From the Internet of Things, a business intelligence bounty
Data from machines and devices is the next business opportunity for your big data analytics initiatives.
Every 60 seconds, 1,820 TB of data are created—and that rate is steadily increasing. While social media and personal mobile devices each contribute to the data glut, another culprit is only now being widely acknowledged for its role in big data: intelligent devices, also referred to as connected products.
Increasingly, businesses are discovering that products and machines embedded with sensors that report data back to the Internet or a corporate network are a major source of not just data volume, but competitive advantage and profitable growth.
“A wide variety of devices and products are transmitting an enormous amount of data over the Internet,” says Jeff Healey, director of product marketing at HP Vertica. Manufacturing equipment, delivery trucks, utility meters, vending machines, cash registers, elevators—the list goes on—are examples of this Internet of Things that use embedded software to distribute data to a public or private network, where it can be analyzed to support a wide range of use cases.
“All this additional data is giving companies more insight into how they use their resources, and allowing them to be more proactive in discovering and resolving problems related to distribution or changes in behavior,” says Martin Arlitt, a senior research scientist at HP Labs.
How “things” make big data
There are three ways, Arlitt says, that sensors and other “things” generate large volumes of data:
- Many endpoints, low velocity—When thousands or millions of sensors are deployed, even a single data point adds up quickly. “Some utility providers have tens of millions of customers,” Arlitt says. “When you multiply 50,000 readings per year by 40 million customers, that’s two trillion readings each year. You need better technology to get actionable data out of that volume.”
- Few endpoints, high velocity—At the other end of the spectrum are situations where relatively few sensors are sending data at extreme frequencies—potentially tens or hundreds of times per second.
- Many endpoints, high velocity—Put both extremes together, of course, and you’re really in trouble if you haven’t figured out how to handle the resulting data. Imagine if electric utilities were receiving power-consumption data from household appliances as they’re being used.
Whether it’s the number of devices, the velocity of distribution, or both, big data is a disruptive force in every industry.
Creating competitive advantage
Businesses can analyze sensor data both to discover new revenue streams and to optimize the efficiency of routine operations. Either strategy can be transformative. Some examples include:
- Usage-based or pay-as-you-drive insurance. Insurance companies are using high-velocity sensor data delivered by customer vehicles to innovate with pay-as-you-drive insurance models. They can also perform predictive modeling on vehicle data to identify lower- and higher-risk customers.
- Energy optimization and predictive maintenance. Businesses with large campuses are using readings from building chillers to optimize energy efficiency and prevent unscheduled downtime.
- Smart drilling. In the oil and gas industry, seismic exploration data is helping companies find profitable locations with less experimental drilling, lowering both operational cost and environmental impact.
- Fleet management. Sensor data from delivery trucks is helping businesses schedule preventive maintenance before mechanical issues can disrupt fleet operations, as well as intelligent route optimization to reduce fuel costs and harmful emissions to the environment.
Planning for the analytics stage
Sensor data is full of potential, but to find the insights that create competitive advantage, you’ll need to plan ahead.
“To get the information you want out of this huge mound of data, you need to interact with it in a very quick way,” Arlitt says.
To get the best chance of success with sensor data, make the following preparations:
- Understand your compliance obligation. If you’ll be collecting sensitive data—for example, patient medical information from medical devices or credit card numbers from a retail device—you’ll need to make sure the proper regulatory processes are heeded.
- Consider your deployment options. A cloud-based analytics platform can be an effective deployment model for organizations that would prefer to outsource the provisioning and maintenance of servers. However, government agencies and financial services are examples of industries where an on-premise deployment will fit better with risk policies.
- Plan out your device deployments to get the results you need today. “You can put out a lot of sensors and collect a lot of data, more than you need, but it can be expensive, particularly if you are using a cellular network for connectivity,” Arlitt says. “And it runs the risk of creating data silos.” A better solution is to start small, adding more features and collecting more data as you go. Analyze that data to prove or disprove your theories and use cases, building your solution in increments as you learn what data you need and don’t need.
As the amount of available data increases, so do opportunities to reduce costs, improve customer satisfaction, and create value-added services. With high-performance, scalable analytics, you can handle these volumes of data—and even automate business processes based on this sensor data.
Think consumable resupply, whereby a printer’s toner cartridge can tell the manufacturer when the ink is low, triggering a reorder that leads to the consumer receiving the cartridges in the mail and being billed appropriately—often at a discounted rate. If you can get lightning-fast intelligence from a smart, scalable platform, the Internet of Things, and big data in general, become assets rather than challenges.
To help align your information management strategy with our device-connected future, listen to the webinar “Unlocking the Massive Potential of Sensor Data and the Internet of Things.”
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