The next generation of enterprise search is using data-based analysis, contextual clues, and data-driven machine learning to give users the most accurate relevant results. Learn about the benefits and tradeoffs of signals-based and rules-based analysis and recommendations.
In this new white paper, IDC Analysts Carrie Solinger and David Schubmehl give you an overview on the advantages of using context versus data for relevancy. You will learn:
- Why a rules-only approach falls short for providing the most relevant search results to end users
- How signals are captured, stored, and aggregated to create dynamically adjusting relevancy
- All the different types signals that a search application can capture for calculating relevancy and getting users to their “next best action”
- Cost and resource concerns and constraints when moving to signal-based relevancy in your search and data apps
- How machine learning and data-based analysis are paving the way for the next generation of intelligent search systems