Get started Bring yourself up to speed with our introductory content.

'Big data' analytics: Tapping into transactional data -- and more

In this video Q&A series, business intelligence experts and IT vendor executives explain "big data" technologies and use cases and give tips on how to get started with big-data analytics.

“Big data” and big-data analytics were big topics of conversation at the Pacific Northwest BI Summit 2011, held in late July in Grants Pass, Ore. Business intelligence (BI) experts and executives from technology vendors discussed the industry confusion around the term big data, agreeing that it refers to much more than simply large volumes of transactional data stored in data warehouses.

Perhaps the biggest big-data challenge, summit attendees said, is how organizations can harness and make use of “other” data: information that historically hasn’t been manageable via traditional relational database approaches. That data includes categories such as Web activity logs and sensor data, and its characteristics typically include a combination of high volume, velocity, variety and variability – the “four V’s,” as Forrester Research Inc. calls them. Gartner Inc. has a similar take on the attributes of big data, substituting “complexity” for “variability.” Transactional data can fit those descriptions as well – but the other forms of big data are often being captured, stored and managed beyond the purview of IT and data warehousing teams, a situation that summit participants said could make it hard for companies to address and utilize the information in an organized way.  

Throughout the summit, the consultants and vendors in attendance shared their insights on new technologies and approaches for managing big data, such as Hadoop, NoSQL databases and other schema-less technologies. But the big question on the table was about use cases – namely, how (and where) can companies get the most value from their big-data sets? In this series of video interviews, you’ll hear more about big-data technologies and use cases and get advice on how to get started with big-data analytics.

Dig Deeper on Big data analytics

Start the conversation

Send me notifications when other members comment.

Please create a username to comment.