Here are the influential voices leading the conversations where nonprofits and technology overlap.
As a catch-all term, “big data” can be pretty nebulous, in the same way that the term “cloud” covers diverse technologies. Input data to big data systems could be chatter from social networks, web server logs, traffic flow sensors, satellite imagery, broadcast audio streams, banking transactions, MP3s of rock music, the content of web pages, scans of government documents, GPS trails, telemetry from automobiles, financial market data – the list goes on.
To help clarify matters, the three Vs of volume, velocity and variety are commonly used to characterize different aspects of big data. They’re a helpful lens through which to view and understand the nature of the data and the software platforms available to exploit them.
The benefit gained from the ability to process large amounts of information is the main attraction of big data analytics. Having more data beats out having better models: simple bits of math can be unreasonably effective given large amounts of data. If you could run that forecast taking into account 300 factors rather than six, could you predict demand better?
Volume presents the most immediate challenge to conventional IT structures. It calls for scalable storage and a distributed approach to querying. Many organizations already have large amounts of archived data, perhaps in the form of logs, but not the capacity to process it.
The importance of data’s velocity — the increasing rate at which data flows into an organization — has followed a similar pattern to that of volume. Problems previously restricted to segments of industry are now presenting themselves in a much broader setting. Specialized companies, such as capital markets firms, have long turned to systems that cope with fast-moving data in order to gain advantage.
Rarely does data present itself in a form perfectly ordered and ready for processing. A common theme in big data systems is that the source data is diverse, and doesn’t fall into neat relational structures. It could be text from social networks, image data or a raw feed directly from a sensor source. None of these things come ready for integration into an application.