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Difference Between #BigData and Internet of Things | @ThingsExpo #IoT #M2M
What does it mean, as a vendor, to say that you support the Internet of Things (IoT) from an analytics perspective?

A recent argument with folks whose intelligence I hold in high regard (like Tom, Brandon, Wei, Anil, etc.) got me thinking about the following question:

What does it mean, as a vendor, to say that you support the Internet of Things (IoT) from an analytics perspective?

I think the heart of that question really boils down to this:

What are the differences between big data (which is analyzing large amounts of mostly human-generated data to support longer-duration use cases such as predictive maintenance, capacity planning, customer 360 and revenue protection) and IoT (which is aggregating and compressing massive amounts of low latency / low duration / high volume machine-generated data coming from a wide variety of sensors to support real-time use cases such as operational optimization, real-time ad bidding, fraud detection, and security breach detection)?

I don’t believe that loading sensor data into a data lake and performing data science to create predictive analytic models qualifies as doing IoT analytics.  To me, that’s just big data (and potentially REALLY BIG DATA with all that sensor data).  In order for one to claim that they can deliver IoT analytic solutions requires big data (with data science and a data lake), but IoT analytics must also include:

  1. Streaming data management with the ability to ingest, aggregate (e.g., mean, median, mode) and compress real-time data coming off a wide variety of sensor devices “at the edge” of the network, and
  2. Edge analytics that automatically analyzes real-time sensor data and renders real-time decisions (actions) at the edge of the network that optimizes operational performance (blade angle or yaw) or flags unusual performance or behaviors for immediate investigation (security breaches, fraud detection).

If you cannot manage real-time streaming data and make real-time analytics and real-time decisions at the edge, then you are not doing IOT or IOT analytics, in my humble opinion.  So what is required to support these IoT data management and analytic requirements?

The IoT “Analytics” Challenge
The Internet of Things (or Industrial Internet) operates at machine-scale, by dealing with machine-to-machine generated data.  This machine-generated data creates discrete observations (e.g., temperature, vibration, pressure, humidity) at very high signal rates (1,000s of messages/sec).  Add to this the complexity that the sensor data values rarely change (e.g., temperature operates within an acceptably small range).  However, when the values do change the ramifications, the changes will likely be important.

Consequently to support real-time edge analytics, we need to provide detailed data that can flag observations of concern, but then doesn’t overwhelm the ability to get meaningful data back to the core (data lake) for more broad-based, strategic analysis.

One way that we see organizations addressing the IoT analytics needs is via a 3-tier Analytics Architecture (see Figure 1).

Figure 1: IoT Analytics 3-Tier Architecture

We will use a wind turbine farm to help illustrate the 3-tier analytics architecture capabilities.

Tier 1 performs individual wind turbine real-time performance analysis and optimization.  Tier 1 must manage (ingest and compress) real-time data streams coming off of multiple, heterogeneous sensors. Tier 1 analyzes the data, and processes the incoming data against static or dynamically updated analytic models (e.g., rules-based, decision trees) for immediate or near-immediate actions.

Purpose-built T1 edge gateways leverage real-time data compression techniques (e.g., see the article “timeseries storage and data compression” for more information on timeseries databases) to only send a subset of the critical data (e.g., data that has changed) back to T2 and T3 (core).

Let’s say that you are monitoring the temperatures of a compressor inside of a large industrial engine.  Let’s say the average temperature of that compressor is 99 degrees, and only varies between 98 to 100 degrees within a 99% confidence level.  Let’s also say the compressor is emitting the following temperature readings 10 times a second:

99, 99, 99, 98, 98, 99, 99, 98, 99, 99, 100, 99, 99, 99, 100, 99, 98, 99, 99…

You have 10,000 of readings that don’t vary from that range.  So why send all of the readings (which from a transmission bandwidth perspective could be significant)?  Instead, use a timeseries database to only send mean, medium, mode, variances, standard deviation and other statistical variables of the 10,000 readings instead of the individual 10,000 readings.

However, let’s say that all of a sudden we start getting readings outside the normal 99% confidence level:

99, 99, 99, 100, 100, 101, 101, 102, 102, 103, 104, 104, 105, …

Then we’d apply basic Change Data Capture (CDC) techniques to capture and transmit the subset of critical data to T2 and T3 (core).

Consequently, edge gateways leverage timeseries compression techniques to drive faster automated decisions while only sending a subset of critical data to the core for further analysis and action.

The Tier 1 analytics are likely being done via an on-premise analytics server or gateway (see Figure 2).

Figure 2:  IoT Tier 1 Analytics

Tier 2 optimizes performance and predicts maintenance needs across the wind turbines in the same wind farm.  Tier 2 requires a distributed dynamic content processing rule generation and execution analytics engine that integrates and analyzes data aggregated across the potentially heterogeneous wind turbines. Cohort analysis is typical in order to identify, validate and codify performance problems and opportunities across the cohort wind turbines.  For example, in the wind farm, the Tier 2 analytics are responsible for real-time learning that can generate the optimal torque and position controls for the individual wind turbines. Tier 2 identifies and shares best practices across the wind turbines in the wind farm without having to be dependent upon the Tier 3 core analytics platform (see Figure 3).

Figure 3: Tier 2 Analytics: Optimizing Cohort Performance

Tier 3 is the data lake enabled core analytics platform. The tier 3 core analytics platform includes analytics engines, data sets and data management services (e.g., governance, metadata management, security, authentication) that enable access to the data (sensor data plus other internal and external data sources) and existing analytic models that supports data science analytic/predictive model development and refinement.  Tier 3 aggregates the critical data across all wind farms and individual turbines, and combines the sensor data with external data sources which could include weather (humidity, temperatures, precipitation, air particles, etc.), electricity prices, wind turbine maintenance history, quality scores for the wind turbine manufacturers, and performance profiles of the wind turbine mechanics and technicians (see Figure 4).

Figure 4:  Core Analytics for Analytic Model Development and Refinement

With the rapid increase in storage and processing power at the edges of the Internet of Things (for example, the Dell Edge Gateway 3000 Series), we will see more and more analytic capabilities being pushed to the edge.

How Do You Start Your IoT Journey
While the rapidly evolving expertise on the IoT edge technologies can be very exciting (graphical processing units in gateway servers with embedded machine learning capabilities with 100’s of gigabytes of storage), the starting point for the IoT journey must first address this basic question:

How effective is your organization at leveraging data and analytics to power your business (or operational) models?

We have tweaked the Big Data Business Model Maturity Index to help organizations not only understand where they sit on the maturity index with respect to the above question, but also to provide a roadmap for how organizations can advance up the maturity index to become more effective at leveraging the wealth of IOT data with advanced analytics to power their business and operational models (see Figure 5).

Figure 5:  Big Data / IoT Business Model Maturity IndexMaturity Index

To drive meaningful business impact, you will need to begin with the business and not the technology:

  • Engage the business stakeholders on day one,
  • Align the business and IT teams
  • Understand the organization’s key business and operational initiatives, and
  • Identify and prioritize the use cases (decisions/goals) that support those business initiatives.

If you want to monetize your IOT initiatives, follow those simple guidelines and you will dramatically increase the probability of your business and monetization success.

For more details on the Internet of Things revolution, check out these blogs:

The post Difference between Big Data and Internet of Things appeared first on InFocus Blog | Dell EMC Services.

Read the original blog entry...

About William Schmarzo
Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.

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@CloudExpo Blogs
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Colocation is a central pillar of modern enterprise infrastructure planning because it provides greater control, insight, and performance than managed platforms. In spite of the inexorable rise of the cloud, most businesses with extensive IT hardware requirements choose to host their infrastructure in colocation data centers. According to a recent IDC survey, more than half of the businesses questioned use colocation services, and the number is even higher among established businesses and businesses with an existing base of IT expertise.
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For most organizations, the move to hybrid cloud is now a question of when, not if. Fully 82% of enterprises plan to have a hybrid cloud strategy this year, according to Infoholic Research. The worldwide hybrid cloud computing market is expected to grow about 34% annually over the next five years, reaching $241.13 billion by 2022. Companies are embracing hybrid cloud because of the many advantages it offers compared to relying on a single provider for all of their cloud needs. Hybrid offers balance and flexibility. It helps companies achieve a wide array of business goals, including availabili...
The margins of cloud products like virtual machines are still in the 50% range. In essence, price drops are going to be a regular feature for the foreseeable future. This begets the question - are hosted solutions becoming irrelevant today? Boston-based market research firm, 451 Research, has been publishing their ‘Cloud Price Index' for a few years now. The quarterly study looks into the pricing of various offerings in the cloud market to understand the shifting dynamics in the public, private and hybrid cloud market. According to Dr. Owen Rogers from 451 Research, the approach here is simi...
The dirty little secret, however, is that we are as unable to predict the future as you. None of us really knows what will happen next. The future is made up of an incredibly complex mixture of technologies, possibilities, and events that come together in ways that no one can truly predict. The answer, therefore, is not to attempt to predict the future. But instead, to prepare for an uncertain future by building organizational capabilities that allow you to anticipate and rapidly pivot, respond and react to any change that the market may throw at you.
The purpose of enterprise architecture is to be able to consciously design an enterprise rather than allowing it to happen randomly and unconsciously. It is worth noting that it implies knowledge of a certain intended outcome or desired state in mind. Enterprise architecture (EA) is a discipline that enables designing the enterprise consciously and deliberately, rather than letting it happen randomly. EA design is informed by business vision, strategic intent, and insights on the functioning of the enterprise. So the purpose of enterprise architecture is to be able to consciously design an ent...
The cloud’s massive success and the growing desire for Big Data analytics are just two of the factors that are making traditional storage architectures obsolete. Organizations cannot afford to scale bygone storage appliances. Even if they could, the time it would take is unacceptable in the digital world. Adding multiple servers could not accommodate storage demands, either. Vertical storage architecture contains bottlenecks that slow performance to a crawl.
The Internet of Things (IoT) has evolved quite rapidly, especially if you consider how long it took for little packet-switching networks like ARPANET to morph into today’s nearly ubiquitous internet. Our nascent IoT ecosystem is already populated by billions of sensors and connected devices, hundreds of millions of servers, and thousands of hybrid clouds serving billions of people… all of them perpetually producing and/or consuming data in an explosion of information.
Human body is the most complex machine ever created! With a complex network of interconnected organs, millions of cells and the most advanced processor, human body is the most automated system in this planet. In this article, we will draw comparisons between working of a human body to that of a data center. We will draw parallels between human body automation to data center automation and explain different levels of automation we need to drive in data centers. This article is divided into 4 parts covering each of body main functions and drawing parallels on automation. This is 3rd article in ...
A look across the tech landscape at the disruptive technologies that are increasing in prominence and speculate as to which will be most impactful for communications – namely, AI and Cloud Computing. In his session at 20th Cloud Expo, Curtis Peterson, VP of Operations at RingCentral, highlighted the current challenges of these transformative technologies and shared strategies for preparing your organization for these changes. This “view from the top” outlined the latest trends and developments in AI and Cloud Computing technology innovation for enterprise communications to help you shape your ...
The cloud ushered in a fundamental change in the way applications are deployed. Instead of worrying about how much dedicated hardware to order for your data center, you simply spin up as many servers as you need to do the job, then decommission them once the job is done. Yet, you still have to plan how many servers you will need at each stage of processing; you still have to remember to decommission them when done; and you pay for the entire virtual machine during the time you use it even though the server may be idle for most of that time.
In today's cloud based world, automation is key enabler for success. Yet, folks are scared of automation due to risk of losing job. However automation needs to be seen as a way to keep your job in and not out. Wikipedia definition of automation is "Automation or automatic control, is the use of various control systems for operating equipment such as machinery, processes in factories, boilers and heat treating ovens, switching on telephone networks, steering and stabilization of ships, aircraft and other applications and vehicles with minimal or reduced human intervention." The key word in th...
Automation is enabling enterprises to design, deploy, and manage more complex, hybrid cloud environments. Yet the people who manage these environments must be trained in and understanding these environments better than ever before. A new era of analytics and cognitive computing is adding intelligence, but also more complexity, to these cloud environments. How smart is your cloud? How smart should it be? In this power panel at 20th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists looked at the evolving nature of hybrid cloud, how it affects enterprise IT staffing requirement...
Containers are things for your things. That is true in the development world too - containers are things for your software, and, like containers for your things, software containers can help you be organized, efficient, and secure. Chris Morgan (@cmorgan_cloud), the Technical Director, OpenShift Partner Ecosystem at Red Hat, delivered a keynote at the 2016 All Day DevOps conference. Chris lives and breathes containers and recently discussed how they are transforming DevOps pipelines.
SYS-CON Events announced today that MobiDev, a client-oriented software development company, will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. MobiDev is a software company that develops and delivers turn-key mobile apps, websites, web services, and complex software systems for startups and enterprises. Since 2009 it has grown from a small group of passionate engineers and business managers to a full-scale mobile software company with over 200 developers, designers, quality assur...
SYS-CON Events announced today that GrapeUp, the leading provider of rapid product development at the speed of business, will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Grape Up is a software company, specialized in cloud native application development and professional services related to Cloud Foundry PaaS. With five expert teams that operate in various sectors of the market across the USA and Europe, we work with a variety of customers from emerging startups to Fortune 1000 c...
SYS-CON Events announced today that Ayehu will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on October 31 - November 2, 2017 at the Santa Clara Convention Center in Santa Clara California. Ayehu provides IT Process Automation & Orchestration solutions for IT and Security professionals to identify and resolve critical incidents and enable rapid containment, eradication, and recovery from cyber security breaches. Ayehu provides customers greater control over IT infrastructure through automation. Ayehu solutions have been deployed by major enterprises worldwide, and ...
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Past SYS-CON Events
    Cloud Expo West
cloudcomputingexpo
2011west.sys-con.com

 
    Cloud Expo East
cloudcexpo
2011east.sys-con.com

 
    Cloud Expo West
cloudcomputingexpo
2010west.sys-con.com

 
    Virtualization Expo West
virtualization
2010west.sys-con.com
    Cloud Expo Europe
cloudexpoeurope2010.
sys-con.com

 
    Cloud Expo East
cloudcomputingexpo
2010east.sys-con.com

 
    Virtualization Expo East
virtualizationconference
2010east.sys-con.com
    Cloud Expo West
cloudcomputingexpo
2009west.sys-con.com

 
    Virtualization Expo West
virtualizationconference
2009west.sys-con.com
    GovIT Expo
govitexpo.com
 
    Cloud Expo Europe
cloudexpoeurope2009.sys-con.com
 

Cloud Expo 2011 Allstar Conference Faculty

S.F.S.
Dell

Singer
NRO

Pereyra
Oracle

Ryan
OpSource

Butte
PwC

Leone
Oracle

Riley
AWS

Varia
AWS

Lye
Oracle

O'Connor
AppZero

Crandell
RightScale

Nucci
Dell Boomi

Hillier
CiRBA

Morrison
Layer 7 Tech

Robbins
NYT

Schwarz
Oracle

What The Enterprise IT World Says About Cloud Expo
 
"We had extremely positive feedback from both customers and prospects that attended the show and saw live demos of NaviSite's enterprise cloud based services."
  –William Toll
Sr. Director, Marketing & Strategic Alliances
Navisite
 


 
"More and better leads than ever expected! I have 4-6 follow ups personally."
  –Richard Wellner
Chief Scientist
Univa UD
 


 
"Good crowd, good questions. The event looked very successful."
  –Simon Crosby
CTO
Citrix Systems
 


 
"It's the largest cloud computing conference I've ever seen."
  –David Linthicum
CTO
Brick Group