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Creating a connected enterprise: It's not rocket science

This blog post is part of our Essential Guide: Understanding the IoT business environment

The internet of things is more than things — devices and sensors — and their connectivity – internet — with IT Infrastructure. The real definition of IoT starts when these IoT-enabled things reveal critical data complemented with analytics to derive business insights. Thus, a successful connected enterprise can only be achieved when it bundles things, connectivity, data and analytics acting as business accelerators.

Why IoT is more relevant than ever to implement now

First, to put simply, hardware costs are falling and it is pretty easy to build such devices today. Smartwatches and bands that measure human behavior did not exist five years back, but are easily available now. Manufacturing costs of these smart devices and sensors have gone down significantly.

Second, connectivity is everywhere. People stay connected with each other from anywhere with smartphones and mobile devices connected via network. Moreover, these smart devices are compatible with IoT assets making real-time data capture for analytics easier.

Third, IoT support from cloud platforms. Modernized cloud solutions are more sophisticated and cost-friendly. They offer lower-cost, higher-scalability, flexible customization and friendly support for any IoT implementation, which was limited back in 2010. Connected enterprises are leveraging cloud solutions to build a virtual infrastructure, which is both easy and flexible to manage.

Fourth, more advanced software support. IoT suites such as Microsoft’s Azure IoT suite, offer a stack of off-the-shelf services that are easy to customize, on par while billing with AWS and Google Cloud. It offers virtually unlimited scalability and can be interfaced with almost any IoT enabled devices.

Microsoft Azure IoT Suite as a platform to implement IoT and analytics solutions

IoT has evolved since 2010 and it is more complex now with estimated 7 billion smart devices connected over the internet, worldwide. The data is more discrete and highly complex than ever, demanding powerful analytical engines for faster and real-time analysis. Every business domain is now laden with several smart devices and sensors that generate terabytes of data. However, the challenge remains in making proper use of this captured IoT data for critical decision making.

Microsoft Azure starts by building the infrastructure that is already in place. With familiar devices, services and appropriate technology in hand, enterprises can uncover insights from the data generated and make more informed business decisions. Azure IoT Suite starts from where the business is today –whether it is a start-up or ready to scale years of investments in existing IoT scenarios.

IoT assets generate data in different formats, values, retention requirements and traffic patterns across different protocols. In addition, this data is too large and complex and becomes very difficult to process using typical on-premises database management and processing applications. However, Microsoft Azure IoT makes this process simple. It automates the data by providing a framework to ingress and be processed through filters, rules, triggers, etc. It also offers a set of tools, engines and scalable architecture model to process data that evolves as the business evolves. Azure makes the untapped data contextual by combining it with many other assets, sources and datasets.

An IoT implementation use case for energy and utility enterprises

A cloud-based solution for storage and analysis can combine data from multiple sources without worrying about capacity constraints or the significant costs that might result from building out of the on-premises infrastructure.

For instance, in the utility sector, having real-time insights on water consumption proves a holy grail for water corporations and consumers to understand consumption patterns. In such a scenario, what if we devise a machine learning algorithm that can predict ahead of time and alert consumers on over-consumption of water? The use of a trained model, as a part of the machine learning algorithm, can learn from previous water consumption patterns and help customers with push notifications.

For example, a smart meter and energy management solution benefited from IoT by getting real-time visibility of water consumption. It helped in achieve remote asset management and maintenance, implement cost-efficient smart metering and data management, deploy enterprise-wide mobile applications to connect mobile field forces and improve customer experience.

A U.S.-based water utility corporation connected over 500,000 smart water meters that capture 200+ million records with less than a second response time. This IoT implementation provided more than the needed real-time visibility of water consumption, meter health, water leakage/theft and hourly consumption data to the water corporation and its customers. This proved to be a highly cost-efficient and rapid deployment of an internet of things solution for the water meter manufacturing company.

The IoT roadmap for connected enterprises

Enterprises can connect their business model with their ERP, IoT applications, and customer and business data to start getting insights and process information. Not only humans, but even machines can use this information to get insights and automate processes within a business model. Businesses aiming to exploit the true potential of an IoT implementation need to identify which devices they want to connect using Microsoft IoT Suite as well as what business insights they want to analyze and predict when the critical data is available.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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Good Article!! IoT making machines to talk with each other to assure 0% loss
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