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.
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.
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.
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 »
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”).
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.
Over the past few weeks, I’ve shared how we are helping our customers address one of their toughest challenges brought on by the Internet of Everything (IoE), Big Data and hybrid IT environments: effective management of the massive amounts of data, types of data and in various locations. With solutions like Data Virtualization , Big Data Warehouse Expansion and Cisco Tidal Enterprise Scheduler, we give our customers the tools to address this challenge head on.
Once you have access to all of your data…what next? The second challenge is to extract real-time valuable information from data in order to make better business decisions. As I’ve said before, more data is only a good thing if you use that data to better respond to opportunities and potential threats. Our customers certainly understand this and, in a recent Cisco study, 40% of surveyed companies identified effectively capturing, storing and analyzing data generated by connected “things” (e.g., machines, devices, equipment) as the biggest challenge to realizing the value of IoT.
The majority of data analysis has historically been performed after moving all data into a centralized repository, but digital enterprises will have so many connections creating so much widely distributed data that moving it all to a central place for analysis will no longer be the optimal approach. For insights needed in real-time, or data sets that are too large to move, the ability to perform analytics at the edge will be a new capability that must be incorporated into any comprehensive analytics strategy.
Analytics 1.0 was all about structured data, in centralized data repositories. Analytics 2.0 added unstructured data and gave rise to Big Data. Analytics 3.0 will require all of those existing capabilities but will also require data management and analytics capabilities closer to where the data is created…at the edge of the network.
With this new approach in mind, today we announced Connected Analytics for IoE, packaged, network-enriched analytics that leverage Cisco technologies and data to extract real-time valuable information called:
Optimize the fan experience -- Connected Analytics for Events monitors Wi-Fi, device and application usage along with social media to deliver insights on fan engagement and business operations.
Improve store operations and customer service -- Connected Analytics for Retail supports analysis of metrics, including customer and operational data in retail environments, to help stores take new steps to assure customer satisfaction and store performance.
Enhance service quality, customer experience and unveil opportunities for new business -- Connected Analytics for Service Providers provides near real-time operational and customer intelligence from patterns in networks, operations, and customer system data.
Understand how to get the most out of your IT assets -- Connected Analytics for IT provides advanced data management, data governance, business intelligence and insights to help align and get the most out of IT capabilities and services.
Reveal hidden patterns impacting network deployment and optimization -- Connected Analytics for Network Deployment analyzes devices, software, and features for inconsistencies that disrupt network operations and provides visualizations and actionable recommendations to prioritize network planning and optimization activities.
Understand customer patterns in order to meet quality expectations and uncover monetization strategies -- Connected Analytics for Mobility analyzes mobile networks to provide network, operations and business insights for pro-active governance to Wi-Fi solution customers.
Gain a holistic view of customers across data silos -- Cisco Connected Analytics for Contact Center delivers actionable customer intelligence to impact behaviors and outcomes during the critical window of customer decision making. Having the right offer at the right time will drive market leadership.
Measure the impact of collaboration in comparison with best practices -- Cisco Connected Analytics for Collaboration measures the adoption of collaboration technologies internally. It leverages data collection using the Unified Communications Audit Tool, from sources such as WebEx, IP Phones, Video, Email and Jabber.
The portfolio also includes Cisco Connected Streaming Analytics, a scalable, real-time platform that combines quick and easy network data collection from a variety of sources with one of the fastest streaming analytics engines in the industry.
In the world of IoE, data is massive, messy, and everywhere, spanning many sources – cloud, data warehouses, devices – and formats – video, voice, text, and images. The power of an intelligent infrastructure is what brings all of this data together, regardless of its location or type. That is the Cisco difference.
Cisco today announced a data and analytics strategy and a suite of analytics software that will enable customers to translate their data into actionable business insight regardless of where the data resides.
With the number of connected devices projected to grow from 10 billion today to 50 billion by 2020, the flood tide of new data — widely distributed and often unstructured — is disrupting traditional data management and analytics. Traditionally most organizations created data inside their own four walls and saved it in a centralized repository. This made it easy to analyze the data and extract valuable information to make better business decisions.
But the arrival of the Internet of Everything (IoE) — the hyper-connection of people, process, data, and things – is quickly changing all that. The amount of data is huge. It’s coming from widely disparate sources (like mobile devices, sensors, or remote routers), and much of that data is being created at the edge. Organizations can now get data from everywhere — from every device and at any time — to answer questions about their markets and customers that they never could before. But IT managers and key decision makers are struggling to find the useful business nuggets from this mountain of data.
As an example, take the typical offshore oil rig, which generates up to 2 terabytes of data per day. The majority of this data is time sensitive to both production and safety. Yet it can take up to 12 days to move a single day’s worth of data from its source at the network edge back to the data center or cloud. This means that analytics at the edge are critical to knowing what’s going on when it’s happening now, not almost 2 weeks later.