Sponsored by Lucidworks
With data eating the world, efficient search is vital — and enterprise search technologies need to keep pace. Join this VB Live event to learn how machine-learning and search combine to boost efficient data discovery and insight, what operationalized AI means, and more.
The wealth of data available today has irrevocably changed the culture of the enterprise and people’s roles within it. The Google search experience, which does such a great job of connecting data to search and findability, has spoiled users, who come into their work roles expecting the same, or better, from their enterprise search.
“Users come into organizations expecting their experiences at using data better, more seamless, and more relevant,” says JP Sherman, enterprise search & findability expert at Red Hat. “Because a corporation tends to have such a large collection of its own data, presumably it would be able to offer an equivalent experience, or at least a sophisticated enough network of connections and insight to present more personalized search and discovery.”
From the business perspective, being able to connect people to the information they’re looking for faster allows people to spend less time searching for information, and use that information to make better business decisions. That results in a savings of time, savings of energy, and a savings of cost in productivity and connection.
“Unfortunately, organizations as a whole generally don’t dedicate time resources or money into site search or search experience,” Sherman says. “There’s a strong perception that it’s hard, that it’s expensive, and unless you’re ecommerce, the ROI isn’t immediately clear.”
There’s also the Google effect — we can’t beat Google, so why are we going to even try? And that leads to bad experiences and, basically, a level of distrust. If a user knows they are generally not going to find what they’re looking for from an organizational infrastructure search, then they’ll probably just skip that step and try to chase down a subject matter expert.
“That takes time, that takes effort, and it’s a barricade in the user’s overall path to insight,” Sherman says. “My argument is you don’t need to figure out everything, like Google does. You just need to understand your data, your employees and your users. You have a smaller universe to control — there’s definitely value in trying.”
AI and machine learning has made that effort to optimize the pathway infinitely easier, and for employees, it has made access to that data far deeper and almost seamless. It adds the ability to detect user intent, so that searches are faster and far more relevant.
“It’s all about connecting people to the information they’re looking for,” Sherman explains. “We’ve developed a way to present that information to them faster. That directly impacts productivity. It affects trust. It affects things like task completion. As a user, if I’m able to find the information I’m looking for to accomplish tasks, I’m going to trust that search experience and trust the organization.”
Then there’s the customer side of AI-powered search. “Red Hat is an enterprise software company and we know people come to our site looking for installations, migrations, downloads, troubleshooting, optimization, and administration,” explains Sherman. “We’ve built a system that is beginning to understand, from the query perspective, what a user is looking for, and we turn that into a task. If they’re looking to deploy an application using our software, without even having the user type in “deploy,” we can see that the intention is most likely about deployment, and so we start biasing specific content.”
Getting enterprise search right is more important than you think — and AI and machine learning has made it more functional, more efficient, and more powerful. To learn more about the way AI and machine learning has transformed search, how companies can seamlessly integrate search that actually meets user expectations, how it can actually drive Opex savings and more, don’t miss this VB Live event.
Don’t miss out!
- What operationalized AI means
- How search and machine learning align to drive efficiency and Opex (operational expenditure) savings
- How search and machine learning can create revenue opportunities
- Success factors for operationalized AI and top lessons learned
- Simon Taylor, Vice President Worldwide Channels & Alliances, Lucidworks
- JP Sherman, Enterprise Search & Findability Expert, Red Hat
- Richard Isaac, CEO, RealDecoy