by Angela Guess
Stephen Baker, CEO of Attivio, recently wrote in InsideBigData, “Enterprise search has been around for more than two decades. And in that time, it’s grown from an ineffective and not-very-user-friendly tool to become a business critical application for enterprise knowledge workers to find the information they need to do their jobs. As enterprise information stores have grown to enormous volumes, search vendors have worked hard to make their solutions fast, accurate, and easy to use – like Google for the enterprise. Enterprise search finds unstructured content housed in file shares like SharePoint and other content management systems, in email archives, and in the content repositories of applications like customer relationship management. The same approach to helping knowledge workers find information parallels a thorny, emerging problem for business analysts: finding data. And as a result, it’s clear that the convergence of BI, Big Data, and enterprise search is fueling the future of analytics.”
Baker goes on, “Enterprise search has benefited from new data processing frameworks like Hive, Pig, HBase, Presto, Impala, Spark, and others. These frameworks can connect structured, semi-structured, and unstructured data to a range of natural language search tools. The notion of search applied to data is very powerful. And if a search solution can combine data with unstructured content, the potential for game-changing insights soars. Data-driven applications can embed ubiquitous search—untethering ordinary business users from the constraints of the expert mindset of data architects and data scientists. And a new generation of enterprise search applications often carry with them a data visualization layer that makes them even easier to use and pushes analytical processing to the data source, which reduces latency.”
photo credit: Flickr/ Julian Partridge