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Making the Last Traffic Jam a Reality

Traffic jams aren’t just stressful—they’re expensive. A recent study by the Centre for Economics and Business Research revealed that in 2013 traffic jams cost the U.S. $124 billion. By 2030, they estimate the annual price of traffic in the U.S. and Europe will soar to $293 billion.

Can we turn this around? I think so. The Last Traffic Jam can happen through the Internet of Everything (IoE) and the increased value that comes from connections between people, process, data, and things. It’s in this highly connected world where we’ll see amazing things happen—including the Last Traffic Jam. Read More »

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Could Big Data and Cloud go together?

In today’s era of increasing connectivity, data is getting generated in vast proportions.  Moreover, it is also important to be able to generate insights from it quickly and act accordingly.  Gone are the days when one would move data into a data warehouse and then extract insights from it to act at a later date.  Here are four scenarios why.

Scenario 1: Cloud and Social

If a discussion around a brand is trending positively or negatively, that organization needs to take action then and cannot wait for a future time to do so. They might want to capitalize on the positive sentiment and amplify it or perhaps take action and remedy a trending negative sentiment. Both Twitter and Facebook provide several real time analytics capabilities leveraging big data technologies that they pioneered themselves.  These analytics run within their cloud environment and provider users real time insights.

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In Retail, Insight Is Currency, and Context Is King

Today’s retailers face a rising tide of change, disruption, and challenges, all driven by technology. As their business landscape is upended, many are struggling to adapt to changing consumer behaviors, competition from disruptive innovators, and exponentially increasing complexity.

The source of much of this disruption is the Internet of Everything (IoE). IoE is the networked connection of people, process, data, and things, and Cisco projects these connections to surge from 13 billion today to 50 billion in the next decade. For retailers, that means a sharp increase in the potential channels, devices, and shopping journeys that are available to consumers. Increasingly, retailers must meet new demands for relevant, efficient, and convenient shopping experiences, whether in-store or out.

IoE_Retail_Figure_Journey_3-2

But for traditional retailers, IoE also presents tremendous opportunities. At the National Retail Federation’s “Big Show” in New York this week, I have seen a great openness to change and innovation. As I see it, traditional retailers are ready to step into the IoE era, but they will need the right ecosystem of partners to guide them through the transformation and help them make the right investments.

To better understand these opportunities and the changing competitive dynamics in retail, Cisco recently undertook a comprehensive, three-pronged study consisting of original research, economic analysis, and interviews with retail industry thought leaders. Released this week, the first wave of primary research findings includes 1240 consumer responses from the United States and the United Kingdom.

A key theme that emerged from the research was that today’s consumers demand new kinds of digital experiences, both in-store and out. In our survey, we presented respondents with 19 concept tests — everything from digital signage and same-day delivery to mobile payments and augmented reality. Above all, we found that shoppers seek a hyper-relevant experience — more so than a hyper-personalized one. In short, efficiency and savings are more important to them than personal engagement.

In our survey, 38 percent of respondents identified greater efficiency in the shopping process (e.g., ensuring items are in stock, speeding checkout times) as the area retailers most need to improve. By contrast, 13 percent sought improvements that would lead to a more personalized shopping experience. Read More »

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How Trinity Leeds is changing the future of retail with IoE

Shopping centres have been around for thousands of years, supposedly starting in Ancient Rome. The basic concept of a shopping centre has not changed much since then; a large building, or multiple buildings connected, which contain a variety of retail stores, services and restaurants. However, we at Land Securities, a commercial property group based in the UK, are changing the way customers experience shopping with our newest shopping centre, Trinity Leeds, “the mall of the future.” Read More »

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How To Gain an Edge by Taking Data Analytics to the Edge

In Part 1 of this blog series, I talked about how data integration provides a critical foundation for capturing actionable insights that generate improved outcomes. Now, in Part 2, I’ll focus on the two other challenges that must be met to extract value from data: 1) automating the collection of data, and 2) analyzing the data to effectively identify business-relevant, actionable insights. This is where things, data, processes, and people come together.

Let’s start with automation.

After IoT data is captured and integrated, organizations must get the data to the right place at the right time (and to the right people) so it can be analyzed. This includes automatically assessing the data to determine whether it needs to be moved to the “center” (a data center or the cloud) or analyzed where it is, at the “edge” of the network (“moving the analytics to the data”). Analytics at the Edge

The edge of the network is essentially the place where data is captured. On the other hand, the “center” of the network refers to offsite locations such as the cloud and remote data centers — places where data is transmitted for offsite storage and processing, usually for traditional reporting purposes. The edge effectively could be anywhere, such as on a manufacturing plant floor, in a retail store, or on a moving vehicle.

In “edge computing,” therefore, applications, data, and services are pushed to the logical extremes of a network — away from the center — to enable analytics knowledge generation and immediate decision-making at the source of the data.

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