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Connected Analytics: Learn to Live on the Edge – and Love It!

Not surprisingly, as a networking company Cisco frequently publishes predictions on the growth of Internet traffic. Bragging unintended, typically the forecasts are pretty accurate. In a 2012 report we predicted that by 2017 there would be 2.5 devices and related connections for every person on earth, while there would be 5 devices and related connections for every Internet user in the same year. In the same report, we also predicted that this burst in hyperconnectivity – including machine to machine connections that are increasingly prevalent with growth of the Internet of Things (IoT) – would create more global network traffic in 2017 alone than in all prior “Internet years” combined.

How correct were our predictions? You don’t have to wait until 2017 for an answer. Welcome to the early arrival of the future of networked communications – a future where the hyper-distribution of information is driving new business demands, and where the old rules of data management and analytics no longer apply. Data is no longer passive. Central stores of stale information aren’t sufficient. Analytics can’t be an afterthought. The new rules require that you live your business daily on the edge of your network, where vital customer and market data is created. And you need to be prepared to respond to what you learn immediately. Are you ready to live on the edge?

The Future is Now . . . Like it or Not

Pervasive connectivity and ubiquitous cloud services have reset user expectations for all types of products and services. A wider and wider variety of connected endpoints combined with mobile and cloud service delivery expands both the kinds and types of data generated by and about users, as well as the devices and the processes that connect them. Data may come from various sources – operations, infrastructure, sensors, etc. Machine intelligence will become better and better, replacing human reasoning in some cases.  And, like humans, machines will develop deeper and deeper insights through continuous learning over time. The good news is that increasingly intelligent machines will free humans for even bigger thinking – and the process will keep repeating itself – machines and humans cooperating for a more intelligent whole.

But the network’s edge ultimately belongs to the end-user. Consumers are well positioned to define and demand a technology experience that meets their specific requirements. Enterprises undergoing digital transformation understand this. Using IT automation, these companies are moving intelligence and analytics to the edge of the network to understand how to benefit from this new perspective.  Put simply, analysis is moving to where the data is generated for instant business insights.

The list of challenges for companies coping with the nature and the speed of digital transformation is a long one. Here are a few of the most critical:

  • The variety of data on the network increases with every new application used
  • High velocity, valuable information from market data, mobile, sensors, clickstream, transactions and other sources requires a new approach to data management
  • Almost universal connectivity has reset user expectations for all types of services
  • Data insights are often perishable and need to be acted on immediately
  • Competitive pressures and increasing customer expectations require that businesses anticipate customer needs, react instantly, and make decisions in real-time

Click image to view a larger version of this graphic

Shape the Edge to Your Requirements

Enterprises of all kinds are responding to these challenges in innovative ways to gain competitive advantage. One example is retailers, which I profiled in an earlier blog on the future of shopping. Merchants understand that the longer a shopper remains in a store the more likely the prospect is to purchase. So, if a retailer can increase a shopper’s “dwell time,” it is more likely to stimulate a purchase.  We’re seeing retailers do this today as they measure where, how and why buyers make decisions on the path to purchase starting at the network’s edge. Through customized applications that permit the retailer to analyze real-time customer engagement with products or in-store displays, the retailer gains immediate insights that let it customize a promotional offer by individual and then push the offer instantly to the consumer’s device. This sort of personalized interaction also creates a better customer experience.

From a service provider’s perspective, knowing the habits of your mobile customers can help it improve service delivery, lower costs and enhance customer loyalty. Again, this knowledge starts at the edge of the network by analyzing continuous feedback on the use habits of mobile subscribers. For example, a service provider can determine unique and new clients, analyze usage by day, week or month, gather active session information to identify network usage patterns or manage promotional programs, determine authenticated vs. unauthenticated associations to identify potential subscribers, or grab information on total data usage to pinpoint network anomalies or usage spikes. These and other edge measurements can then be further analyzed for trends. Automation enables the analysis. The analysis, in turn, creates fast decision-making, which leads to concrete business outcomes.

The Way Forward . . .

If you believe living on the edge is vital to your business, it’s important to have a strategic framework in which to manage your digital transition. First, think of your analytics’ needs in three parts:  1. Real time analysis; 2. Data management; 3. Flexibility of use. Then, demand that the analytics solution you choose to move forward with addresses the requirements for each part as I describe below:

1. Real Time Analysis

  • Real-time trending
  • Dynamic dashboards
  • Predictive analytics integration
  • Continuous queries
  • Event generation

2. Data Management

  • Ability to combine information from network and applications
  • Seamlessly query live and historic data
  • Historic reporting framework

3. Flexibility

  • Analysis of complex queries from fact streams and dimensional data
  • North/south and east/west interfaces for customization
  • Multi-vendor extensibility

Living on the edge of your network doesn’t have to be intimidating. In fact, you’ll come to like the speed at which you’ll find new business insights. Cisco can help in your transformation to a digital business with automation and analytics at its core. I plan to share more on this topic at the Cisco Data and Analytics Conference  on October 20-22 in Chicago. I hope you can join me at that time.

Meanwhile, I’m interested to hear how you feel about the importance of managing and analyzing data at the edge of your network. What are the issues and opportunities that you see?

Please feel free to comment, share and connect with us @CiscoEnterpriseFacebookLinkedIn and the Enterprise Networks Community.

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Apple iOS 8 and MAC Randomization: What It means for Cisco’s Connected Mobile Experiences (CMX) Solution

As you may have read, Apple’s iOS 8 will come with some changes to the way MAC addresses are exposed in Wi-Fi probe requests. Apple’s intent was to provide an additional layer of privacy for consumers and target those companies that offer analytics without providing any value to the end consumer. We’ve been getting some questions about what this means and how it impacts our Connected Mobile Experiences (CMX)  solution, so we wanted to clear this up for our customers.

What does this mean for you? 

First and foremost, Cisco has always been dedicated to privacy for our customers and their end-users. There are four aspects of privacy that are built into our CMX solution:

1. Anonymous Aggregate Information: All analytics are based on aggregate, anonymized location data.

2. Permission-based: Users have to opt-in to join a Wi-Fi network or download an app

3. MAC Address Hash: Users’ MAC addresses can be hashed before exposing to 3rd party apps

4. Opt Out: End-users are always presented with the option to opt out of location-based services

The true value of CMX analytics for organizations is in aggregate location data to be used for business analysis to improve the customer experience for end-users. Providing customers with high performing Wi-Fi not only keeps always-on mobile users happy and opens the doors to delighting customers with more personalized experiences, but also helps provide more granularity to those aggregate trends to feed back into the experience creation machine. Win-win.

What does this mean for our CMX value proposition? Read More »

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Attack Analysis with a Fast Graph

TRAC-tank-vertical_logo-300x243This post is co-authored by Martin Lee, Armin Pelkmann, and Preetham Raghunanda.

Cyber security analysts tend to redundantly perform the same attack queries with different input data. Unfortunately, the search for useful meta-data correlation across proprietary and open source data sets may be laborious and time consuming with relational databases as multiple tables are joined, queried, and the results inevitably take too long to return. Enter the graph database, a fundamentally improved database technology for specific threat analysis functions. Representing information as a graph allows the discovery of associations and connection that are otherwise not immediately apparent.

Within basic security analysis, we represent domains, IP addresses, and DNS information as nodes, and represent the relationships between them as edges connecting the nodes. In the following example, domains A and B are connected through a shared name server and MX record despite being hosted on different servers. Domain C is linked to domain B through a shared host, but has no direct association with domain A.

graph_image_1 This ability to quickly identify domain-host associations brings attention to further network assets that may have been compromised, or assets that will be used in future attacks.

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Angling for Silverlight Exploits

VRT / TRACThis post is co-authored by Andrew Tsonchev, Jaeson Schultz, Alex Chiu, Seth Hanford, Craig Williams, Steven Poulson, and Joel Esler. Special thanks to co-author Brandon Stultz for the exploit reverse engineering. 

Silverlight exploits are the drive-by flavor of the month. Exploit Kit (EK) owners are adding Silverlight to their update releases, and since April 23rd we have observed substantial traffic (often from Malvertising) being driven to Angler instances partially using Silverlight exploits. In fact in this particular Angler campaign, the attack is more specifically targeted at Flash and Silverlight vulnerabilities and though Java is available and an included reference in the original attack landing pages, it’s never triggered.

Rise in Angler Attacks

HTTP requests for a specific Angler Exploit Kit campaign

Exploit Content Type

Angler exploit content types delivered to victims, application/x-gzip (Java) is notably absent


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Big Data in Retailing: Follow the Money!

Retailers looking at the Big Data opportunity may well find themselves with an array of choices: the opportunities seem so vast, where does one begin?

Well, a pragmatic way forward is to focus on some pragmatic possibilities and then “follow the money”!

In examining the Big Data opportunity for retailers, Cisco IBSG has identified three key areas where we believe value can be generated through Big Data analytics – and we have put together a framework for assessing and comparing the financial impact of options within these areas.

As outlined in our previous report, “Surfing the Data Deluge: How Retailers Can Turn Big Data into Big Profits,” three areas – video, social and mobile data –promise unprecedented insights into what consumers want or need, at the earliest stages of interest, and will drive the Big Data thrust in retail over the next few years. These three essentials not only represent a major stream of incoming data, but also provide an outbound mechanism to communicate with customers on a more personalized basis. In other words, they are both a source of Big Data analytics and a way of implementing Big Data insights!
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