Advanced Analytics & Operational Intelligence: A Perfect Marriage?

Operaional Intelligence Use Cases
In today’s fast-paced, data-driven economy, traditional Business Intelligence moves too slowly since it uses historical data to inform future decisions. With the growing volume, variety, and velocity of data and the demand for an enhanced customer experience, enterprises need to rapidly sense and respond to changing business conditions in real-time. Operational Intelligence – the ability to sense and respond quickly to changing business conditions – complements Business Intelligence by taking into account the real-time status of business processes as they occur. Business Intelligence uses traditional approaches, such as data warehouses or relational database management systems,

The Analytics Value Chain –The Key to Delivering Value in IoT?

Analytics Value Chain
In our last blog, “Can a Traditional Analytics Approach Empower Business Operations In The IoT?” we asked if a traditional analytics approach is capable of supporting business operation for the Internet of Things (IoT). Our conclusion, based on customer requirements, was that traditional approaches are not sufficient and that new approaches are needed to handle the time critical nature of IoT challenges

The Foundational Steps

The first step in designing a new approach is to simplify the process by integrating all the data for an IoT application. That includes all the

Can a traditional analytics approach empower business operations in the IoT?

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The Internet of Things (IoT) is about digitization of physical assets encompassing sensors, devices, machines, gateways and the network. This creates possibilities of significant value creation and revenue generating business models through data democratization across IoT networks. However, digitization is not simple. Connecting devices or things to the network and to the cloud will not only drive complexity but also scale at unprecedented levels. Customers can only realize the value of such complex networks through the application of advanced data analytics to extract timely insights and actions from ‘dark’ data. Let’s look at how

IoT Evolution or Revolution? – Making the Thing Nervous System a Reality

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The Internet of Things (IoT) could not be a bigger "buzz phrase" at this point. Even more than past technology hype cycles, this one requires the discerning business person to filter through the noise and develop a clear view of how this technology wave will affect their business. It is useful to take a look at IoT within the larger context of previous waves of information and communications technology innovations. Typical vendors in the business to business (B2B) market have been focusing on technology advancements and generic productivity arguments. We call this a technology looking

Power of Fast Analytics over Slow Data

Fast analytics over fast data makes sense intuitively. How about fast analytics over slow data? It sounds like an oxymoron, doesn’t it? Before we answer this, let’s look at what fast data, slow data and fast analytics mean. Fast data is like a high-velocity stream coming out of a fire hose. Sensor data, financial ticker data, and click-stream data are a few examples of fast data, where large volumes of data come into play at high velocity. The “data cycle” refers to how frequently data gets updated and reported. So, basically, a slower data cycle means

Streaming Analytics Value Chain: Multi-Stage Stream Processing Drives Value in IoT

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Let us look at one use case – preventing and mitigating power outages on the Smart Grid due to severe weather (e.g., the recent Northeast blizzard). What if we could monitor the weather events and the electrical equipment, and predict in real-time when a piece of equipment was about to fail? We could then take defensive measures to prevent the failure or lessen its impact. In the extreme, if a catastrophic failure is predicted, then electric service could be automatically shut down to protect the equipment. This is exactly what we can do with

M2M Conference Keynote: The Next Big Thing in the Internet of Things

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This week in Miami, FL, our CTO & Co-Founder, Dr. Dale Skeen, was keynoting at the M2M Evolution Conference. Machine-to-machine (M2M) communications is a subset of the Internet of Things (IoT) and a market of markets, where each of the markets is growing exponentially. The title of Dale’s keynote was “The Next Big Thing in the Internet of Things: Real-Time Big Data Analytics,” but essentially his keynote was about “Real-Time Analytics” and its importance to IoT, and why you should care. Real-Time Analytics over enormous

Real-Time Big Data Analytics + Internet of Things (IoT) = Value Creation

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Vitria recently administered an online survey on the topic of Real-Time Big Data Analytics as it pertains to the Internet of Things (IoT) and were pleased to have the chance to collect data from leading industry experts and learn about the challenges that companies are currently facing in the field. According to the survey, executives across the consumer, enterprise, and industrial sectors see enormous opportunity with real-time Big Data analytics to drive business outcomes for their IoT initiatives. In many cases, executives are already deploying or planning to deploy first-generation real-time analytics for IoT within

Real-Time Cross-Channel Customer Engagement (part 4) – Combining Streaming and Predictive Analytics

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In the first part of this 4-part blog series, we presented a business use case wherein retailers are able to engage customers at the right moment armed with insights in real-time (to the second or minute) supported with accurately predicted individual customer preferences. We also gave an overview of relevant technologies that not only help to solve individual disparate problems, but also have to come together and act in unison. In the second part, we discussed the details of a Streaming Analytics platform, which is leveraged for behavioral analytics on dynamic tracking data

Real-Time Cross-Channel Customer Engagement (part 3) – the Predictive Analytics

Cross Channel Engagement Real Time Predictive Analytics
In the first part of this 4-part blog series, we presented a business use case wherein retailers are able to engage customers at the right moment armed with insights in real-time (to the second or minute) and supported with accurately predicted individual customer preferences. We also gave an overview of relevant technologies that not only have to solve individual disparate problems, but also have to come together and act in unison. In the second part, we discussed the details of the Streaming Analytics Engine, which is leveraged for behavioral analytics on the

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