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IoT Value in the Connected and Quantified Enterprise


Enterprises and IT leaders understand the value of connectivity. Accordingly, many of these organizations have experienced the daunting task of M2M implementations. Following an M2M deployment, enterprises initially focus on maintenance of the infrastructure, but soon shift their eye towards other projects and activities. Meanwhile, however, M2M is evolving into a greater concept: the Internet of Things. If M2M is a linear, closed system architecture consisting of hardware with a modem, a service plan and a data repository, then IoT is the next logical progression for data and application management. A key difference between these two models is that the goal of M2M is simple data access and connectivity, while IoT’s objectives involve continuous improvements of Product-as-a-Service functionality based on data analysis.

The value of IoT then resides not in the device, but in the end-to-end solution, the data produced, and the actions taken to enhance the value chain and the end-point of consumption. The driver for supporting IoT is the analytic engine at the top of the IoT stack. As such, leading firms look to these in-depth analytics to help achieve corporate objectives.

This heavy reliance on data analytics, however, creates an issue between the enterprise and its individual users regarding the ownership of this data. Creating a strategy that defines and manages ownership of data and applications can determine the success of an IoT adoption. For example, data from an IoT device can include your home zip code, the amount fuel in your home’s oil tank, and other information that marketing entities can use to create targeted offers. Other examples of IoT data sources include:

  •  Automobiles: The use of data-driven IoT within the automobile industry can allow for drivers, manufacturers, and mechanics to predict and manage the health of a vehicle. This allows a mechanic or manufacturer to analyze the data to offer deals on tune-ups and efficiently managing appointments and preferences.
  • Retail: Retailers can utilize geo-fencing technologies to inform consumers of local deals and offers based on their current location. Retailers can also synthesize consumer data for personalized marketing based on a person’s past preferences and shopping frequency to locations in the area.
  • Home: Within a residence, data-driven IoT can support everything from general maintenance of a house, to environmental settings such as heat, lighting, and security measures.

The use of IoT data implies an inherent trust between the consumer and the various third parties. Any breach of this trust severely impacts the brand promise that drives customer lifetime profitability. This is why as hybrid cloud matures, DLP for data/analytics is a requirement for firms to participate in IoT multi-system value chains. IoT is similar to mobility because both require a systems strategy involving end-to-end security of data, communication, and identity across multiple, integrated systems.

For IoT, the enterprises that succeed will have created multi-platform IoT ecosystems designed to keep customers not only engaged, but also spending their budget on a value chain. To ensure the continued success of this symbiotic relationship, data analytics and security measures must evolve to preserve and protect the mutual and trust and accuracy between both consumers and enterprises.