it comes to this critical "last mile," connecting data science insight to actions that drive bottom-line business results, too many organizations are doing it wrong.

To get greater value from analytics, organizations need to view data science not as a project, but as a process that includes a range of stakeholders and a repeatable set of steps, including feedback loops that engender continuous improvement. This whitepaper looks at some of the key mistakes that businesses keep making when it comes to machine learning and predictive analytics, and outlines a better approach, a blueprint that will help ensure more streamlined, and ultimately more successful, analytic efforts.

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