|By Fiona McNeill||
|August 21, 2014 07:00 PM EDT||
Connected cars, factory equipment and household products communicating over the Internet is increasingly becoming a reality – one that might soon elicit headlines like “Is the Internet of Things a big bust?”
That’s because it’s one thing to connect a device to the Internet and direct data back to the manufacturer or service provider. It’s another, to derive new information from those data streams. The ability to analyze data in the IoT is critical to designing better products, predicting maintenance issues, and even improving quality of life.
Understanding the Internet of Things
The Internet is no longer just a network of people using computers and smart devices to communicate with each other. In the not too distant future, everything from the factory floor to a city street will be connected to the Internet. Three out of four global business leaders are exploring the economic opportunities created by the Internet of Things (IoT), according to a report from the Economist.
This connectivity has the potential to allow enterprises to create groundbreaking new products and services. An early warning that a piece of equipment is failing faster than expected allows a manufacturer to redesign the equipment, and if needed, get a jumpstart on recalling defective products. This could eliminate many warranty claims, a larger recall and bad press.
A sprinkler system maker could leap ahead of the competition with a system programmed to sense soil dampness and compare that with current weather forecasts to decide whether to turn a sprinkler on. A savvy entrepreneur could market garbage cans that alert municipal staff when the can is nearly full. That information could be used to re-route trucks in the short-term and over time, to optimize sanitation employee schedules.
Whatever the innovation, adding intelligence to the IoT requires advanced analytics. The ability to process and analyze data in real time – as it streams from assets and across the network – is the key to taking advantage of the IoT.
Managing the Complexity Data Streams
An interesting example of IoT potential is already in the early stages of adoption by the auto industry. McKinsey Research suggests IoT technologies could save insurers and car owners $100 billion annually in accident reductions using embedded systems that detect imminent collisions - and then take evasive action. When you break apart what it would take to implement such game changing technology, the role of advanced analytics becomes clear. The data must be understood in real time, but the radar, laser and other sensor data alone isn’t enough to make an intelligent decision for the driver in that split second. It needs to know what is about to happen before it actually does happen. And to do that, it needs models that evaluate the near future scenario, rules that form the decision points of when the model scores are relevant, and prescribe actions based on well-understood patterns and historic scenario analysis. All of that data needs to be analyzed into models that live in the streams so they are assessing real-time conditions and can guide the car away from a pending accident.
Internet-connected sensors that are being embedded in everything from roadways to refrigerators will transmit so much information that it will be meaningless without robust analytics. Consider these examples:
Sensoring and smart meter programs can reduce energy consumption, but only if energy companies have sophisticated forecasting solutions that use the data to quickly reduce expensive last-minute power grid purchases.
Remote patient monitoring can provide convenient access to health care, raise its quality and save money. But if researchers don’t use the data to immediately understand the problem detected by enhanced sensors, added monitoring will simply drive up health costs with no added benefits.
Machine monitoring sensors can diagnose equipment issues and predict asset failure prior to service disruption. When connected to inventory systems, parts would be automatically ordered and field repair team schedules would be optimized across large regions. This only happens, however, if analytics are embedded throughout this process, recognizing an issue trend as it occurs, identifying the rate of asset lifetime depletion, specifying what’s needed from stock and of course, calculating the human resource needs
Analysis in the Internet of Things
Some of the common analytic techniques used today aren’t fast enough to work with IoT data streams. In traditional analysis, data is stored in a repository, tables, etc., and then analyzed. With streaming data, however, the algorithms and decision logic are stored and the data passes through them for analysis. This type of analysis makes it possible to identify and examine patterns of interest as the data is being created – in real time.
Instead of stream it, score it and store it, your organization needs to be able to stream it, score and then decide if you need to store it.
With advanced analytic techniques, data streaming moves beyond monitoring of existing conditions to evaluating future scenarios and examining complex questions - continuously. And because you have up to the fraction of a second information at your fingertips – you consistently know what could happen next, tweaking tactical activities and enriching decision strategies.
To achieve predictive abilities using IoT data, routines and algorithms are coded into software that reads the stream data at the device level or say, in a repository (typically cloud-based). Additionally, data normalization and business rules are also included in the programming, cleansing the stream data and defining the threshold conditions associated with patterns of interest defined for current and future scenarios. In addition to monitoring conditions and thresholds, you can build smart filters into the data streams from the IoT, to decide what should be kept for further analysis to assess likely future events and plan for countless what-if scenarios, or even what to archive vs. what to throw away.
Advanced and high-performance analytics that can work with streaming data are critical to realizing the potential of the Internet of Things. Without it you’ll soon see “Internet of No Thing” headlines on your favorite website.
The Internet of Things (IoT), in all its myriad manifestations, has great potential. Much of that potential comes from the evolving data management and analytic (DMA) technologies and processes that allow us to gain insight from all of the IoT data that can be generated and gathered. This potential may never be met as those data sets are tied to specific industry verticals and single markets, with no clear way to use IoT data and sensor analytics to fulfill the hype being given the IoT today.
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