Sponsored by Lucidworks
Your employees have a full spectrum of content and data sourced both within and external to the enterprise. And enterprise search technologies need to keep pace and stay robust. Join this VB Live event to learn how machine-learning and search can drive efficiency and Opex savings.
The internet, Google, and Amazon aren’t just useful consumer tools; they changed the game in terms of what kind of data users expect to have at their fingertips, and how quickly that data can be accessed.
At home, the expectation is that any search will return the right results, even if you have to do some scrolling. At work, internal search is not keeping up, and that means lost money in terms of employee efficiency, employee effectiveness, and lost opportunities.
Employees can spend up to 30 percent of their working life looking for information. The search apps originally embedded in enterprise products relied on structured data — client server solutions that could handle easily indexed data like file and web servers. Fortunately for business, but unfortunately for these technologies, the volume of data has exploded, and user expectations have leapt forward into the future.
AI-powered enterprise search, or cognitive search, is a major evolution, eliminating the traditional scavenger hunts needed to surface the information you need, or having employees spend a chunk of their day emailing links or bookmarks to one another. It augments employees’ intelligence, helping them make smarter decisions and complete tasks faster when it’s integrated into your employee’s digital workspace, letting them access their search capabilities on any device, from any location.
A cognitive search platform connects to your data resources and organizes it so it can be found by queries in a way that’s much more sophisticated than the traditional keyword search engine. Cognitive search combines indexing with text analytics, and AI technologies to actually parse the data, and to boost the relevancy and completeness of results, not just establish data connections. The better the cognitive search platform understands the query, the better the results will be. It will return information to them — not just documents.
With AI, it can interpret inquiries based on intent, parsing the text of the query, the context, or which application your employee is working on, and even taking into account what it begins to learn about the user. In other words, personalized results keyed to your employee’s own context and role as well.
Of course, cognitive search use cases vary widely, and each needs to be customized according to the data you’re feeding it. It’s also not entirely hands-off, relying on human intelligence to tune and update it in order to stay up-to-date and boost relevancy.
Your employee’s search needs may also evolve over time, or the number of apps users require in their digital workspaces may proliferate or get more sophisticated. That means you’ll also need to ensure the integrated search embedded in your applications can scale along with them, and have enough flexibility in order to be customized for every context.
To learn more about enterprise, or cognitive search, how it can dramatically improve your employee experience from your army of knowledge workers to your leadership and C-suite, the benefits of operationalized AI 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