Operational Intelligence and Monetizing the Internet of Things
|David Brinker in Enterprise Monday, August 18, 2014|
The Internet of Things (IoT) has moved from clever idea to the cusp of realization. Billions of machine sensors, accessories and devices such as mobile phones, are poised to be networked for the IoT – with 26 billion installed units by 2020, according to Gartner. The conversation surrounding the IoT has progressed from devices to storage and now to the technologies that will allow enterprises to obtain insights on the data the IoT will generate.
When it comes to analyzing the IoT, “real-time analytics” is the term that often gets used these days. However while the analysis may be fast, the datasets themselves are usually static. It might be last month’s manufacturing floor machine logs or last week’s stocking data. While the fast analysis of static data helps identify important data patterns and long term trends, it leaves a critical gap between the identification of a pattern and the use of that intelligence to capture business opportunities in the moment.
The real challenge created by the IoT is to apply real-time analytics to live, continuously-updated data with extremely low latency. Known as “operational intelligence,” this concept has been around for years, but until now the technology to realize it has lagged. Operational intelligence continuously analyzes live data and maintains a dynamic model of a real-world system.
Unlike conventional approaches, which analyze only streaming data or only static data, this technique integrates live data and historical information to provide a more complete picture of IoT activity. This enables a deeper understanding of customer needs and business opportunities and provides timely, effective feedback.
Although implementing operational intelligence introduces significant challenges in handling large volumes of data with fast response time and high availability, it creates amazing new possibilities for the IoT – the kind of possibilities that transform industries.
Here are some examples followed by a peek at the technology that makes operational intelligence possible.
Giving Brick-and-Mortar Retailers an Advantage with Mobile
The growth and maturity of e-commerce poses an increasing threat to traditional brick-and-mortar retailers. While many consumers prefer the experience of picking out items in a store, the convenience of online shopping is undeniable. According to ShopperTrak, during the 2013 holiday shopping season U.S. retailers saw approximately half the foot -traffic than just three years prior.
Currently, brick and mortar companies are looking at operational intelligence as a strategy for combining and analyzing historical data and mobile device data in real time to level the playing field with online retail giants. Mobile phones paired with operational intelligence can be used to identify individual customer needs in real time, allowing for an incredibly customized shopping experience.
When consumers walk into a store, they can opt-in to an enhanced shopping experience that integrates their location and preferences with their shopping history. Operational intelligence empowers sales associates to make personalized recommendations for the consumer to better match needs, thereby combining the personalization of online shopping with the tactile experience of in-store shopping.
Moreover, this technology enables the store to track inventory with high efficiency using RFID tags, reducing the stock that needs to be kept on hand and ensuring immediate access to requested items. By offering the shopper highly personalized and relevant offers and managing inventory with new levels of efficiency, operational intelligence gives the brick and mortar retailer a distinct competitive edge.
Making Cable TV Smarter
The spread of cable TV has changed lifestyles by providing an explosive number of entertainment options for the viewer. In fact, there are so many choices that customers sometimes have difficulty in making a selection, but at the same time have a healthy appetite for even more options.
Telecommunications providers understand these challenges and are beginning to use operational intelligence to help viewers identify new entertainment options personalized to their tastes. For example, by applying operational intelligence to data streamed from set-top boxes combined with a viewer’s historical viewing patterns, the customer can be alerted to an upcoming program featuring a favorite sports team or to product offers that match both the current show and the viewer’s profile.
The cable provider also can monitor and analyze set-top box data to quickly identify and address issues with viewing quality or network speed while displaying messages to the viewer if needed and quickly dispatching a service representative. These new capabilities both enhance the customer’s experience and offer upsell opportunities for the provider, while lowering operating costs.
The Technology Behind Operational Intelligence
Companies have been analyzing their historical data for years to create business intelligence, that is, to identify patterns and trends hidden in the data. They have learned about their customers’ preferences and behavior and then applied this knowledge to improving their product and service offerings. Operational intelligence enables companies to take the next step so that data analytics can be carried out and acted on in the moment. The value of operational intelligence is clear, but what does it take to make it a reality?
Operational intelligence for the IoT requires a computing environment that can store, rapidly update, and continuously analyze data representing a large population of real-world entities, such as devices, inventory items, and customers. The secret sauce that makes this possible is a technology called “in-memory computing.”
This technology has been used for more than a decade to store and update large, fast-changing data sets. More recently, scalable, in-memory computing capabilities have been introduced to give this computing platform the horsepower needed to rapidly analyze live data and provide immediate feedback. For example, this scalable computing technology can analyze a terabyte of continuously changing data in a few seconds or less.
Unlike analytics platforms designed to analyze static data in a back office, in-memory computing platforms can be integrated directly into live systems, reliably tracking and analyzing large populations of fast-changing data. When designed to meet the stringent needs for high availability in commercial environments, these platforms enable companies to obtain the operational intelligence they need to manage the IoT.
In-memory computing products with integrated, continuous high availability, such as ScaleOut Software’s ScaleOut Analytics Server, are specifically targeted for use in live, mission-critical systems.
With the coming of the IoT and the continuous influx of devices being networked for the enterprise, data is being generated at an unprecedented rate. Operational intelligence enables enterprises to capitalize on this trend by leveraging the fast-changing data created by the IoT to enhance the customer experience, optimize business processes, and grow profits. With their scalable performance and integrated high availability, in-memory computing platforms now open the door to operational intelligence for systems which manage the IoT.
Read more: http://www.scaleoutsoftware.com/
This content is made possible by a guest author, or sponsor; it is not written by and does not necessarily reflect the views of App Developer Magazine's editorial staff.
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