3 Predictive Modeling Flaws That Cripple Data Science Projects
Low data quality, a lack of data lineage, and isolated data scientists are common roadblocks to data science work. Learn how to overcome them in this article from TechTarget.
Data science can be highly rewarding for companies that do it right. But in many cases, poor data quality and the inability to track how data is collected and used, as well as having a data science team that is isolated from decision makers, can take a toll on the value of data science work.
In this article on TechTarget, DataScience CEO Ian Swanson explains how companies can overcome these roadblocks to build predictive models that are both accurate and valuable.