IoT Analytics in the Cloud

IoT Analytics in the Cloud

As adoption of the Internet of Things has picked up in recent months, the conversation has turned from “should I do IoT” to “what is the best way to generate business value from IoT?”

And a consensus answer to that second question also seems to be emerging – Analytics for IoT will be one of the fundamental areas for generating business value. Operations managers and business analysts have now turned their attention to implementation of IoT Analytics.

IoT Analytics in the Cloud

One of the first planning questions in considering how to implement IoT Analytics is the question of a cloud vs. premise approach. Security issues around IoT has led many managers to choose already tested premise implementations that can leverage security models that they know already work for their business. Many modest scale implementations can be done quickly in this way and many successes already have come for premise-based IoT applications.

Nevertheless, cloud-based IoT Analytics does offer implementation advantages over premise-based approaches. One of the most important advantages is the rapid integration of 3rd party data feeds and real-time information into larger systems and solutions. Examples of data feeds and external information that are often leveraged in IoT applications include:

  • Weather reports and updates
  • Shipment location and process status
  • Transportation schedules and arrivals
  • Real-time pricing for various commodities in a supply chain

In all these cases, vendors of solutions and web services of these types have already pre-integrated their offerings either directly with various cloud providers or with the standard cloud protocols and data formats.

Another potential advantage of cloud-based IoT Analytics is the basic task of data integration. IoT by its very nature involves unifying multiple sources of data. Many IoT Analytics applications require integration of data from multiple companies – supply chain and manufacturing use cases typically often have this requirement. In these cases, the ease of access and centralization that cloud-based platforms offer often make it simpler and faster to unify the data for further analysis and preparation.

While the Cloud does offer potential advantages for organizations implementing IoT Analytics, there are potential drawbacks and limitations to keep in mind. Application that use real-time data will have strict performance requirements. In cases where that involves large streams of data, companies will need to ensure that their broadband and network systems can meet these demands. Real-time IoT analytics is likely to put demands on communications and networks that may not have been anticipated in previous design cycles.

Finally, it is also important to point out that the question of cloud vs. premise is often best considered as a spectrum of options vs. a pure binary choice. Hybrid cloud options that involve a mix of premise applications and data with external data and services are often a good choice. Companies can pick and choose which parts of their overall application and system are best for cloud vs. premise-based implementation, and plan accordingly. In this way, companies can balance security, performance, integration costs, and overall value.

 

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