Chris Talbot recently argued that the semantic web is and will be instrumental to the effective analysis of Big Data. He writes, “As big data stores continue to grow and require additional management, enterprises are faced with the task of managing their explosive data growth while also trying to find the best way to analyze that data. According to the recent InformationWeek ‘Database Discontent’ report, a top item on IT departments’ 2012 to-do list is handling big data in a way that allows for change over time.”

He continues, “As the amount of data available in an enterprise continues to grow, it’s becoming a chore to track, manage and understand that data. Additionally, data is scattered in different places–on-premise storage, cloud-based storage, virtualized systems, desktop and notebook hard drives, and across a growing number of mobile devices (some owned by the enterprise and others owned by the worker). The process and productivity improvements gained through the Web and implementing new and more integrated transactional systems have created a certain level of process entitlement within enterprises.”

He adds, “With the significant depth and breadth of data contained inside and outside the enterprise, in addition to the high volume of transactions that are continually generating more data, there is no reasonable way for people to know where to look when seeking out actionable knowledge, [David] Read said. Predictive analytics will likely outpace reporting and traditional business intelligence efforts in the future, and they will be used to inform SMEs about where to invest their business intelligence efforts, he added.”

Read how the semantic web solves this problem here.

Image: Courtesy Flickr/ Kevin Krejci