Your smart sprinkler system is happily pumping water to your lawn in highly efficient sprays that are “aware” of the soil, the climate, the weather, the time of day, and even whether or not your kids are playing in the backyard on a Saturday. Suddenly, a faulty valve bursts and an uncontrolled geyser erupts. One part of your property is about to be ruined by flooding while the rest of the lawn is left to yellow in the sun.
You and your family are miles away, yet you know all about it. Sensors throughout the system alert your smartphone. At the same time, machine-to-machine signals shut down the pumps, and an expert from the sprinkler company is dispatched to your home with the precise replacement part and the real-time knowledge to fix the system.
It’s a great example of how the Internet of Everything (IoE) may soon funnel precise information in real time to the people — or machines — that need it most. Many of these “remote expert“ technologies are either already here or on the horizon.
At the recent Cisco Live 2013 event in Orlando, I talked about the business value of converging operations technology (OT)—used for industrial automation systems—with IT business networks, in order to create more secure, end-to-end, standard communications and control. Regarding business value of IT/OT convergence for machine builders/integrators and consequently their manufacturing customers, I referenced a case study involving Comau Group that Al Presher from DesignNews recently picked up in a blog entitled “Connectivity Enabling Smart Manufacturing.”
Comau is a leading supplier and partner for most global automakers, integrating welding and assembly lines that coordinate dozens of robots and ancillary automation across multiple stations.
The order-to-engineering sign-off cycle requires months and the consequent build and commissioning to full production adds many more months for a new or refreshed manufacturing line.
Multiple fieldbus protocols at the device level complicate both design and implementation, requiring more integration services—time and money—to make the system work.
By designing a converged IT/OT “Connected Machine” solution that utilizes IP-standards-based, off-the-shelf modularity with a network architecture validated for both business and controls topologies, Comau has been able to reduce engineering cycles and cut integration time by more than two-thirds. Quoting an Engineering Manager from the company, “Installation, commissioning and debugging for 10 stations with 12-15 robots takes a couple days, rather than 1-2 weeks.” Read More »
Naturally, the Internet of Everything brings its share of IT challenges. Data collection starts at the network edge, including a multitude of endpoint devices and sensors in everyday objects that automatically collect, analyze and transmit data—including video—on a massive scale.
For the most part, it is data that has previously gone untapped—a giant superset of the persistent data that is the subject of Big Data today. The velocity and volume of this data make it difficult to bring it together into one place and extract value from it in a timely fashion. A key IT challenge is deciding what data to store (which can be costly) and what data to ignore (which can be a lost opportunity).
For example, high-definition video surveillance cameras combined with data analysis offer retailers insight into everything from facial recognition to age, gender and socioeconomic indicators. Retailers can also use video intelligence to create augmented reality mirrors or spot customers in need and send associates to assist them. However, not all the data from these devices needs to be stored or even analyzed, but rather used in the moment to create interactive engagements with the customers.
To address these challenges, intelligence and automated data processing must be embedded in the network. This intelligence takes the guesswork out of selecting the correct data from the torrent, because the network can filter based on relevance. At the same time, it can prioritize what data to retain and what data to discard based on value policies. This requires a flexible infrastructure where compute, storage, network and security resources can be assigned on the fly where and when needed. In most cases with Data in Motion, the application moves to where the data is, not the other way round.
Another key challenge is security, which remains paramount all the way from the edge to the cloud and back. The rapid deployment of Internet of Things and M2M technologies is leading to a proliferation of devices whose variety, data, complexity and vulnerability go beyond the traditional IT landscape. Along with the tremendous value that can be extracted from Data in Motion come new risks that require network-centric security approaches.
The Internet of Everything brings together people, process, data and things to make networked connections more relevant and valuable than ever before, thus providing unprecedented economic opportunity for businesses, individuals and countries. We are still in the early stages of evolution for Data in Motion and the impact it will have on all of us. But it is clear that the more knowledge we have, based on meaningful information pulled from a variety of data sources, the more wisdom we can gain and apply. It will profoundly change the world.
In a previous post, we discussed the importance of the rising tide of real-time, sensor-generated data—aka Data in Motion—that will gather momentum as the Internet of Everything emerges. Unlocking the potential of Data in Motion cannot be achieved by analyzing stored data or by examining historical data. Rather, it requires tools and interactions that capture value here and now, in real time.
The intelligent network plays a key role here. It can add contextual information such as location, identity and presence while the data is moving. Value can be extracted and acted upon through policy changes, security enforcement and packet processing, as events occur to create advantage here and now, or even to predict the future. By harnessing the value of Data in Motion through the intelligent network, organizations can make better decisions, deliver enhanced experiences to their customers, partners and employees, and build a competitive advantage over the long term.
For example, to maintain and improve patient care in a cost-effective way, healthcare providers can use Machine-to-Machine (M2M) technology to remotely monitor the progress of patients in their homes. Remote monitoring is more efficient and cost effective than having patients repeatedly visit healthcare facilities. As real-time healthcare applications continue to develop, Data in Motion will help patients take more proactive control of their own health, using instant biofeedback to help them modify personal behaviors.
To be clear, Data at Rest is not without value. Indeed, combining it with Data in Motion can produce optimal business outcomes. Data at Rest provides the context for creating the actionable insights from Data in Motion, helping organizations analyze and understand the past while they take contextual action on events in real time.For instance, by tracking a consumer’s real-time location and historical online interaction, a retailer could develop valuable contextual information while enabling store touchpoints with mobile access. With an up-to-the minute view of customers, the retailer could send customized promotions in real time.
And then there’s the opportunity for service providers. For most of them, Data in Motion represents a largely untapped opportunity, despite the wealth of data flowing through their networks. Think of the potential. Their networks and users are constantly generating huge amounts of real-time and near real-time data, packed with details like location, content and subscriber information—much of which can be analyzed and correlated in real-time to create usage and traffic patterns, network congestion analytics, media behavior, dwell times analytics and more. A service provider, for example, could extract detailed data such as a user’s device type, data quota, recent Internet activity and current connection speed. Armed with this real-time intelligence, the provider could offer highly targeted mobile advertising or sponsored data—and charge a premium for it.
Harnessing the potential of Data in Motion creates business opportunities but also new IT challenges. In a next post, we will look at some of these challenges and how to best address them.
Reports of the physical retail store’s death have been greatly exaggerated. As a recent survey from the Cisco® Internet Business Solutions Group (IBSG) found, 93 percent of products sold in the United States are still bought in brick-and-mortar locations. And while technology has upended many product categories and more than a few individual retailers, it simultaneously creates opportunities for retailers to continue to make the store shopping experience both relevant and compelling. Big Data in the store is key to achieving this.