[Download] The Missing Ingredient to Predictive Modeling Success
AI and ML underpin many core enterprise operations. This is especially true of predictive modeling, which turns historical data into behavioral insights and predictions about future behavior.
Unfortunately, most enterprise data scientists limit themselves to first-party data to build models that feed ML processes. While the value of first-party data is undeniable, its use in machine learning to build predictive models is less so. That’s because the predictive modeling process relies on an appropriate breadth and depth of data to train the ML algorithm and generate insights.
In this white paper, discover the limitations of relying on first-party data to train algorithms and how to overcome them.