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IT Challenges in the Internet of Everything

- June 13, 2013 - 0 Comments

In a previous post, we looked at examples of new business opportunities enabled by the Internet of Everything and the importance of evaluating Data in Motion in new ways.

Naturally, the Internet of Everything brings its share of IT challenges. Data collection starts at the network edge, including a multitude of endpoint devices and sensors in everyday objects that automatically collect, analyze and transmit data—including video—on a massive scale.

For the most part, it is data that has previously gone untapped—a giant superset of the persistent data that is the subject of Big Data today. The velocity and volume of this data make it difficult to bring it together into one place and extract value from it in a timely fashion. A key IT challenge is deciding what data to store (which can be costly) and what data to ignore (which can be a lost opportunity). 

For example, high-definition video surveillance cameras combined with data analysis offer retailers insight into everything from facial recognition to age, gender and socioeconomic indicators. Retailers can also use video intelligence to create augmented reality mirrors or spot customers in need and send associates to assist them. However, not all the data from these devices needs to be stored or even analyzed, but rather used in the moment to create interactive engagements with the customers.

To address these challenges, intelligence and automated data processing must be embedded in the network. This intelligence takes the guesswork out of selecting the correct data from the torrent, because the network can filter based on relevance. At the same time, it can prioritize what data to retain and what data to discard based on value policies. This requires a flexible infrastructure where compute, storage, network and security resources can be assigned on the fly where and when needed. In most cases with Data in Motion, the application moves to where the data is, not the other way round.

Another key challenge is security, which remains paramount all the way from the edge to the cloud and back. The rapid deployment of Internet of Things and M2M technologies is leading to a proliferation of devices whose variety, data, complexity and vulnerability go beyond the traditional IT landscape. Along with the tremendous value that can be extracted from Data in Motion come new risks that require network-centric security approaches.

The Internet of Everything brings together people, process, data and things to make networked connections more relevant and valuable than ever before, thus providing unprecedented economic opportunity for businesses, individuals and countries. We are still in the early stages of evolution for Data in Motion and the impact it will have on all of us. But it is clear that the more knowledge we have, based on meaningful information pulled from a variety of data sources, the more wisdom we can gain and apply. It will profoundly change the world.


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