Smart applications and the power of analytics in the IoT value chain

As we move from an M2M world to the Internet of Things (IoT) we will increasingly see the value of aggregating and analyzing data from various devices – some machines, some personal devices like smartphones. The aggregation and systematic use of these data is one of the M2M-to-IoT transitions we described in a prior blog post entitled Progression from M2M to the Internet of Things: an introductory blog.

According to Wikipedia, “analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.” By using various predictive models and statistical techniques, analytics can help optimize decisions.

In the IoT world, analytics is an amalgam of techniques and technologies to make sense of large reams of data. These analyses allow companies to improve business processes (lower costs) or foster innovation (increase revenues). Some examples follow.

Smart cities – In order to reduce the impact of vehicular traffic on the environment and quality-of-life, cities can use analytics to best route people to and from city centers. Analytics tools can aggregate data from city parking lots, buses, trains, cameras on highways, emergency responder systems and others. With careful algorithms, these analytics tools can provide a series of recommended actions to help alleviate vehicular traffic and thereby abate pollution issues.

Optimizing maintenance processes helps preserving the value of expensive equipment and avoiding costly downtimes.

Optimizing maintenance processes helps preserving the value of expensive equipment and avoiding costly downtimes.

Heavy asset management – In order to best preserve the value of expensive equipment like cranes, dump trucks, front loaders and other construction vehicles, it is best to perform maintenance directly before there is the possibility of equipment failure. However, over-maintenance of vehicles results in wasted resources and excessive non-productive time for the vehicles. Using analytics tools and algorithms, fleet managers can predict when maintenance is best performed on vehicles using in situ sensor data from the vehicles and data from vehicle manufacturers.

Security and surveillance – In order to attempt to prevent violence in public venues – for example, airports – governmental agencies can use facial recognition and other diagnostic software to determine if visitors to the venue are disposed to certain illegal or dangerous actions. Using analytics and complex algorithms, security personnel can better identify possibly nefarious behavior before it occurs. For more information on security and surveillance solutions see this prior blog posting on, What to expect from security and surveillance monitoring solutions in an IoT world.

Various vendors and service providers – from some of the world’s largest software companies to system integrators to smaller services companies – offer analytics tools. There have been numerous acquisitions of companies in the analytics sector over the past years: this highlights the increasing interest in the power of analytics.

In our research we make a distinction between the simple use of M2M applications and a more holistic IoT use of analytics to fuel decision-making for enterprises. In the M2M world, an application generally accesses data from a fairly limited set of sensors. Because very little external data are “mashed-up” from various sources the value of the outputs are isolated around improvements for a single business process. In the IoT world, applications access various sensor data. These sensor data are combined with other data sources. These data are fed into an analytics tool which creates a series of meaningful outcomes: these outcomes are aimed at improving various business processes to both streamline operations and foster innovation.

Share your thoughts about how analytics fits into the IoT value-chain. What value does analytics bring? What are some of the key barriers that prevent analytics from being used in IoT deployments?

Thanks and stay connected for next month’s posting of my series here on Bosch´s IoT blog.

 

About the author

Steve Hilton

Steve Hilton

Steve Hilton is a co-founder and President at MachNation, the leading insight services firm researching Internet of Things (IoT) middleware and platforms. His primary areas of expertise include competitive positioning, marketing media development, cloud services, small and medium businesses and sales channels. Steve serves on Cisco’s IoT World Forum Steering Committee where he is co-chairperson of the Service Provide working group. Steve has 23 years’ experience in technology and communications marketing. Prior to founding MachNation, he built and ran the IoT/M2M and Enterprise practice areas at Analysys Mason. He has also held senior positions at Yankee Group, Lucent Technologies, TDS (Telephone and Data Systems) and Cambridge Strategic Management Group. Steve is a frequent speaker at industry and client events, and publishes articles and blogs in several respected trade journals. He holds a degree in economics from the University of Chicago and a Master’s degree in marketing from Northwestern University’s Kellogg School of Management. Steve is a guest author for the Bosch ConnectedWorld Blog.