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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.

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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Cloud Expo - Cloud Looms Large on SYS-CON.TV


Cloud Expo 2013 East Opening Keynote by IBM
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In this "Cloud Trends" Power Panel at the 10th International Cloud Expo, moderated by Cloud Expo Conference Chair Jeremy Geelan, Karen Padir, EVP of Products & Engineering at EnterpriseDB, and Aaron Hollobaugh, VP of Marketing & Communications at Hostway Corporation, map the hottest areas of cloud computing right now, and share real-world examples of offerings that are generating value. ... (more)

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We live in a world that requires us to compete on our differential use of time and information, yet only a fraction of information workers today have access to the analytical capabilities they need to make better decisions. Now, with the advent of a new generation of embedded business intelligence (BI) platforms, cloud developers are disrupting the world of analytics. They are using these new BI platforms to inject more intelligence into the applications business people use every day. As a result, data-driven decision-making is finally on track to become the rule, not the exception.
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@CloudExpo Blogs
identify the sources of event storms and performance anomalies will require automated, real-time root-cause analysis. I think Enterprise Management Associates said it well: “The data and metrics collected at instrumentation points across the application ecosystem are essential to performance monitoring and root cause analysis. However, analytics capable of transforming data and metrics into an application-focused report or dashboards are what separates actual application monitoring from relatively simple silo monitoring.
One problem that all developers and companies struggle with is trying to decide if they should "build it" or "buy it". Software developers love to build things. That is what we do! Their natural reaction tends to lean towards building things. We are also always up for a new challenge. There are very good reasons for building or buying software. There are also good reasons to use open source projects, which is a third option.
These days, no matter what task you’re trying to accomplish within your online properties, chances are there’s at least one cloud solution that provides it. However, with so much of our personal and business data living now online, there’s perhaps no functionality more important than cloud security. With cyber attacks more prevalent than ever, it’s imperative that organizations – regardless of their size and scope – protect both themselves and their clients from nefarious individuals who prey on unsecured networks and data.
MongoDB, an open-source document store and most popular NoSQL database on the market today, offers a variety of advanced features to administer security over your MongoDB deployments. In this tutorial post, we’re going to show you how to set up role-based access control (RBAC) to manage user access across your MongoDB systems for reIndex, mongodump and mongorestore. If you're the administrator of your MongoDB databases, you've likely received requests to provide an individual user with the capabilities to perform a certain action(s). MongoDB's security features are fairly mature now, and allo...
Quick quiz! What’s the first thing that comes to mind when you hear the following phrases? Artificial grass Artificial sweeteners Artificial flavors Artificial plants
The benefits of automation are well documented; it increases productivity, cuts cost and minimizes errors. It eliminates repetitive manual tasks, freeing us up to be more innovative. By that logic, surely, we should automate everything possible, right? So, is attempting to automate everything a sensible - even feasible - goal? In a word: no. Consider this your short guide as to what to automate and what not to automate.
The rule of thumb for network security today is that there is no perimeter anymore. An outsider can easily become an insider once perimeter security is breached. Every day, attackers find new ways to breach enterprise perimeter security through ransomware, malware or phishing through social engineering. This is not to suggest that all is lost. Rather, organizations can defeat cybercriminals, in part, by better managing what has already been put in place. As an example of what can go wrong if that doesn’t happen, consider the following story.
Cavirin Systems has just announced C2, a SaaS offering designed to bring continuous security assessment and remediation to hybrid environments, containers, and data centers. Cavirin C2 is deployed within Amazon Web Services (AWS) and features a flexible licensing model for easy scalability and clear pay-as-you-go pricing. Although native to AWS, it also supports assessment and remediation of virtual or container instances within Microsoft Azure, Google Cloud Platform (GCP), or on-premise. By drawing on a comprehensive library of curated industry guidelines, control frameworks, and best practi...
Let's do a visualization exercise. Imagine it's December 31, 2018, and you're ringing in the New Year with your friends and family. You think back on everything that you accomplished in the last year: your company's revenue is through the roof thanks to the success of your product, and you were promoted to Lead Developer. 2019 is poised to be an even bigger year for your company because you have the tools and insight to scale as quickly as demand requires. You're a happy human, and it's not just because of the bubbly in your glass. Now how does one turn this visualization into reality? You st...
"Opsani helps the enterprise adopt containers, help them move their infrastructure into this modern world of DevOps, accelerate the delivery of new features into production, and really get them going on the container path," explained Ross Schibler, CEO of Opsani, and Peter Nickolov, CTO of Opsani, in this SYS-CON.tv interview at DevOps Summit at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
Troubleshooting a problem on a remote server, especially in production, is not an easy task. Sometimes it involves debugging the application code directly on the server. But the production servers are usually run in a strict environment, where not all convenient developer tools are available. In this article, you'll discover how to configure a running web server and debug your application using standard facilities provided by the Java platform.
Developing mobile apps has never been an easy task. Creating a mobile app for iOS means owning strong programming skills about Objective-C or Swift and knowing their APIs. Android-based apps are not so different: you have to know Java and the Android Platform and its API. The learning curve is not fast and it includes how to create nice and interactive user interfaces, connecting embedded features like GPS, camera, showing maps, images and so forth.
The nature of test environments is inherently temporary—you set up an environment, run through an automated test suite, and then tear down the environment. If you can reduce the cycle time for this process down to hours or minutes, then you may be able to cut your test environment budgets considerably. The impact of cloud adoption on test environments is a valuable advancement in both cost savings and agility. The on-demand model takes advantage of public cloud APIs requiring only payment for the time needed to run automated tests. In this framework, success depends on two things: automated i...
BnkToTheFuture.com is the largest online investment platform for investing in FinTech, Bitcoin and Blockchain companies. We believe the future of finance looks very different from the past and we aim to invest and provide trading opportunities for qualifying investors that want to build a portfolio in the sector in compliance with international financial regulations.
Digital experience monitoring plays a vital role in the ecommerce economy. The industry is booming with millions of websites selling everything imaginable. Online stores are expected to be super fast and easy to navigate; users are quick to assess website performance and if said perceived performance is below expectations, they will quickly move on to competitor’s website.
The question before companies today is not whether to become intelligent, it’s a question of how and how fast. The key is to adopt and deploy an intelligent application strategy while simultaneously preparing to scale that intelligence. In her session at 21st Cloud Expo, Sangeeta Chakraborty, Chief Customer Officer at Ayasdi, provided a tactical framework to become a truly intelligent enterprise, including how to identify the right applications for AI, how to build a Center of Excellence to operationalize the intelligence and how to implement a strategy to scale efforts. She pulled from her ex...
Organizations around the world are struggling to cope with the current data explosion. A vital characteristic of this data is that it is unstructured and represents things like email, images, and videos. Storage of this form of data is typically in an object format which differs significantly from the database norm. Databases housed data grows very slowly because most of it is structured. Object storage formats are now being used to optimize access to large amounts of non-transactional files across a growing number of vertical markets.
"IBM is really all in on blockchain. We take a look at sort of the history of blockchain ledger technologies. It started out with bitcoin, Ethereum, and IBM evaluated these particular blockchain technologies and found they were anonymous and permissionless and that many companies were looking for permissioned blockchain," stated René Bostic, Technical VP of the IBM Cloud Unit in North America, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
The end of the year is a time for reflection. It’s when most of us are looking back at the choices, accomplishments, and mistakes of the year prior and setting goals to improve the following year. It’s also when businesses analyze the year’s trends and behaviors to determine necessary strategic changes to be made; however, if you aren’t analyzing the right metrics, such reflection is a useless effort. Below is an excerpt from an article provided by Elad Rave, founder and CTO of Teridion, explaining why TTLB (Time to Last Byte) should be one of the performance metrics on your radar.
How much does it cost to make an app is almost as popular a question as it is confusing. No one tries to learn the exact costs of, say, making a movie: people realize that there’s an overwhelming amount of variables involved on which they depend. But almost every day a new inquiry is posted on a tech forum, Quora, or Reddit as to how much it’d take to build a mobile business app.
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Past SYS-CON Events
    Cloud Expo West
cloudcomputingexpo
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    Cloud Expo East
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    Cloud Expo West
cloudcomputingexpo
2010west.sys-con.com

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

 
    Virtualization Expo West
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    GovIT Expo
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    Cloud Expo Europe
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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