Cisco Builds One Network for the Modern Grid - Cisco Certifications

Utilities are transforming. In working with over 200 utilities worldwide, we find that they are facing three key business imperatives – securely improving grid reliability and efficiency, integrating distributed energy resources (like wind and solar), and identifying new revenue opportunities.

Take our customers Enedis, the primary energy distributor in France, who wants to improve their operational efficiency by modernizing 750K secondary substations and innogy, Germany’s leading energy company, who wants to eliminate their CO2 footprint by completely transitioning to renewables.

To realize their business priorities and transform with the changing nature of the electricity business, customers like Enedis and innogy are beginning with – the foundation of a secure and reliable communications network.

And who can best provide that than the global technology leader – Cisco. Over 50,000 customers deploy Cisco’s secure communication technologies for Operational Technology (OT) use cases across a broad range of industries – from utilities and manufacturing to transportation and smart cities.

We continue to innovate for our utility customers, and I am excited to share our latest announcements –Cisco’s next-generation Industrial Routers and Catalyst Industrial Ethernet switches; enhancements to Cisco resilient mesh network now with Wi-SUN 1.0 support; new Cisco Validated Designs – blueprints for distribution and substation automation; and a developer eco-system extending Cisco platforms.

Innovations like these are enabling our customers to build one utility network for their entire grid with unmatched flexibility, scale and security.

Multi-layered grid security

A modern, connected grid has a greater threat surface – making security a top concern for OT and IT alike. But securing the grid is not easy. It is a complex, multi-dimensional problem, ranging from physical security to cyber security.

At Cisco, we have a multi-layered security approach. We integrate security into all of our network layers – the hardware, operating system, and edge computing apps – eliminating security gaps. And our grid security solutions combine industry-leading cyber security with video surveillance to monitor physical security of remote locations – ensuring a comprehensive view.

Connecting their grids through Cisco’s secure network that is based on global security best practices like NERC CIP, empowers our customers to detect and contain threats in real time. 

Flexible deployment at scale

Cisco networks are built for flexibility. One of the largest utilities in the United States is optimizing the cost and performance of their distribution automation system by integrating multiple access technologies into a single operational network. Using Cisco IR800 series Integrated Services Routers (in areas with cellular coverage) and Cisco IR500 series and Cisco Resilient mesh (in areas without cellular coverage), they are building a unified network managed through a single pane of glass using Cisco IoT Field Network Director. And both our next-generation routers and switches have a flexible and modular design. In the future, as technologies like 5G get deployed, customers can replace a module and retain the rest of their investments.

We find that complex, large-scale utility problems cannot be solved by one vendor alone. That is why, we partnered with industry leaders to provide proven solutions and blueprints for successful deployments across six key utility use cases – Distribution Automation, Substation Automation, Utility WAN, Advanced Metering Infrastructure (AMI), Grid Security, and Mobile Workforce.

Last week we announced a new Developer Center (with free resources and tools) on Cisco DevNet for – ISVs, partners and customers. Now over 500,000 developers can build edge computing and security apps that can solve the utility challenges of today and tomorrow.

Take Eximprod, our ISV partner who has developed a Virtual RTU that runs as an IOx app on the Cisco IR809 Integrated Services Router. With Cisco’s free tools like the sandbox, utilities can test Eximprod’s app before deployment or develop their own innovative capabilities.

Now, with Cisco’s flexible and scalable network and management platforms, customers can manage their entire grid and mobile workforce with ease.

New revenues beyond the grid

On top of their modern grid, utility customers are building new services for smart cities, like street lighting, waste management, electric vehicle charging and more.

As utilities and cities partner, they need a multi-purpose network that is standards-based and interoperable – the very basis of the Wi-SUN Alliance. As the founding member of this alliance, I am proud to say we have introduced the first product certified to the alliance’s recently released FAN certification program – the IR509 Industrial Router.

By extending Cisco’s trusted network to their new smart services, utilities can leverage familiar technologies to easily connect and manage their entire business – from the grid to the city. CIMCON Lighting is one such partner, who combines Cisco’s resilient mesh network with their lighting management systems to deliver smart city lighting.

Additionally, Cisco Kinetic for Cities, a cloud-based smart city data aggregation platform, enables our customers to easily create new service offerings across six different domains including – street lighting, waste management and environmental monitoring.

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Helping the Law Capture Every Call - Cisco Certifications

Admit it. Sometimes you let your calls go to voicemail. It’s not a crime to keep that pollster waiting or call that relative back later with a proper defense as to why no thank-you note.

On the other hand, if you work in law enforcement, or are an elected official – or both, as was the case with a recent district attorney’s office we worked with – calls that come in can’t be missed. The call could be routine, congratulatory, or someone asking for or offering help. Or, the caller could be filing a complaint, confessing something, or making a threat. These aren’t your relatives (hopefully); you’ve got to keep track.

Imagicle says…

IT managers at a district attorney’s office asked us for a reliable call recording solution that would record, track, categorize and archive every incoming and outgoing call. They needed something cost-effective, user-friendly, and that would work not only in their jurisdiction, but also in their offices all across the state.

Their existing infrastructure, built around the Cisco BE 7000 product line, was a good fit for Imagicle’s latest centralized Call Recording product. We installed Call Recording with a reliable HA (High Availability) active-active cluster, which comes with two recording options: Always On and on-Demand.

By opting-in to record every call, district attorney’s office is now able to better manage complaints, track offensive or threatening language, and provide evidence of how it handles cases. And for further security, only authorized users are able to access the system for audit trails. If Call Recording would be a good fit for you, call us. We promise to pick up.

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Machine Learning – using DataRobot to scale humans - Cisco Certifications

AI promises us a world in which cars can predict when to swerve, to avoid hitting mothers crossing the street with their babies. It promises us refrigerators that can predict what food to order before you run out. It promises us networks that can predict when to heal themselves before they become overloaded. AI promises us a world, underpinned by predictions based on data, in which our machines will know what we want and need before we ourselves do.

In order to realize that vision, we need to address the very real hurdles that stand in the way of operationalizing predictive analytics, which are empowered by Machine Learning (ML). ML is software that learns by example, ingesting data and tuning algorithms to predict likely outcomes, using a variety of use-case specific input variables For example, if you’ve ordered eggs at 6 p.m., every Thursday over the past 12 months, ML will tell us that you’ll likely want to order eggs again next Thursday, at 6 p.m.

Why don’t we use more data to drive better decisions? We face a major problem today in that machines and software scale well, but humans do not. Our data has grown at a much faster pace than our population of data scientists, who represent a relatively small and high-demand subset of today’s workforce. According to a recent 451 Research survey, 36 percent of companies cited lack of skilled workers as the most significant barrier to deploying machine learning.(1)

Why is ML limited largely to skilled workers?

Machine learning is operationally hard. Data science projects in general require a lot of manual input. In spite of great strides towards easier data management, data today is still relatively messy (disorganized, incomplete, error prone), slow (batch-oriented, vs. real-time), and heavy (difficult to move).

If you were to launch a machine learning project today, you’d probably first collect data from a variety of data sources (databases with different organizational schemes, data from legacy systems, etc.). Then you’d likely clean that data as a next step (correct for incompleteness, errors, match variables that may comprise the same information but that are labeled differently – e.g., “phone number” vs. “mobile #”). As your third step, you might choose and apply a learning algorithm to your data, in order to ultimately produce a predictive model for whatever you’re hoping to forecast. The final step is to deploy the model and then monitor it so when accuracy degrades over time (typically as the business changes), the model can be refreshed and redeployed.

On your first try, you might end up with a predictive model that works.

Or, you might end up with a predictive model that fails to offer a representative view of the relationship between your input variables and predictive output.

Or, you might end up with a predictive model that perfectly fits the data to your sample, historic data, but fails in the real world, with a larger and more current data set.

So what would you do? You could go back to your data and increase or decrease the number of input variables. You could choose a different learning algorithm to generate your predictive model. You could choose a different set of data from a different set of sources. You could change your threshold of acceptable accuracy for your model (maybe acceptable for when your refrigerator needs to order eggs for you, but probably not acceptable for your car to decide when not to hit someone).

Bottom line, ML is a difficult and particularly iterative problem to solve.

How could we empower less technical users to put ML into practice?

We first saw DataRobot in action at the Strata Data Conference in New York in 2017. One of their sales people demoed the product, explaining that a key goal of the company was to put the power of machine learning into the hands of business analysts.

The first thing we noticed looking at DataRobot’s interface was the giant “Start” button in the middle of the screen. DataRobot, as their sales person explained, aimed to do for machine learning what the point and shoot camera did for photography – simplify a complex technical process into the shortest number of steps.

DataRobot’s sales person also explained that their ultimate goal was to create a clear link between machine learning and ROI impact. That is, they aimed to help the business analysts, versus the data scientist, understand the link between predictive analytics and business problems.

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