LexisNexis spent much of the last year rebuilding its search abilities and migrating to Solr. The ongoing project manages more than 3 petabytes of data and has helped LexisNexis develop a data-driven culture through integration with other critical home-grown functions to improve search.
LexisNexis’ goals were to:
- Build a best-in-class search experience by migrating search to an open source engine.
- Deliver dramatically faster search performance for products
- Enable a faster path to delivery of search improvements.
Their activities to accomplish these goals is in progress and includes:
- Moving to a modern open source search engine that enables faster innovation, is cloud friendly and has lower operational costs.
- Implementing architecture that ensures search response time under a second and is noticeably faster than the competition.
- Deploying development continuously with higher quality and frequency, enable faster A/B testing, experimentation and automation.
They’re seeing some success – ingesting content at scale with a continuous integration and deployment pipeline that allows them to check data and statistics in real time.
Part of their process is to test – constantly checking data and statistics on ingestion rates and relevancy of search results.
Dennis Chaney, the program manager, says that these changes are already driving innovation – through cost savings and faster development and testing cycles – all to serve customers better.