Welcome!

@DXWorldExpo Authors: Pat Romanski, Elizabeth White, Yeshim Deniz, Liz McMillan, William Schmarzo

Related Topics: @ThingsExpo, @CloudExpo, @DXWorldExpo

@ThingsExpo: Article

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.

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.

DXWorldEXPO Digital Transformation Stories
Bill Schmarzo, Tech Chair of "Big Data | Analytics" of upcoming CloudEXPO | DXWorldEXPO New York (November 12-13, 2018, New York City) today announced the outline and schedule of the track. "The track has been designed in experience/degree order," said Schmarzo. "So, that folks who attend the entire track can leave the conference with some of the skills necessary to get their work done when they get back to their offices. It actually ties back to some work that I'm doing at the University of ...
DXWorldEXPO LLC, the producer of the world's most influential technology conferences and trade shows has announced the 22nd International CloudEXPO | DXWorldEXPO "Early Bird Registration" is now open. Register for Full Conference "Gold Pass" ▸ Here (Expo Hall ▸ Here)
When it comes to cloud computing, the ability to turn massive amounts of compute cores on and off on demand sounds attractive to IT staff, who need to manage peaks and valleys in user activity. With cloud bursting, the majority of the data can stay on premises while tapping into compute from public cloud providers, reducing risk and minimizing need to move large files. In his session at 18th Cloud Expo, Scott Jeschonek, Director of Product Management at Avere Systems, discussed the IT and busine...
The revocation of Safe Harbor has radically affected data sovereignty strategy in the cloud. In his session at 17th Cloud Expo, Jeff Miller, Product Management at Cavirin Systems, discussed how to assess these changes across your own cloud strategy, and how you can mitigate risks previously covered under the agreement.
CloudEXPO New York 2018, colocated with DXWorldEXPO New York 2018 will be held November 11-13, 2018, in New York City and will bring together Cloud Computing, FinTech and Blockchain, Digital Transformation, Big Data, Internet of Things, DevOps, AI, Machine Learning and WebRTC to one location.
The Internet of Things will challenge the status quo of how IT and development organizations operate. Or will it? Certainly the fog layer of IoT requires special insights about data ontology, security and transactional integrity. But the developmental challenges are the same: People, Process and Platform and how we integrate our thinking to solve complicated problems. In his session at 19th Cloud Expo, Craig Sproule, CEO of Metavine, demonstrated how to move beyond today's coding paradigm and sh...
The best way to leverage your Cloud Expo presence as a sponsor and exhibitor is to plan your news announcements around our events. The press covering Cloud Expo and @ThingsExpo will have access to these releases and will amplify your news announcements. More than two dozen Cloud companies either set deals at our shows or have announced their mergers and acquisitions at Cloud Expo. Product announcements during our show provide your company with the most reach through our targeted audiences.
Traditional on-premises data centers have long been the domain of modern data platforms like Apache Hadoop, meaning companies who build their business on public cloud were challenged to run Big Data processing and analytics at scale. But recent advancements in Hadoop performance, security, and most importantly cloud-native integrations, are giving organizations the ability to truly gain value from all their data. In his session at 19th Cloud Expo, David Tishgart, Director of Product Marketing ...
DevOpsSummit New York 2018, colocated with CloudEXPO | DXWorldEXPO New York 2018 will be held November 11-13, 2018, in New York City. Digital Transformation (DX) is a major focus with the introduction of DXWorldEXPO within the program. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term.
Enterprises are striving to become digital businesses for differentiated innovation and customer-centricity. Traditionally, they focused on digitizing processes and paper workflow. To be a disruptor and compete against new players, they need to gain insight into business data and innovate at scale. Cloud and cognitive technologies can help them leverage hidden data in SAP/ERP systems to fuel their businesses to accelerate digital transformation success.