Three features characterize 'big data.' Volume, variety, and velocity (1).
Volume: with automation of so many products, electronic sensors, GPS, mobile aps, networked equipment, log files, and social media, more data is being generated than ever before and is creating challenges to traditional architectures in terms of how do store and access it.
Variety: Not only do we still utilize traditional data sources (like customer data bases) but social media and automation also are creating new forms of data that all must be handled uniquely in order to be analyzed in some way to inform strategy and decision making.
Velocity: Because so much of this data is generated real time our time preference for analysis also becomes more present oriented. We may need to get ahead of trends quickly. This calls for tools and architectures that can give us real time analysis.
References and Further Reading:
Big Ag Meets Big Data (Part 1 & Part 2)
1. IDC. "Big Data Analytics: Future Architectures, Skills and Roadmaps for the CIO," September 2011.
2. With Hadoop, Big Data Analytics Challenges Old-School Business Intelligence. Doug Henschen, Information Week
http://www.informationweek.com/software/business-intelligence/with-hadoop-big-data-analytics-challenge/240001922
3. Big Bets On Big Data. Eric Savitz, Forbes. http://www.forbes.com/sites/ciocentral/2012/06/22/big-bets-on-big-data/
Creative Commons Image Attributions:
Volume: with automation of so many products, electronic sensors, GPS, mobile aps, networked equipment, log files, and social media, more data is being generated than ever before and is creating challenges to traditional architectures in terms of how do store and access it.
Variety: Not only do we still utilize traditional data sources (like customer data bases) but social media and automation also are creating new forms of data that all must be handled uniquely in order to be analyzed in some way to inform strategy and decision making.
Velocity: Because so much of this data is generated real time our time preference for analysis also becomes more present oriented. We may need to get ahead of trends quickly. This calls for tools and architectures that can give us real time analysis.
References and Further Reading:
Big Ag Meets Big Data (Part 1 & Part 2)
1. IDC. "Big Data Analytics: Future Architectures, Skills and Roadmaps for the CIO," September 2011.
2. With Hadoop, Big Data Analytics Challenges Old-School Business Intelligence. Doug Henschen, Information Week
http://www.informationweek.com/software/business-intelligence/with-hadoop-big-data-analytics-challenge/240001922
3. Big Bets On Big Data. Eric Savitz, Forbes. http://www.forbes.com/sites/ciocentral/2012/06/22/big-bets-on-big-data/
Creative Commons Image Attributions:
Handheld GPS
By Paul Downey from Berkhamsted, UK (Earthcache De
Slufter Uploaded by Partyzan_XXI)
[CC-BY-2.0 (http://creativecommons.org/licenses/by/2.0)], via Wikimedia Commons
Satellite: NAVSTAR-2 (GPS-2) satellite Source:
http://www.jpl.nasa.gov/images/grace/grace_083002_browse.jpg Status:
PD-USGov-Military-Air Force {{PD-USGov-Military-Air Force}} Category:Satellites
Tractor: bdk [CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0)],
via Wikimedia Commons
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