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The Advice Advantage: How Banks Can Close the ‘Value Gap’ and Regain Customer Trust

Today’s banking consumers are used to experiences that reflect their likes, dislikes, past histories, and even their future plans. But not always from their banks. These kinds of interactions are more common when buying an online book, streaming a movie, or planning a vacation. Despite numerous omnichannel initiatives, many banks continue to lag in providing contextual, relevant, and convenient experiences to their customers. And while many customers yearn for personalized financial guidance, a Cisco survey of 7,200 smartphone users and bank customers in 12 countries found that for too many bank customers, the choice is between no advice, or what they perceive to be generic advice delivered inconveniently.

As a result, bank customers often try to attain their most important financial goals on their own, via “friends” on social media, or from non-traditional providers of financial services. Moreover, since the financial crisis of 2007-2008, banks’ brand equity has fallen. Read More »

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Fiber. Great In Your Diet. Costly In Your Data Center.

Guest Blog by Pat Chou, TMG Product Manager

Pat ChouSince the introduction of the Cisco’s 40G QSFP BiDi (Quad Small Form-Factor Pluggable Bidirectional module), we’ve seen phenomenal growth and adoption. I guess people do see the benefit.

What’s that? You haven’t heard?

…Ok, let’s say you’re in charge of a data center and your boss reminds you that streaming video and IoT devices are all the rage and if you don’t keep up with bandwidth demand, you’re toast. You have 10G links that use 10G SFP+ SR transceivers at the aggregation layer. You upgrade your switches or linecards to ones that have 40G QSFP ports like the Nexus series switches. Read More »

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Fog Computing: The New Model for the IoE

Last month, we proudly announced Connected Analytics for the Internet of Everything (IoE), easy-to-deploy software packages that bring analytics to data regardless of its location. It is a continued part of our commitment to delivering on our vision for fog computing, also called edge computing, a model that does not require the movement of data back to a centralized location for processing. If you’ve been reading my blog, you’ve seen me write about this as the concept of ‘Analytics 3.0’ or the ability to do analytics in a widely distributed manner, at the edge of the network and on streaming data. This capability is unique to Cisco and critical for deriving real-time insights in the IoE era.

To perform analytics using a traditional computing method, once data is generated it is aggregated, moved and stored into a central repository, such as a data lake or enterprise data warehouse, so it can be analyzed for insight. In the IoE, data is massive, messy, and everywhere – spanning many centralized data repositories in multiple clouds, and data warehouses. Increasingly, data is also being created in massive volume in a very distributed way…from sensors on offshore oil rigs, ships at sea, airplanes in flight, and machines on factory floors. In this new world, there are many problems that arise with the traditional method – not only is it expensive and time consuming to move all of this data to a central place, but critical data can also lose its real-time value in the process. In fact, many companies have stopped moving all of their data into a central repository and accepted the fact that data will live in multiple places.

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Analytics 3.0 creates a more appropriate model, where the path to derive insight is different by combining traditional centralized data storage and analysis with data management and analytics that happen at the edge of the network…much closer to where the huge volume of new data is being created. Analytics involves complicated statistical models and software, but the concept is simple…using software to look for patterns in data, so you can make better decisions.  It makes sense then to have this software close to where data is created, so you can find those patterns more quickly…and that’s the key concept behind Analytics 3.0. Once it’s analyzed, we can make more intelligent decisions about what data should be stored, moved or discarded. This model gives us the opportunity to get to the ‘interesting data’ quicker and also alleviates the costs of storing and moving the ‘non-interesting data.’

Analytics 3.0 is not about replacing big data analytics, cloud analytics and other centralized analytics.  Those elements are all part of Analytics 3.0, but they are not sufficient to handle the volume of massively distributed data created in the IoE, and so they must be augmented with the ability to process and analyze data closer to where it is created. By combining centralized data sources with streaming data at the edge, you will look for and find new patterns in your data. Those patterns will help you make better decisions about growing your business, optimizing your operations or better serving your customers…and that is the power of Analytics for the IoE.

 

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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 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.

Read More »

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