This week learn about open data models, the way that mobile data is driving development and how data has aided IT's paradigm shift. The concept of "effective altruism" is explained and big data perils discussed.
This Information Management post explores IT’s paradigm shifts in the areas of cloud computing, big data, mobility and the Internet of Things. Kapil Bakshi says that these 4 areas will converge to create a new “platform” for enterprises, which will enable them to develop new business and mission capabilities through a more integrated view of IT architecture.
This Chronicle of Philanthropy post explains the small but growing “effective altruism” movement which began a few years ago when young philosophers, mathematicians and economists began chatting online about how they could increase their social impact. “Effective altruists” seek to either increase their income or decrease their spending so they can give more. They then try to make their donations more useful by giving to what they consider the most effective groups working on the most important global problems. Sam Bankman-Fried an active participant in this “effective altruism” movement, describes how this movement can reap benefits for nonprofits that work in the developing world, those focused on animal rights and the future of humanity. However, it is also noted that this could hurt charities that can’t show that they save or improve lives.
The UN Global Pulse has just published its guide ‘Mobile Phone Network Data for Development’, a primer on how analysis of Call Detail Records (CDRs) can provide valuable information for humanitarian and development purposes. It brings together a growing body of research on mobile phone data analysis in development or humanitarian contexts. The document explains three types of indicators that can be extracted through analysis of CDRs (mobility, social interaction and economic activity) and includes research examples and privacy protection considerations.
This post on CNN’s Fortune talks about the errors that can arise when Big Data and the cloud gives supercomputing capabilities to everyone. Citing examples he says that the tools and models we use to interpret and use the information can be flawed and inevitably lead to errors that can have disastrous consequences. While big data is still useful and far from doomed he admonishes against falling in love with the technology available at the expense of realistic success. He says since that the big data models we use are usually not peer tested or reviewed and big data usually exists siloed inside of large corporations this increases the margin of errors.