Welcome!

Wearables Authors: Liz McMillan, Elizabeth White, Yeshim Deniz, Pat Romanski, Rostyslav Demush

Related Topics: @CloudExpo, @DXWorldExpo, @ThingsExpo

@CloudExpo: Blog Post

The #IoT and #Analytics | @ThingsExpo #BigData #BI #AI #DX #MachineLearning

The Internet of Things promises to change everything by enabling “smart” environments and smart products

The Internet of Things (IoT) and Analytics at The Edge

The Internet of Things (IoT) promises to change everything by enabling “smart” environments (homes, cities, hospitals, schools, stores, etc.) and smart products (cars, trucks, airplanes, trains, wind turbines, lawnmowers, etc.). I recently wrote about the importance of moving beyond “connected” to “smart” in a blog titled “Internet of Things: Connected Does Not Equal Smart”. The article discusses the importance of moving beyond just collecting the data, to transitioning to leveraging this new wealth of IoT data to improve the decisions that these smart environments and products need to make: to help these environments and products to self-monitor, self-diagnose and eventually, self-direct.

But one of the key concepts in enabling this transition from connected to smart is the ability to perform “analytics at the edge.” Shawn Rogers, Chief Research Officer at Dell Statistica, had the following quote in an article in Information Management titled “Will the Citizen Data Scientist Inherit the World?”:

“Organizations are fast coming to the realization that IoT implementations are only going to become more vast and more pervasive, and that as that happens, the traditional analytic model of pulling all data in to a centralized source such as a data warehouse or analytic sandbox is going to make less and less sense.

So, most of the conversations I’m having around IoT analytics today revolve around looking at how companies can flip that model on its head and figure out ways to push the analytics out to the edge. If you can run analytics at the edge, you not only can eliminate the time, bandwidth and expense required to transport the data, but you make it possible to take immediate action in response to the insight. You speed up and simplify the analytic process in a way that’s never been done before.”

So I asked Shawn and his boss John Thompson, General Manager of Advanced Analytics at Dell, to help me understand what exactly they mean by “analytics at the edge.” It really boils down to these questions:

  • Are we really developing analytics at the edge?
  • If not, then what sorts of analytics are we performing at the edge?
  • Where are the analytic models actually being built?
  • And finally, what the heck does “at the edge” really mean?
  • So let’s actually start with that last question: What does “at the edge” really mean?

Question #1: What Is “At The Edge”?
“At the edge” refers to the multitude of devices or sensors that are scattered across any network or embedded throughout a product (car, jet engine, CT Scan) that is generating data about the operations and performance of that specific device or sensor.

For example, the current Airbus A350 model has close to 6,000 sensors and generates 2.5 Tb of data per day, while an even newer model – expected to be available in 2020 – will capture more than triple that amount! It is becoming more and more common for everyday common products to have hundreds if not thousands of embedded sensors that are generating readings every couple of seconds on the operations and performance of that particular product (see Figure 1).

Figure 1: Sensors at the Edge

But collecting these huge and real-time volumes of data doesn’t do anything to directly create business advantage. It is what you do with that data that drives the business value, which brings us to…

Question #2: Are We Really Developing Analytics “At The Edge”?
Are we really “performing analytics” (collecting the data, storing the data, preparing the data, running analytic algorithms, validating the analytic goodness of fit and then acting on the results) at the edges, or are we just “executing the analytic models” at the edges? It’s one thing to “execute the analytic models” (e.g., scores, rules, recommendations) at the edges, but something entirely different to actually “perform analytics” at the edges.

Per Shawn and John, “We can deliver analytic models to any end point. We can execute the analytic models in any environment – large or small. We can execute all the steps in performing analytics in a wide range of environments, but there are limits at the edge. The limits are on the robustness of the environment (i.e. cannot deliver an executable to an environment that does not have the memory or processing power to store it or execute it. We cannot change the laws of physics…;-).)”

Question #3: What Sorts Of Analytics Are We Performing At The Edge?
In our airplane example with 6,000 sensors on the plane generating over 2.5 Tb of data per day, how are we performing the analytics at the end?

Per John and Shawn, if the jet engine has a place to house a Java Virtual Machine (JVM) and an analytic model (i.e., lightweight rules based model), then we can execute the model on the engine itself. If the model streams the data to a network, we can execute the analytic model on a gateway, or intermediate server (see Figure 2).

Figure 2: Executing Analytic Models at The Edge

Think of the network as having concentric rings. Each ring can have many servers. Each server can do either – either executing an analytic model or building the analytic models. Now think of many network networks with concentric rings that interlock at various intersections. Analytics can be at any or all levels including at the core, in a data center or in the cloud.

Per Shawn, “By working in tandem with Dell Boomi, we’ve given users the ability to deploy JVM’s with the analytic models on any edge device or gateway anywhere on the network or device. This edge scoring capability enables organizations to address nearly any IoT analytics use case by executing the analytic models at the edge of the network where data is being created.”

Question #4: Where Are The Analytic Models Actually Being Built?
Okay, so we “execute” the pre-built modes at the edge, but we actually build (test, refine, test, refine) the analytic models by bringing the detailed sensor data back to a central data and analytics environment (a.k.a. the Data Lake). Figure 3, courtesy of Joel Dodd of Pivotal, shows the data flow and the supporting analytics execution.

Figure 3: “At the Edge” Analytic Model Execution

Final point, even if you are doing all the sensor/IoT analysis at the edges, you are likely still going to want to bring the raw IoT data back into the data lake for more extensive analysis in order to house the detailed IoT history. For example, we have major economic cycles every 4 to 7 years. You might want to quantify the impact of these economic changes on your network demand and performance. That would eventually require 8 to 14 years of data. And that’s why you are going to want a data lake as the foundation of the transition from a “connected” IoT world to a “smart” IoT world.

The post The Internet of Things (IoT) and Analytics at The Edge appeared first on InFocus.

Read the original blog entry...

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Hitachi Vantara as CTO, IoT and Analytics.

Previously, as a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

IoT & Smart Cities Stories
In his session at 21st Cloud Expo, Raju Shreewastava, founder of Big Data Trunk, provided a fun and simple way to introduce Machine Leaning to anyone and everyone. He solved a machine learning problem and demonstrated an easy way to be able to do machine learning without even coding. Raju Shreewastava is the founder of Big Data Trunk (www.BigDataTrunk.com), a Big Data Training and consulting firm with offices in the United States. He previously led the data warehouse/business intelligence and Bi...
Contextual Analytics of various threat data provides a deeper understanding of a given threat and enables identification of unknown threat vectors. In his session at @ThingsExpo, David Dufour, Head of Security Architecture, IoT, Webroot, Inc., discussed how through the use of Big Data analytics and deep data correlation across different threat types, it is possible to gain a better understanding of where, how and to what level of danger a malicious actor poses to an organization, and to determin...
Nicolas Fierro is CEO of MIMIR Blockchain Solutions. He is a programmer, technologist, and operations dev who has worked with Ethereum and blockchain since 2014. His knowledge in blockchain dates to when he performed dev ops services to the Ethereum Foundation as one the privileged few developers to work with the original core team in Switzerland.
Cloud-enabled transformation has evolved from cost saving measure to business innovation strategy -- one that combines the cloud with cognitive capabilities to drive market disruption. Learn how you can achieve the insight and agility you need to gain a competitive advantage. Industry-acclaimed CTO and cloud expert, Shankar Kalyana presents. Only the most exceptional IBMers are appointed with the rare distinction of IBM Fellow, the highest technical honor in the company. Shankar has also receive...
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
"MobiDev is a Ukraine-based software development company. We do mobile development, and we're specialists in that. But we do full stack software development for entrepreneurs, for emerging companies, and for enterprise ventures," explained Alan Winters, U.S. Head of Business Development at MobiDev, in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
Cloud computing delivers on-demand resources that provide businesses with flexibility and cost-savings. The challenge in moving workloads to the cloud has been the cost and complexity of ensuring the initial and ongoing security and regulatory (PCI, HIPAA, FFIEC) compliance across private and public clouds. Manual security compliance is slow, prone to human error, and represents over 50% of the cost of managing cloud applications. Determining how to automate cloud security compliance is critical...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
Recently, REAN Cloud built a digital concierge for a North Carolina hospital that had observed that most patient call button questions were repetitive. In addition, the paper-based process used to measure patient health metrics was laborious, not in real-time and sometimes error-prone. In their session at 21st Cloud Expo, Sean Finnerty, Executive Director, Practice Lead, Health Care & Life Science at REAN Cloud, and Dr. S.P.T. Krishnan, Principal Architect at REAN Cloud, discussed how they built...
When talking IoT we often focus on the devices, the sensors, the hardware itself. The new smart appliances, the new smart or self-driving cars (which are amalgamations of many ‘things'). When we are looking at the world of IoT, we should take a step back, look at the big picture. What value are these devices providing. IoT is not about the devices, its about the data consumed and generated. The devices are tools, mechanisms, conduits. This paper discusses the considerations when dealing with the...