Originally published in Analytics Magazine For a rocket scientist, the math isn’t the hardest part. What’s hard is being so often misunderstood. The same goes for data scientists, who time and again lack the support needed to successfully launch the fruits of their brilliant labor into action. These math heads have got to integrate into the organization as a whole, lest they vanish into the obscurities of their analysis. Their isolation is an enemy to their usefulness. After all, the most wicked and pervasive pitfall of predictive analytics is organizational in nature, not technical: Predictive models often fail to launch.
To view this content
OR subscribe for free
Already receive the Machine Learning Times emails?
Click here to complete this one-time subscription upgrade
The Machine Learning Times now requires legacy email subscribers to upgrade their subscription - one time only - in order to attain a password-protected login and gain complete access.