The Internet of Things Has Arrived, Let the Games Begin

1 - The Internet of Things Has Arrived, Let the Games Begin
2 - Not Just for Consumers Anymore
3 - It's a Brave New Computing World
4 - What Should I Do With All That Data?
5 - Feeling Insecure
6 - The Edge and the Cloud Duke It Out
7 - Global Dedupe Lends a Hand
8 - Data Classification Moves Front and Center
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The Internet of Things Has Arrived, Let the Games Begin

The Internet of things will bring new challenges to organizations as it will be seen as a complete paradigm shift and require new solutions and ways of thinking.

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Not Just for Consumers Anymore

According to a report by futurist and Stringify CTO Dave Evans, an average of 127 new things are connected to the Internet every second. While IoT has initially focused on consumer applications, industrial IoT is on the way. Clean tech businesses are pulling data from connected windmills, for example, with companies using genomic sensors or aggregating dispersed data for industrial purposes. Even government is getting into the act: The Homeland Security Department announced last year that it's exploring wearable equipment for emergency first responders.

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It's a Brave New Computing World

Behind the emergence of IoT are major structural issues about information governance—especially as apps are moved to the cloud. Connected devices consume and generate information that requires backup, recovery and management. To accomplish this, IoT requires a new, holistic approach to the data residing in endpoints, data centers and the cloud. Yet unresolved questions remain about data processing, backup and intelligence.

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What Should I Do With All That Data?

An enormous challenge of IoT is that many organizations jumping on the bandwagon don't yet realize they are now big data companies that have to process and manage massive data sets, including personal information and passwords. Currently, CTOs allocate engineering resources to this effort, but they'll eventually want ready-made tools to manage workflows. Unless the people and companies driving IoT can get their arms around big data, privacy and security concerns will thwart mass adoption.

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Feeling Insecure

IoT, which often collects information unbeknownst to users, raises a host of privacy, security and liability issues. Yet authors of a report issued by the Institute for Critical Infrastructure Technology (CIT) note that IoT often lacks any form of security, representing "practically an infinite attack surface" for cyber-criminals. The risks are real. According to Marc Rotenberg of the Electronic Privacy Information Center, "If you think you've got a cyber-security problem now, wait for the cold winter day when a hacker halfway around the world turns down the thermostat on 100,000 homes in Washington D.C." As a result, IoT solutions will require security to protect sensitive commercial information and safeguard users' personal data.

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The Edge and the Cloud Duke It Out

Traditionally, data has been collected from endpoints and sent to the home server or data center for processing. Because this approach is ineffective for handling massive data sets, organizations are increasingly using edge computing to maintain and process IoT data locally. At the same time, it's routine for sensor and other IoT-generated data to be moved to the cloud. Since there's little agreement on which approach is best for processing, IoT employs a mix of edge and cloud computing, with data management and protection strategies required for both.

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Global Dedupe Lends a Hand

Global deduplication has proved key for transmitting and managing IoT data. By understanding data patterns that are common to devices, global dedupe transmits only what's uncommon, thereby reducing enterprise bandwidth transmission by about 90 percent. While global dedupe uses the GPU to achieve fast fingerprint processing, its intelligent use of CPU cycles can also help minimize user disruption.

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Data Classification Moves Front and Center

IoT data classification has become a hot topic given its ability to optimize information backups, governance and workflows. By classifying data based on variables of its choosing, organizations can optimize data management, such as highlighting information to be disposed of versus stored and retrieved later on. Data identification is currently a manual process, but this will change as auto-classification evolves over the next few years.

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