All Posts

OK so Enterprise Search is "Janky" - Now what?

Recorded - available as on demand webcast

Search for the enterprise seems to have hit a wall. Bad search is the top complaint of users interacting with their internal data. Meanwhile, there is a seemingly never-ending flood of products, SaaS offerings and new solutions in the market all claiming and attempting to solve the problem.  

In this roundtable, we will define what expectations organizations should really have about their search platforms and discuss what benefits to expect from using techniques like boosting, auto-classification, natural language processing, query expansion, entity extraction and ontologies. We will also explore what will supersede search in the enterprise.

Visit our sponsors' websites: IEEE/IT ProfessionalCMSWire.

view webcast

Seth Earley
Seth Earley
Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.

Recent Posts

[Earley AI Podcast] Episode 31: Kirk Marple

It’s All About the Data Guest: Kirk Marple

[Earley AI Podcast] Episode 30: Alex Babin

The Holy Grail of AI Guest: Alex Babin

The Critical Element of Foundational Architecture

Recently I chaired the Artificial Intelligence Accelerator Institute Conference in San Jose – in the heart of Silicon Valley.  The event has brought together industry innovators from both large and small organizations, providing a wide range of perspectives. For example, the CEO of AI and ML testing startup of Kolena, Mohamed Elgendy and Srujana Kaddevarmuth, Senior Director, Data & ML Engineering, Customer Products, Walmart Global Tech discussed productization of AI solutions and ways to increase adoption. I especially liked the idea of a model catalogue from which data scientists can retrieve data sets and machine learning models that others have built rather than starting from scratch.