Become a data-driven organization with the Internet of Things
Executive summary
Personal health monitors tracking your fitness, trashcans monitoring their fullness, watches telling you more
than just the time, and agricultural soil monitors saying it’s time to water. It seems a day doesn’t go by that
we don’t hear about the latest “offline” thing, device, or equipment becoming “online,” moving from isolation
to being connected to the Internet of Things (IoT). It’s clear that integrating sensors, electronics, and
network connectivity into devices can enable innovation, enhancing and extending the way we work and
interact with each other and the world around us.
8.0Transforming records management for Information Governance
•Access and understand virtually any source of information on-premise and in the cloud
•A strategic pillar of HP’s HAVEnBig Data platform
•Non-disruptive, manage-in-place approach complements any organization
Understanding human information
•Access and understand virtually any source of information on-premise and in the cloud
•A strategic pillar of HP’s HAVEnBig Data platform
•Non-disruptive, manage-in-place approach complements any organization
HPE IDOL Technical Overview - july 2016Andrey Karpov
Search and Analytics Platform for Text and Rich Media
Open Innovation is transforming everything
Connected people, apps and things generating massive data in many forms
How do you bridge the gap between data and outcomes?
Augmented Intelligence power apps for competitive advantage
Machine Learning at the Service of Business Augmented Intelligence
HPE Big Data Advanced Analytics Software Solutions
Strong information and weak information
HPE IDOL: Natural Language Processing (NLP) engine
Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014pietvz
A customer service call can transform internal processes. Information in Tweets and reviews can lead to better products. Structured and unstructured data brought together can reveal patterns and relationships that unlock powerful business opportunities. We will discuss real-world use cases and best practices for building the infrastructure you need to power Big Data analytics solutions. From the latest in Hadoop innovation, cognitive computing, and cloud-based analytical web services, you will learn how organizations large and small can harness the power of unstructured human information to create, deploy, and deliver the next generation of analytics applications.
Powered by HP IDOL, HP Autonomy delivers intelligent applications that allow your organization to understand the concepts and context of all information in real time, mitigating risk and identifying opportunity. Join us at this session to learn how HP Autonomy can unlock the value of your company’s structured and unstructured data for better insight and greater competitive advantage. HP IDOL, the OS for human information, enables you to index, manage, and process all your data, both structured and unstructured. Learn how HP IDOL delivers unprecedented insights into optimized architecture, scalability, performance, mapped security, and connectivity. Find out more about IDOLOnDemand.com and how you can leverage this revolutionary technology in your own organization.
How to Avoid Pitfalls in Big Data Analytics WebinarDatameer
Big data analytics is revolutionizing the way businesses are collecting, storing, and more importantly, analyzing data. However, the adoption of a big data analytics solution has its share of failures and false starts.
Watch this webinar to learn how to navigate the most common obstacles of big data analytics.
Datameer and MapR have worked with customers to identify and solve the common pitfalls organizations face when deploying Hadoop-based analytics.
In this webinar, we will show you how to:
• Find the balance between infrastructure and business use cases
• Overcome challenges of using multiple tools that address big data analytics
• Leverage all your resources (data scientists, IT and analysts) most effectively
Customer Experience: A Catalyst for Digital TransformationCloudera, Inc.
Customer experience is a catalyst in many digital transformation projects. It is why many businesses invest in new technologies and processes to more effectively engage customers, constituents, or employees. The goal of putting digital tools to work in a transformative way is to ensure that data and insights connect people with information and processes that ultimately lead to a better experience for customers. Yet, it demands a modern approach that considers all of the platforms, processes, and data across the customer journey. The goal for many organizations is dynamically maintaining a single source of truth about each customer to drive personalized experiences based on individual preferences and behaviors.
However, businesses today have primarily invested in systems of record. While these systems are critical for managing internal operational processes, they are typically not effective for today's pace of business change. Insight-driven experiences require customer intelligence platforms that can finally create a customer 360. The deeper data and improved algorithms now available let users factor in individual affinity, segment, and a myriad of growing data sources. The result is greater relevance and effectiveness to deliver a differentiated experience that in today’s competitive landscape is not a luxury, but a necessity for survival.
In this session we will address:
3 things to learn:
•Leaders and Laggards of digital transformation
•How to create data-driven customer insights
•The importance of machine learning to uncover hidden insights
HUG Hadoop User Group du 29 Janvier 2015 chez HP.
Slidedeck des 3 talks ci-dessous:
#1: Traitement des données non structurées (Vidéos, images, …) avec Haven pour Hadoop,
#2: Apache Flink: Fast and Reliable Large-scale Data Processing,
#3: Etude de cas, projet Hadoop dans le domaine des RH avec Capgemini.
La vectorisation des documents : rendre comparables des informations non structurées, de nouvelles opportunités pour un acteur de l’emploi
8.0Transforming records management for Information Governance
•Access and understand virtually any source of information on-premise and in the cloud
•A strategic pillar of HP’s HAVEnBig Data platform
•Non-disruptive, manage-in-place approach complements any organization
Understanding human information
•Access and understand virtually any source of information on-premise and in the cloud
•A strategic pillar of HP’s HAVEnBig Data platform
•Non-disruptive, manage-in-place approach complements any organization
HPE IDOL Technical Overview - july 2016Andrey Karpov
Search and Analytics Platform for Text and Rich Media
Open Innovation is transforming everything
Connected people, apps and things generating massive data in many forms
How do you bridge the gap between data and outcomes?
Augmented Intelligence power apps for competitive advantage
Machine Learning at the Service of Business Augmented Intelligence
HPE Big Data Advanced Analytics Software Solutions
Strong information and weak information
HPE IDOL: Natural Language Processing (NLP) engine
Take the Big Data Challenge - Take Advantage of ALL of Your Data 16 Sept 2014pietvz
A customer service call can transform internal processes. Information in Tweets and reviews can lead to better products. Structured and unstructured data brought together can reveal patterns and relationships that unlock powerful business opportunities. We will discuss real-world use cases and best practices for building the infrastructure you need to power Big Data analytics solutions. From the latest in Hadoop innovation, cognitive computing, and cloud-based analytical web services, you will learn how organizations large and small can harness the power of unstructured human information to create, deploy, and deliver the next generation of analytics applications.
Powered by HP IDOL, HP Autonomy delivers intelligent applications that allow your organization to understand the concepts and context of all information in real time, mitigating risk and identifying opportunity. Join us at this session to learn how HP Autonomy can unlock the value of your company’s structured and unstructured data for better insight and greater competitive advantage. HP IDOL, the OS for human information, enables you to index, manage, and process all your data, both structured and unstructured. Learn how HP IDOL delivers unprecedented insights into optimized architecture, scalability, performance, mapped security, and connectivity. Find out more about IDOLOnDemand.com and how you can leverage this revolutionary technology in your own organization.
How to Avoid Pitfalls in Big Data Analytics WebinarDatameer
Big data analytics is revolutionizing the way businesses are collecting, storing, and more importantly, analyzing data. However, the adoption of a big data analytics solution has its share of failures and false starts.
Watch this webinar to learn how to navigate the most common obstacles of big data analytics.
Datameer and MapR have worked with customers to identify and solve the common pitfalls organizations face when deploying Hadoop-based analytics.
In this webinar, we will show you how to:
• Find the balance between infrastructure and business use cases
• Overcome challenges of using multiple tools that address big data analytics
• Leverage all your resources (data scientists, IT and analysts) most effectively
Customer Experience: A Catalyst for Digital TransformationCloudera, Inc.
Customer experience is a catalyst in many digital transformation projects. It is why many businesses invest in new technologies and processes to more effectively engage customers, constituents, or employees. The goal of putting digital tools to work in a transformative way is to ensure that data and insights connect people with information and processes that ultimately lead to a better experience for customers. Yet, it demands a modern approach that considers all of the platforms, processes, and data across the customer journey. The goal for many organizations is dynamically maintaining a single source of truth about each customer to drive personalized experiences based on individual preferences and behaviors.
However, businesses today have primarily invested in systems of record. While these systems are critical for managing internal operational processes, they are typically not effective for today's pace of business change. Insight-driven experiences require customer intelligence platforms that can finally create a customer 360. The deeper data and improved algorithms now available let users factor in individual affinity, segment, and a myriad of growing data sources. The result is greater relevance and effectiveness to deliver a differentiated experience that in today’s competitive landscape is not a luxury, but a necessity for survival.
In this session we will address:
3 things to learn:
•Leaders and Laggards of digital transformation
•How to create data-driven customer insights
•The importance of machine learning to uncover hidden insights
HUG Hadoop User Group du 29 Janvier 2015 chez HP.
Slidedeck des 3 talks ci-dessous:
#1: Traitement des données non structurées (Vidéos, images, …) avec Haven pour Hadoop,
#2: Apache Flink: Fast and Reliable Large-scale Data Processing,
#3: Etude de cas, projet Hadoop dans le domaine des RH avec Capgemini.
La vectorisation des documents : rendre comparables des informations non structurées, de nouvelles opportunités pour un acteur de l’emploi
Presumption of Abundance: Architecting the Future of SuccessInside Analysis
Hot Technologies with Dr. Claudia Imhoff, Dr. Robin Bloor and SAS
Live Webcast on Jan. 14, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=9431631f43a8c7561f2ba996750a4612
When resources are scarce, organizations focus heavily on keeping processes intact and costs down. The result is often a cycle of decisions that hinders development and ultimately leads to zero innovation. But these days, the market is teeming with game-changing solutions with more attractive price points, paving the way toward a new mindset and an era of abundance.
Register for this episode of Hot Technologies to learn from veteran Analysts Claudia Imhoff and Robin Bloor as they discuss how the proliferation of data and analytics is forcing the enterprise to rethink and redesign its architecture. They’ll be briefed by Gary Spakes of SAS, who will explain his company’s approach to Big Data analytics. He will show how disruptive technologies like Hadoop can give organizations the scalability and reliability they need, and at the same time boost data discovery, analytic innovation and time-to-value.
Visit InsideAnalysis.com for more information.
What's in store for Big Data in 2015? Will the 'Internet of Things' fuel the Industrial Internet? Will Big Data get Cloudy? Check out the top five Big Data predictions for 2015 according to Quentin Gallivan, CEO, Pentah0
Getting Started with Big Data for Business ManagersDatameer
Big Data has become critical to the enterprise because of the massive amount of untapped data sources, and the potential to gain new insights that were previously not possible. So, how to get started with Big Data and Hadoop becomes a question more pertinent than ever before.
Listen to leading analyst at Ovum, Tony Baer, as he discusses answers to the key questions around how to:
Approach Big Data and associated business challenges
-- Identify what types of new insights can be revealed by Big Data
-- Staff for this undertaking and implement the technology necessary to be successful
-- Take the first steps toward getting started with Big Data on Hadoop
Dark Data Discovery & Governance with File AnalysisCraig Adams
Discover and classify your Data Data and deliver Information Govenrance on your unstructured data held in Exhchange, File Shares, SharePoint, Documetum, FileNet, OpentText etc. Make your Digital Landfill a thing of the past.
This presentation was given by MapR CMO Jack Norris at Gartner BI and Analytics Summit in las Vegas on April 2, 2014.
Hadoop revolutionizes how data is stored processed and analyzed. Hadoop represents a new data and compute stack that provides huge operational advantages and is being used to change how organizations compete. This session will provide an overview of how customers are using Hadoop today through details on initial uses and a glimpse of how this new platform is providing organizations 10X performance at 1/10 the cost
Fast Data Mining: Real Time Knowledge Discovery for Predictive Decision MakingCodemotion
Fast Data as a different approach to Big Data for managing large quantities of “in-flight” data that help organizations get a jump on those business-critical decisions. Difference between Big Data and Fast Data is comparable to the amount of time you wait downloading a movie from an online store and playing the dvd instantly.
Data Mining as a process to extract info from a data set and transform it into an understandable structure in order to deliver predictive, advanced analytics to enterprises and operational environments.
The combination of Fast Data and Data Mining are changing the “Rules”
The Power of your Data Achieved - Next Gen ModernizationHortonworks
Fueled by ever-changing customer behaviors and an increasing number of industry disruptions, the modern enterprise requires analytics to stay ahead of the game. Today’s data warehouse needs continuous enhancements to address new requirements for advanced analytics, real-time streaming data, Big Data, and unstructured data. The focus should be on developing a forward-looking, future-proof view and holistically addressing the combination of forces that are impacting the existing operational model.
Examples of how leading healthcare organizations use analytics to deliver better clinical and business outcomes. These slides were put together by Jack Phillips Co-founder & CEO of the International Institute for Analytics and Tom Davenport, IIA Director of Research and Visiting Professor of Harvard Business School
For more information on how your healthcare company can be helped using analytics follow this link to take the DELTA-Powered Analytics Assessment TM. It measures how well healthcare providers use data for strategic decision making.
http://info.iianalytics.com/healthcarebenchmarking
To take action before IT security attacks become critical, organizations need the analytics capabilities necessary to identify anomalous and suspicious behavior quickly.Our Anomalous Behavior Detection Solution addresses security issues that conventional methods can’t. It can help to detect and prevent theft of data or intellectual property (IP), for instance at the behest of nation states, organized crime, or by a disenchanted employee. It can quickly identify when a user is behaving in a way that is abnormal for them and take appropriate action to limit what they can do, or flag up the situation for managerial attention. It can also predict when anomalous behavior is likely to occur, flagging events of interest for further investigation for potential security breach.
Redington Value Distribution's ‘Value Journal’, a monthly news journal whose purpose is to update the channel on the latest vendor news and Redington Value’s Channel Initiatives.
Key stories from the September Edition:
• HPE Acquires SGI for $ 275 Million
• Dell-EMC deal set to close in September
• Oracle Minicluster S7-2 Engineered System
• Trend Micro Achieves recommended status from NSS Labs
• Cyberark secures enterprise cloud orchestration and automation
• Fortinet launches universal wireless access points
Terminology guide for digital health in 2021Velametis
It is no surprise that 2020 contributed to a surge in digital healthcare-related activities, in light of the COVID-19 pandemic.
At Velametis, we anticipate a continued growth in the industry in 2021, so we collated the most important digital healthcare terms and definitions you will come across this year.
Find out more @ https://velametis.com/
The flexibility of Apache Hadoop is one of its biggest assets – enabling businesses to generate value from data that was previously considered too expensive to be stored and processed in traditional databases – but also results in Hadoop meaning different things to different people. In this session 451 Research’s Matt Aslett will explore the impact that Hadoop is having on the traditional data processing landscape, examining the expanding ecosystem of vendors and their relationships with Apache Hadoop, investigating the increasing variety of Hadoop use-cases, and exploring adoption trends around the world.
Best Practices for Big Data Analytics with Machine Learning by DatameerDatameer
Don't forget! You can watch the full Datameer recording here:
http://info.datameer.com/Online-Slideshare-Big-Data-Analytics-Machine-Learning-OnDemand.html
Learn through industry use cases, how to empower users to identify patterns & relationships for recommendations using big data analytics.
This is a re-boot of a presentation originally given on the potential role of cloud infrastructure in healthcare delivery from eHealth Canada 2012.
Key concepts are the drivers of change in healthcare, how hospitals can protect themselves when using of cloud, the potential use of enterprise content management as part of healthcare delivery and the current models that we are seeing in Canada and the US.
Presumption of Abundance: Architecting the Future of SuccessInside Analysis
Hot Technologies with Dr. Claudia Imhoff, Dr. Robin Bloor and SAS
Live Webcast on Jan. 14, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=9431631f43a8c7561f2ba996750a4612
When resources are scarce, organizations focus heavily on keeping processes intact and costs down. The result is often a cycle of decisions that hinders development and ultimately leads to zero innovation. But these days, the market is teeming with game-changing solutions with more attractive price points, paving the way toward a new mindset and an era of abundance.
Register for this episode of Hot Technologies to learn from veteran Analysts Claudia Imhoff and Robin Bloor as they discuss how the proliferation of data and analytics is forcing the enterprise to rethink and redesign its architecture. They’ll be briefed by Gary Spakes of SAS, who will explain his company’s approach to Big Data analytics. He will show how disruptive technologies like Hadoop can give organizations the scalability and reliability they need, and at the same time boost data discovery, analytic innovation and time-to-value.
Visit InsideAnalysis.com for more information.
What's in store for Big Data in 2015? Will the 'Internet of Things' fuel the Industrial Internet? Will Big Data get Cloudy? Check out the top five Big Data predictions for 2015 according to Quentin Gallivan, CEO, Pentah0
Getting Started with Big Data for Business ManagersDatameer
Big Data has become critical to the enterprise because of the massive amount of untapped data sources, and the potential to gain new insights that were previously not possible. So, how to get started with Big Data and Hadoop becomes a question more pertinent than ever before.
Listen to leading analyst at Ovum, Tony Baer, as he discusses answers to the key questions around how to:
Approach Big Data and associated business challenges
-- Identify what types of new insights can be revealed by Big Data
-- Staff for this undertaking and implement the technology necessary to be successful
-- Take the first steps toward getting started with Big Data on Hadoop
Dark Data Discovery & Governance with File AnalysisCraig Adams
Discover and classify your Data Data and deliver Information Govenrance on your unstructured data held in Exhchange, File Shares, SharePoint, Documetum, FileNet, OpentText etc. Make your Digital Landfill a thing of the past.
This presentation was given by MapR CMO Jack Norris at Gartner BI and Analytics Summit in las Vegas on April 2, 2014.
Hadoop revolutionizes how data is stored processed and analyzed. Hadoop represents a new data and compute stack that provides huge operational advantages and is being used to change how organizations compete. This session will provide an overview of how customers are using Hadoop today through details on initial uses and a glimpse of how this new platform is providing organizations 10X performance at 1/10 the cost
Fast Data Mining: Real Time Knowledge Discovery for Predictive Decision MakingCodemotion
Fast Data as a different approach to Big Data for managing large quantities of “in-flight” data that help organizations get a jump on those business-critical decisions. Difference between Big Data and Fast Data is comparable to the amount of time you wait downloading a movie from an online store and playing the dvd instantly.
Data Mining as a process to extract info from a data set and transform it into an understandable structure in order to deliver predictive, advanced analytics to enterprises and operational environments.
The combination of Fast Data and Data Mining are changing the “Rules”
The Power of your Data Achieved - Next Gen ModernizationHortonworks
Fueled by ever-changing customer behaviors and an increasing number of industry disruptions, the modern enterprise requires analytics to stay ahead of the game. Today’s data warehouse needs continuous enhancements to address new requirements for advanced analytics, real-time streaming data, Big Data, and unstructured data. The focus should be on developing a forward-looking, future-proof view and holistically addressing the combination of forces that are impacting the existing operational model.
Examples of how leading healthcare organizations use analytics to deliver better clinical and business outcomes. These slides were put together by Jack Phillips Co-founder & CEO of the International Institute for Analytics and Tom Davenport, IIA Director of Research and Visiting Professor of Harvard Business School
For more information on how your healthcare company can be helped using analytics follow this link to take the DELTA-Powered Analytics Assessment TM. It measures how well healthcare providers use data for strategic decision making.
http://info.iianalytics.com/healthcarebenchmarking
To take action before IT security attacks become critical, organizations need the analytics capabilities necessary to identify anomalous and suspicious behavior quickly.Our Anomalous Behavior Detection Solution addresses security issues that conventional methods can’t. It can help to detect and prevent theft of data or intellectual property (IP), for instance at the behest of nation states, organized crime, or by a disenchanted employee. It can quickly identify when a user is behaving in a way that is abnormal for them and take appropriate action to limit what they can do, or flag up the situation for managerial attention. It can also predict when anomalous behavior is likely to occur, flagging events of interest for further investigation for potential security breach.
Redington Value Distribution's ‘Value Journal’, a monthly news journal whose purpose is to update the channel on the latest vendor news and Redington Value’s Channel Initiatives.
Key stories from the September Edition:
• HPE Acquires SGI for $ 275 Million
• Dell-EMC deal set to close in September
• Oracle Minicluster S7-2 Engineered System
• Trend Micro Achieves recommended status from NSS Labs
• Cyberark secures enterprise cloud orchestration and automation
• Fortinet launches universal wireless access points
Terminology guide for digital health in 2021Velametis
It is no surprise that 2020 contributed to a surge in digital healthcare-related activities, in light of the COVID-19 pandemic.
At Velametis, we anticipate a continued growth in the industry in 2021, so we collated the most important digital healthcare terms and definitions you will come across this year.
Find out more @ https://velametis.com/
The flexibility of Apache Hadoop is one of its biggest assets – enabling businesses to generate value from data that was previously considered too expensive to be stored and processed in traditional databases – but also results in Hadoop meaning different things to different people. In this session 451 Research’s Matt Aslett will explore the impact that Hadoop is having on the traditional data processing landscape, examining the expanding ecosystem of vendors and their relationships with Apache Hadoop, investigating the increasing variety of Hadoop use-cases, and exploring adoption trends around the world.
Best Practices for Big Data Analytics with Machine Learning by DatameerDatameer
Don't forget! You can watch the full Datameer recording here:
http://info.datameer.com/Online-Slideshare-Big-Data-Analytics-Machine-Learning-OnDemand.html
Learn through industry use cases, how to empower users to identify patterns & relationships for recommendations using big data analytics.
This is a re-boot of a presentation originally given on the potential role of cloud infrastructure in healthcare delivery from eHealth Canada 2012.
Key concepts are the drivers of change in healthcare, how hospitals can protect themselves when using of cloud, the potential use of enterprise content management as part of healthcare delivery and the current models that we are seeing in Canada and the US.
This presentation is about the Internet of Things - the interaction of everyday objects with users in the physical world via connectivity and data. It is expected that by 2020 a number of IoT gadgets will multiply to 50 bln worldwide (to 8 gadgets per person). GlobalLogic makes its contribution to this process developing innovative IoT projects in such areas as automotive, wearables, healthcare etc.
This presentation by Vitalii Vashchuk (Manager, Sales Enablement, GlobalLogic) was delivered at Outsource People Conference in Kyiv (October 24, 2015).
Tech trends at SXSW & CES 2015 - IoT, Wearable, Sensor, Connected…- Mariko Nishimura
Tech trends at SXSW & CES 2015 - IoT, Wearable, Sensor, Connected…- [at] KMD Graduate School of Media Design, Keio University 2015.06.26.
Mariko Described Tech & Industry trends at SXSW, CES regarding her original view.
An IoT13 presentation showcasing promising companies in the internet of things. Thomas Nicholls outlines the beautifully simple way that Sigfox sets out to enable the Internet of Things
Internet of Things Connectivity for Embedded Devicesmentoresd
Slides presented at "Internet of Things Connectivity for Embedded Devices" live event by Mentor Graphics Embedded Software and Nano Power Communication. See the live event here: https://plus.google.com/u/0/events/cfgduqagg4r5l871uogca4ujea0
Please contact embedded_software@mentor.com for any questions or inquiries.
Embedded systems are becoming interconnected and accessible via the internet. Gartner Group estimates there will be nearly 26 billion devices that make up the Internet of Things by 2020. This results in a massive variety of connected devices with varying security, reliability, and authentication requirements. Cost sensitivity also figures into the equation. This mix of requirements and costs require IoT developers to identify sensor, processor, and software solutions that address the requirements and hit required price points. Join us as IoT solution experts discuss sensors, connectivity, processors, platforms, and software for IoT applications and overview applications of IoT in various markets.
Watch for free on-demand http://ecast.opensystemsmedia.com/511
Building the Internet of Things with Thingsquare and Contiki - day 1, part 1Adam Dunkels
How to build the Internet of Things - what is an Internet of things device and how do we connect it? This is the first Thingsquare IoT workshop slide deck.
How do APIs and IoT relate? The answer is not as simple as merely adding an API on top of a dumb device, but rather about understanding the architectural patterns for implementing an IoT fabric. There are typically two or three trends:
Exposing the device to a management framework
Exposing that management framework to a business centric logic
Exposing that business layer and data to end users.
This last trend is the IoT stack, which involves a new shift in the separation of what stuff happens, where data lives and where the interface lies. For instance, it's a mix of architectural styles between cloud, APIs and native hardware/software configurations.
Case Study 1 Is Business Ready for Wearable ComputersWearable .docxdewhirstichabod
Case Study 1: Is Business Ready for Wearable Computers?
Wearable computing is starting to take off. Smartwatches, smart glasses, smart ID badges, and activity trackers promise to change how we go about each day and the way we do our jobs. According to Gartner Inc., sales of wearables will increase from 275 million units in 2016 to 477 million units by 2020. Although smartwatches such as the Apple Watch and fitness trackers have been successful consumer products, business uses for wearables appear to be advancing more rapidly. A report from research firm Tractica projects that worldwide sales for enterprise wearables will increase exponentially to 66.4 million units by 2021.
Doctors and nurses are using smart eyewear for hands-free access to patients’ medical records. Oil rig workers sport smart helmets to connect with land-based experts, who can view their work remotely and communicate instructions. Warehouse managers are able to capture real-time performance data using a smartwatch to better manage distribution and fulfillment operations. Wearable computing devices improve productivity by delivering information to workers without requiring them to interrupt their tasks, which in turn empowers employees to make more-informed decisions more quickly.
Wearable devices are helping businesses learn more about employees and the everyday workplace than ever before. New insights and information can be uncovered as IoT sensor data is correlated to actual human behavior. Information on task duration and the proximity of one device or employee to another, when combined with demographic data, can shed light on previously unidentified workflow inefficiencies. Technologically sophisticated firms will understand things they never could before about workers and customers; what they do every day, how healthy they are, where they go, and even how well they feel. This obviously has implications for protecting individual privacy, raising potential employee (and customer) fears that businesses are collecting sensitive data about them. Businesses will need to tread carefully.
Global logistics company DHL worked with Ricoh, the imaging and electronics company, and Ubimax, a wearable computing services and solutions company, to implement “vision picking” in its warehouse operations. Location graphics are displayed on smart glasses guiding staffers through the warehouse to both speed the process of finding items and reduce errors. The company says the technology delivered a 25 percent increase in efficiency. Vision picking gives workers locational information about the items they need to retrieve and allows them to automatically scan retrieved items. Future enhancements will enable the system to plot optimal routes through the warehouse, provide pictures of items to be retrieved (a key aid in case an item has been misplaced on the warehouse shelves), and instruct workers on loading carts and pallets more efficiently.
Google has developed Glass Enterprise Edition smar.
These Fascinating Examples Show Why Streaming Data And Real-Time Analytics Ma...Bernard Marr
With the explosion of data in recent years, there’s more emphasis on data-based decision-making for all companies. But what if we could process data and act on it in real-time? What if we could be proactive instead of reactive to improve performance?
In today’s globalized, competitive marketplace, being able to leverage technology to deliver faster turnaround times, meet lower pricing goals and provide customizable options can mean the difference between sustainability and irrelevancy. In this ebook, we’ll explore some of the leading solutions transforming the manufacturing industry:
- Automation for cost savings
- 3D printing for improved productivity
- Smart data for quality assurance
- Connectivity for safety and communication
- Security solutions to protect it all
Learn more: http://ms.spr.ly/6006Twegg
The Augmented Analytics Reset In RetailBernard Marr
Retail has undergone a tremendous amount of turmoil in recent years. The Covid-19 pandemic has fundamentally altered the way consumers shop, with current global supply chain issues only adding to the chaos. Online retail has experienced a huge surge which has led to challenges around coping with scaling to meet customer demand and issues such as needing to process a vastly increased number of returns.
Many consumer guides have been written outlining how Internet of Things technologies might apply to individuals’ lives, but not much exists to give executives and project managers an overview before embarking on the business of the Internet of Things (IoT) and Machine-to-Machine (M2M) communications.
That’s why Aeris has written an eBook that focuses on how the burgeoning IoT ecosystem impacts business. We know that to get started with IoT and M2M for your business, you’ll need a basic understanding of what makes it all work.
New Report by Jessica Groopman
The digitalization of our physical world—what many are now calling the ‘Internet of
Things’—is challenging our expectations of privacy.
Adding sensors to ourselves, and to the objects and places around us, renders our
physical world communicable, contextual, and trackable. The full implications of
ubiquitous connectivity remain blurry, but Altimeter Group’s survey of 2,062 American
consumers makes one point crystal clear: Consumers are decidedly anxious about
how companies use and share data from their connected devices. Our research finds
a massive gulf between consumer awareness and industry practices when it comes
to privacy. But this data reveals more than a concerned citizenry, it reveals tremendous
opportunities for companies to foster more trusted customer relationships.
DOWNLOAD THE COMPLETE REPORT:
http://pages.altimetergroup.com/1506-Consumer-Perceptions-of-Privacy-in-the-IOT-Report.html
Enhancing Employee Productivity and Qualtiy of Life with Big DataInnovations2Solutions
IFMA and Sodexo collaborated to sponsor and host a Future of Work Roundtable conversation on the challenges and opportunities surrounding these questions at IFMA’s Facility Fusion 2015 conference in Orlando in April 2015. The Roundtable was facilitated by Dr. James Ware, Executive Director of The Future of Work...unlimited, Global Research Director for Occupiers Journal Limited, and immediate past president of IFMA’s Corporate Real Estate Council. Jim also prepared this summary of the roundtable conversation.
Using digital technology to your advantage. Should you focus on improving customer experience or new products and services or your core business operations?
Modernizing Insurance Data to Drive Intelligent DecisionsCognizant
To thrive during a period of unprecedented volatility, insurers will need to leverage artificial intelligence to make faster and better business decisions - and do so at scale. For many insurers, achieving what we call "intelligent decisioning" will require them to modernize their data foundation to draw actionable insights from a wide variety of both traditional and new sources, such as wearables, auto telematics, building sensors and the evolving third-party data landscape.
EO Briefing 2015 is structured in three chapters. The first chapter examines the impact of digital technologies, particularly the Internet of Things (IoT) on business. The IoT presents an array of challenges and new revenue possibilities but the question is which companies will be able to capitalise on this opportunity. This an especially crucial question as C-suite executives see competition rising sharply in 2015.
Потребности:Надежное, экономически выгодное и простое в обращении решение для резервного копирования
Среда: VMware и Hyper-V, более, чем10 TБданных.
Предыдущее решение:
Очень дорогое и сложное решение для резервного копирования
Проблема: нехватка бюджета на нужды ИТ
Область применения:Социальные услуги.
Потребности:Сократить время и сложность бэкапа виртуальных машин.
Среда:45 ВМ, 1.5 TБданных, Windows Domain Controller, Lotus Notes, NAS
Область применения:ИТ услуги
Потребности:надежная система резервного копирования, нацеленная на сокращение времени простоя в случае отказа сервера.
Среда:множествоГипервизоров, WEBсайтов, ERP систем.
Hpe Data Protector Disaster Recovery GuideAndrey Karpov
This chapter provides a general overview of the disaster recovery process, explains the basic terms used in the Disaster Recovery guide and provides an overview of disaster recovery methods
Carefully follow the instructions below to prepare for disaster recovery and ensure a fast and efficient restore. The preparation procedure does not depend on the disaster recovery method, and includes developing a detailed disaster recovery plan, performing consistent and relevant backups, and updating the SRD file on Windows.
Assisted Manual Disaster Recovery (AMDR)
Manual Disaster Recovery (MDR)
This chapter contains descriptions of problems you might encounter while performing a disaster recovery. You can start with problems connected to a particular disaster recovery method and continue with general disaster recovery problems.
Example Preparation Tasks
Hpe Zero Downtime Administrator's GuideAndrey Karpov
Part 1: HPE P4000 SAN Solutions
This part describes how to configure the Data Protector HPE P4000 SAN Solutions integration. For information on how to perform zero downtime backup and instant recovery using the HPE P4000 SAN Solutions integration, see the HPE Data Protector Integration Guide for Microsoft Volume Shadow Copy Service.
Part 2: HPE P6000 EVA Disk Array Family
This part describes how to configure the Data Protector HPE P6000 EVA Disk Array Family integration, how to perform zero downtime backup and instant recovery using the HPE P6000 EVA Disk Array Family integration, and how to resolve the integration-specific Data Protector problems
Part 3: HPE P9000 XP Disk Array Family
This part describes how to configure the Data Protector HPE P9000 XP Disk Array Family integration, how to perform zero downtime backup and instant recovery using the HPE P9000 XP Disk Array Family integration, and how to resolve the integration-specific Data Protector problems.
Part 4: HPE 3PAR StoreServ Storage
This part describes how to configure the Data Protector HPE 3PAR StoreServ Storage integration, and how to perform zero downtime backup and instant recovery using the HPE 3PAR StoreServ Storage integration through native storage system support built-in in the Data Protector HPE P6000 / HPE 3PAR SMI-S Agent. For information on how to perform zero downtime backup and instant recovery using the HPE 3PAR StoreServ Storage integration through the Data Protector Microsoft Volume Shadow Copy Service integration, see the HPE Data Protector Integration Guide for Microsoft Volume Shadow Copy Service.
Part 5: EMC Symmetrix
This part describes how to configure the Data Protector EMC Symmetrix integration, how to perform zero downtime backup and instant recovery using the EMC Symmetrix integration, and how to resolve the integration-specific Data Protector problems.
Part 6: NetApp Storage
This part describes how to configure the Data Protector NetApp Storage integration, how to perform zero downtime backup using the NetApp Storage system, and how to resolve the integration-specific Data Protector problems.
Part 7: EMC VNX Family
This part describes how to configure the Data Protector EMC VNX Family integration, how to perform zero downtime backup using the EMC VNX storage system, and how to resolve the integration-specific Data Protector problems.
Part 8: EMC VMAX Family
This part describes how to configure the Data Protector EMC VMAX Family integration, how to perform zero downtime backup using the EMC VMAX storage system, and how to resolve the integration-specific Data Protector problems.
Introducing Backup to Disk devices and deduplication
This document describes how HPE Data Protector integrates with Backup to Disk devices and deduplication. By supporting deduplication, several new concepts are introduced to Data Protector, including a new device type, the Backup to Disk device, and four interface types: the HPE StoreOnce Software deduplication, the HPE StoreOnce Backup System, Smart Cache, and the EMC Data Domain Boost. Backup to Disk devices and deduplication are both discussed in detail in this document.
Backup to Disk devices are devices that back up data to a physical storage disk and support multi-host configurations. They support different backends such as the HP StoreOnce Software deduplication, the StoreOnce Backup system, Smart Cache, or the EMC Data Domain Boost. This document also describes the basic principles behind deduplication technology.
Data Protector supports the following deduplication backends:
HPE Data Protector Software deduplication provides the ability to deploy target-side deduplication on virtually any industry-standard hardware, offers greater flexibility than existing solutions as it can be deployed in a wider range of hardware set-ups, and provides enterprise-class scalability.
Because of the way Data Protector makes use of the extremely efficient HPE StoreOnce engine, Data Protector software deduplication uses memory very efficiently. As a result, you can deploy deduplication on application or backup servers without lowering application performance. Data Protector software deduplication can even be deployed on a virtual machine. In addition, Data Protector software deduplication delivers very high throughput. HPE StoreOnce Backup system devices are disk to disk (D2D) backup devices which support deduplication. Smart Cache devices are backup to disk devices that enable non-staged recovery from VMware backups. EMC Data Domain Boost devices are D2D backup devices which support deduplication.
Hpe Data Protector troubleshooting guideAndrey Karpov
How to troubleshoot
To solve problems quickly and efficiently:
1.Make yourself familiar with the general troubleshooting information.
2.Check if your problem is described in the HPE Data Protector Help file or the troubleshooting sections of applicable guides:
To troubleshoot installation and upgrade, see the HPE Data Protector Installation Guide.
To troubleshoot application integration sessions, see the HPE Data Protector Integration Guide.
To troubleshoot zero downtime backup and instant recovery, see the HPE Data Protector Zero Downtime Backup Administrator's Guide and HPE Data Protector Zero Downtime Backup Integration Guide.
To troubleshoot disaster recovery, see the HPE Data Protector Disaster Recovery Guide.
Overview of the installation procedure
Chapter 2: Installing Data Protector
This chapter contains detailed instructions about:
Installing the Data Protector Cell Manager and Installation Servers
Installing the Data Protector Single Server Edition
Installing the Data Protector web reporting
Chapter 3: Installing Data Protector clients
Chapter 4: Installing the Data Protector integration clients
Chapter 5: Installing Data Protector on Clusters
Chapter 6: Maintaining the installation
Chapter 7: Upgrading the Data Protector
Chapter 8: Data Protector Licensing
Chapter 9: Troubleshooting installation and upgrade
Part 1: IBM Applications
This part of the guide describes ways to back up and restore Informix Server database objects, DB2 databases, and Lotus Notes/Domino Server.
This part includes the following chapters:
Data Protector Informix Server integration
Data Protector DB2 UDB integration
lData Protector Lotus Notes/Domino Server integration
Part 2:
Microsoft Applications
This part of the guide describes ways to configure and use the following:
Data Protector Microsoft SQL Server integration
Data Protector Microsoft SQL Server 2007/2010/2013 integration
Data Protector Microsoft SharePoint Server VSS based solution
Data Protector Microsoft Exchange Server 2007 integration
Data Protector Microsoft Exchange Server 2010 integration
Data Protector Microsoft Exchange Single Mailbox integration
Part 3:
Oracle and SAP
This part of the guide describes ways to configure and use the following:
Data Protector Oracle Server integration
Data Protector MySQL integration
Data Protector SAP R/3 integration
Data Protector SAP MaxDB integration
Data Protector SAP HANA Appliance integration
Part 4:
Sybase and Network Data Management Protocol Server
This part of the guide describes ways to configure and use the following:
Sybase Server integration
Network Data Management Protocol Server integration
NetApp SnapManager solution
Part 5:
Virtualization
This part of the guide describes ways to back up VMware virtual machines and Microsoft Hyper-V data online.
This part includes the following chapters:
Data Protector Virtual Environment integration for VMware
Data Protector Virtual Environment integration for Microsoft Hyper-V
Part 6:
PostgreSQL
This part of the guide describes Data Protector integration.
This part includes the following chapter:
Data Protector PostgreSQL integration
VM Explorer® is a simple but powerful software to back up, replicate and restore your VMware ESX, ESXi and Microsoft Hyper-V Virtual Machines (VM).
The following documentation explains the main tasks required for configuration and daily use of VM Explorer®. All services hereinafter are brought to you by HPE.
The HPE services and materials presented for VM Explorer® hereinafter are protected by copyright, trademark, trade dress, unfair competition, and other intellectual property rights. The trademarks, logos and marks of HPE and VM Explorer® displayed on the services and products are the property of HPE or third parties. You are not permitted to use the Marks without the prior consent of HPE or the third party that may own the Marks.
Трансформация ИТ с помощью Hewlett Packard Enterprise
ИТ для экономики идей
Идеи всегда являлись залогом успеха в развития бизнеса. Однако одних лишь хороших идей мало. Успех определяется тем, насколько быстро компания может превращать идеи в прибыль. Сегодня путь от идеи до ее реализации радикально сократился. Именно поэтому, говоря об особенностях современного этапа развития экономики, эксперты рынка все чаще используют термин «экономика идей».
Building and managing secure private and hybrid clouds
HP Helion extends beyond just cloud to become the very fabric of your enterprise. Delivers an extensible and open portfolio to build and manage enterprise grade end-to-end orchestrated cloud services.
HPE Data Protector Administrator's GuideAndrey Karpov
About Data Protector
HPE Data Protector is a backup solution that provides reliable data protection and high accessibility for your
fast-growing business data. Data Protector offers comprehensive backup and restore functionality
specifically tailored for enterprise-wide and distributed environments.
Major Data Protector features
l Scalable and highly flexible architecture
l Mixed environment support
l Easy central administration
l High performance backup
l Easy restore
l Data and control communication security
l High availability support
l Automated or unattended operation
l Monitoring, reporting, and notification
l Service management
l Integration with online database applications
l Integration with other products
Data Protector Architecture
l Software Version number, which indicates the software version.
l Document Release Date, which changes each time the document is updated.
l Software Release Date, which indicates the release date of this version of the software.
To check for recent updates or to verify that you are using the most recent edition of a document, visit the
Knowledge Base on the HPE Big Data Customer Support Site.
Конференция по программным решениям HPE 2016Andrey Karpov
Конференция по программным решениям HPE 14 апреля 2016
Автоматизация процесса перевода транспортного сервиса между географически распределенными площадками
Система управления ТСЭР
Подсистема автоматизации процедуры перевода обработки
ЭС между центральными серверами ЦТУ ТСЭР (ПАПО)
Автоматизация процессов управления DevOps Новые реалии – новая скорость
March 2016 HPE Data Protector
Comprehensive data protection for the modern enterprise
If you pick up the latest datacenter trends reports from ESG, Gartner, and IDC, you will notice that improving backup and recovery appears among the top IT priorities for organizations. The reason for that is simple: as the velocity, variety and complexity of data continue to accelerate, so do the risks of not being able to speedily restore critical systems and applications in case of disaster or data loss.
HP Distributed R is a high-performance scalable platform for the R language. It enables R to
leverage multiple cores and multiple servers to perform Big Data Advanced Analytics. It consists of
new R language constructs to easily parallelize algorithms across multiple R processes.
HP Distributed R simplifies large-scale analysis by extending R. Because R is a single-threaded
environment, it has limited utility for Big Data analytics. HP Distributed R allows you to specify that
parts of programs be run in multiple single-threaded R-processes. This approach results in
significantly reduced execution times for Big Data analysis.
This document describes the functions performed by an HP Vertica database administrator (DBA).
Perform these tasks using only the dedicated database administrator account that was created
when you installed HP Vertica. The examples in this documentation set assume that the
administrative account name is dbadmin.
l To perform certain cluster configuration and administration tasks, the DBA (users of the
administrative account) must be able to supply the root password for those hosts. If this
requirement conflicts with your organization's security policies, these functions must be
performed by your IT staff.
l If you perform administrative functions using a different account from the account provided
during installation, HP Vertica encounters file ownership problems.
l If you share the administrative account password, make sure that only one user runs the
Administration Tools at any time. Otherwise, automatic configuration propagation does not
work correctly.
l The Administration Tools require that the calling user's shell be /bin/bash. Other shells give
unexpected results and are not supported.
HP Vertica Analytic Database
Creating flex tables is similar to creating other tables, except column definitions are optional. When
you create flex tables, with or without column definitions, HP Vertica implicitly adds a special
column to your table, called __raw__. This is the column that stores loaded data. The __raw__
column type is LONG VARBINARY, and its default maximum width is 130000 bytes (with an
absolute maximum of 32000000 bytes). You can change the width default with the
FlexTablesRawSize configuration parameter.
Loading data into a flex table encodes the record into a map type, and populates the __raw__
column. The map type is a standard dictionary type, pairing keys with string values as virtual
columns.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
2. Executive summary
Personal health monitors tracking your fitness, trashcans monitoring their fullness, watches telling you more
than just the time, and agricultural soil monitors saying it’s time to water. It seems a day doesn’t go by that
we don’t hear about the latest “offline” thing, device, or equipment becoming “online,” moving from isolation
to being connected to the Internet of Things (IoT). It’s clear that integrating sensors, electronics, and
network connectivity into devices can enable innovation, enhancing and extending the way we work and
interact with each other and the world around us.
McKinsey estimates that IoT has a total potential economic
impact of $3.9 trillion USD to $11.1 trillion USD a year by 2025.1
Business white paper
1
The Internet of Things: Mapping the Value Beyond the Hype, McKinsey Global Institute, June 2015.
Executive summary
The value
The business
opportunities
The challenges
Your partner
Where to start
3. Gartner estimates a potential of 26 billion connected devices by 2020.2
Morgan Stanley even estimates a
potential of 75 billion connected devices by 2020.3
More important than the device estimate, though, are
the possible industry transformations and business outcomes. So, the questions we should ask ourselves as
business leaders, technologists, and entrepreneurs are:
Global sensor and device connectivity presents countless opportunities for transformation of businesses
and industries, disrupting the status quo and existing marketplaces. Companies should capitalize on the
opportunity, but need to do it smartly—mitigating financial and operational risks. So, understanding how value
can be created from IoT, identifying the business opportunities, and understanding the challenges and the
technologies needed to mitigate them are the keys to success.
With our long-standing experience and expertise as a trusted technology partner to enterprises across
every industry globally, we will guide you through these important questions so you can maximize the
benefit and minimize the risk from IoT investments. In this paper, we’ll share our perspectives on the value,
the business opportunities, the technology challenges, and the ideal partner to seize the opportunity.
Business white paper
2
Forecast: The Internet of Things, Worldwide, 2013, Gartner, November 2013.
3
Morgan Stanley: 75 Billion Devices Will Be Connected To The Internet Of Things By 2020, Business Insider, Tony Danova, October 2013.
Where is the value? What are the business
opportunities?
What are the challenges to
unlock the promise of IoT?
Executive summary
The value
The business
opportunities
Your partner
The challenges
Where to start
4. The value
In order to justify the investment in RD, infrastructure, and governance required for IoT, senior executives
are demanding to understand the value to the business. In our experience, value can be expressed across
three dimensions—contextual, integrated, and operational.
It should come as no surprise that the primary value of IoT is in the data, more specifically, the enhanced
decision-making and automation that arise from the convergence of analytical insights, enabled by
connected devices, with people. IDC predicts IoT data will account for 10 percent of the world’s data by
2020, of about 44 zettabytes.4
Clearly this is Big Data. But what can this data tell us that we didn’t already
know and how can we use this information to improve our well-being and optimize businesses?
Business white paper
4
The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things, IDC, April 2014.
Executive summary
The value
The business
opportunities
Your partner
The challenges
Where to start
5. Contextual insights
Your health-monitoring wristband, the vending machine at the mall, aircraft engine serial number 9AB429—
these are all examples of devices that are being instrumented with sensors and connected. What is
important to point out about these and every sensor-enabled connected device is the notion of contextual
data, the data each device creates, and contextual insights, the inference that can be made about the
device and environment through analysis of the data. With contextual data, we can start gaining insights and
creating value where previously not possible.
Let’s take, for instance, your health-monitoring wristband. Embedded into this device is an accelerometer,
which is used to sense movement. As you move, contextual data about your personal activity, such as
calories burned, steps taken, and your heart rate is measured, stored, and presented to you through an app
so you can monitor, maintain, and improve your personal health fitness. Should you miss a few sessions at
the gym, data analytics can create contextual insights, determining this trend and having the device send
you an encouraging text that it’s time for a run, helping improve your well-being.
Vending machines can be embedded with pressure sensors and counters collecting contextual data on
real-time inventory levels, which are made available to an operations manager or fed directly into an order
management system. When inventory reaches a specified threshold, local distribution can be automatically
dispatched to refill, ensuring no loss in revenue from stock outs and optimizing profit for the business.
Big Data analytics can even enable contextual insights on historical buying patterns and consumer
preferences, which lead to improved demand forecasting and product planning.
Across all industries, there are billions of devices and equipment essential for operations. For instance, in the
aviation industry, aircraft engines have a vital role in the operations of the airline. Airlines rely on the uptime
and performance of their engines and any unplanned downtime (commonly referred to as “time off wing”)
causes delays to flights—which we all know can lead to a domino effect—negatively impacting customer
experiences and future revenue.
Business white paper
Executive summary
The value
The business
opportunities
Your partner
The challenges
Where to start
6. Unplanned downtime costs a liquid natural gas (LNG) facility on
average about $150 million USD annually.5
By instrumenting industrial equipment such as engines, pumps, valves, and other critical assets with sensors
and collecting and monitoring contextual data such as temperature, power, and pressure, enterprises can
have the data they need to analyze and gain insights about the equipment (status, performance, and health)
while in operation. By analyzing the data in real time, enterprises can have contextual insights delivered
through advanced notification of potential problems in their equipment. So, in turn, unplanned “time off
wing” for the airlines can be minimized, improving operations and revenue, and, of course, getting us to our
destination safely and on time.
Contextual insights are only one part of the potential value of IoT. When we start considering how individual
devices and equipment fit in an ecosystem of other connected devices within the business operations, an
even larger opportunity for value creation can be possible.
Integrated intelligence
Let’s look back to the personal health-monitoring example. While there is value for each person in tracking
their own health metrics, imagine the possibilities when you start combining and correlating data across
disparate data sets and large populations, creating integrated intelligence. For instance, healthcare
providers aspire to provide the highest quality of care for their patients (while, of course, maintaining their
balance sheet). If doctors had access to their patients’ personal health-monitoring data, they could have a
much better understanding of the historical and real-time health and wellness of all their patients and even
populations. There could be a future where treatments and prescription dosages are personalized and
precise based on real-time conditions of their patients and the environment. Through a Big Data analytics
platform, concerning trends in your blood pressure or other measurement could be uncovered and your
doctor could adjust your treatment. This data could even be of interest to pharmaceutical companies to
better understand drug interaction with environmental impacts and human activity.
Business white paper
5
Jeff Immelt on GE Oil and Gas Strategy and the Power of One Percent, ARC Advisory Group, May 2014.
Executive summary
The value
The business
opportunities
Your partner
The challenges
Where to start
7. More visibility means better operational intelligence.
Additionally, although contextual insights on each device or equipment are valuable, enterprises and
industries have more than one type of equipment critical for operations. For instance, an oil field consists of
compressors, pumps, heat exchangers, drilling rigs, artificial lifts, and more. Operators rely upon the uptime
and performance for all equipment. They also rely upon integrated intelligence, through predictive failure
notification, so that visibility into the entire production fleet will maximize operational gain.
Today, a typical oil-drilling platform might contain 30,000 sensors,
but only 1 percent is actually used for decisions.6
In transportation, automotive companies are instrumenting a vast number of sensors that provide data on
each vehicle and the environment around it. VTT Technical Research Centre of Finland has developed a
“slipperiness detection” system for vehicles utilizing standard anti-lock brake system sensors.7
By analyzing
the contextual data from the sensors, VTT can infer the slipperiness of the road based on friction, creating
contextual insights on the road condition. As contextual insights, the system can warn the driver, avoiding a
potential accident. Additionally, with respect to integrated intelligence, every vehicle across a city can report
on its environment and in aggregate we can have citywide situational awareness with endless opportunities
for transportation optimization.
Analyzing multiple discrete data sources can even uncover correlations, interdependencies, and patterns
that lead to new insights, which is not possible by analyzing sensors independently.
Business white paper
6
Unlocking the potential of the Internet of Things, McKinsey Global Institute, June 2015.
7
Slipperiness detection system, VTT Technical Research Centre of Finland, December 2013.
Executive summary
The value
The business
opportunities
Your partner
The challenges
Where to start
8. For instance, looking again at transportation. On-board diagnostics from a vehicle can collect data on
driving behavior. Just that information alone can be used to determine an insurance rating for a driver. But
by adding in information such as the car’s location, the degree of traffic on the road, the weather conditions,
and the driver’s schedule for the day and heart rate, insurance companies might infer from the integrated
intelligence that a stressed driver with a busy schedule is more likely to have an accident and provide “on
the spot” incentives—such as a rebate to drive slower or arrive at his next appointment 5 minutes late.
Meanwhile, oil refineries would prefer processing discounted crude instead of premium crude for the
financial savings, but doing so can potentially lead to corrosion and scaling of critical equipment, which are
detrimental to the expensive assets such as cooling towers and boilers. Refineries today are instrumenting
their production process to monitor water quality, flow rate, pressure, and more. A Big Data analytics
platform has the potential to find patterns across the various data sets to predict equipment degradation
based on historical data. So an optimal balance between processing discounted crude and equipment
performance for maximum financial gain can be achieved. That’s business optimization.
Operationalizing insights
New data from IoT, the analytical insights discovered, and the technology investments would be at a loss if
the insights are not actionable and delivered to the right people and systems across the enterprise at the
right time and in the right format. Therefore, it is essential that actionable insights are integrated within
business operations and for all stakeholders.
In the vending machine example, who should receive the stock out insights—local distribution for re-supply,
fleet managers for planning delivery routes, or brand managers for understanding customer preferences?
Clearly, all of them. However, given they have different roles that impact the business operations differently,
each person needs a different view of the insights. Brand managers may want historical dashboards on their
Web browser, local distribution may want predictive alerts summarizing total supply across their geography,
and fleet managers may want real-time status on a geographic information system (GIS) map overlaid with
driver GPS locations.
Business white paper
Executive summary
The value
The business
opportunities
Your partner
The challenges
Where to start
9. Let’s say there is a low supply at the stadium before a big game. An application tracks the priority events,
locations, and supply levels in real time. An operations manager, who is utilizing the dashboard, can make
better decisions about how to manage inventory levels, then relay this information to drivers through tablets
to re-route, optimizing business value. Maximizing the return on investments in a Big Data platform and IoT
technologies require delivering the actionable insights to the people and systems that can best leverage
them as well as having the right enabling technologies and tools to do so.
Through connectivity and increased automation, devices such as smart utility meters or streetlights can
be remotely monitored and controlled. IoT enables us to take operations that were formerly labor intensive,
requiring physical inspection or actuation, in sometimes hazardous environments and reimagine them in
entirely new ways.
Real-time, automated control can even further increase the value of insights. In some scenarios, with the
right confidence and security protocols, equipment and processes can self-adjust based on analytics. By
integrating multiple data sources, integrated intelligence can enable real-time, closed feedback loops. For
instance, General Electric and Siemens are manufacturers of wind turbines and by analyzing wind speed
and direction in real time, their turbines can self-adjust their pitch and rotor blade angles, maximizing
energy production.
Business white paper
Executive summary
The value
The business
opportunities
Your partner
The challenges
Where to start
10. The business opportunities
By now, you can see the tremendous opportunity to disrupt the status quo and transform industries and
markets with the Internet of Things. By becoming data-driven organizations, companies can capitalize on
the opportunity and create new value for themselves, their customers, and their partners. While companies
of all shapes and sizes can realize new business outcomes with IoT, here are three types that we see as
having much to gain in the near term and why:
Equipment manufacturers
Equipment manufacturers have a tremendous opportunity to leverage IoT to grow their business with improved
or new products, services, and better customer experiences. Customers expect quality-made products and
competition continues to undercut price, impacting profit margins. So competitive advantages and differentiation
are essential for growth. IoT with a Big Data analytics platform is the opportunity to have both.
Business white paper
Executive summary
The value
The business
opportunities
Your partner
The challenges
Where to start
11. For instance, by integrating sensors that monitor the performance and use of installed equipment,
manufacturers can create value with historical and real-time data analysis, generating contextual insights.
These insights help to understand the products better, how they are utilized, and how they perform. It
even predicts problems before they arise. The insights can be sold as software application offerings to their
customers or even packaged in contracts, enabling the manufacturer to offer service-level agreements
guaranteeing the performance of the equipment. That’s business model innovation.
Additionally, historical data from usage across a fleet of products dispersed in the field can help the
manufacturer better understand condition-based impact and the insights can be fed back into product
development for improved design and competitive advantages.
Arguably, IoT’s greatest value comes from the broader ecosystem built by both suppliers and customers
atop the data and devices. This ecosystem creates competitive differentiation that moves beyond the cost to
focus on value creation and long-term customer and supplier collaboration.
Manufacturers can now differentiate their offerings from a onetime capital purchase to new and improved
products and services. They can even enable new business models. Take for instance, the connected
printers from HP Inc. By integrating sensors into the ink cartridges, HP Inc. can automatically ship ink based
on real-time use, ensuring customers don’t run out of supply and enable an innovative “ink as a service”
business model.
Business white paper
Figure 1: HP Inc. connected printers: Business transformation
Connected printer
Connects to the cloud
Supply chain
Optimized and delivers ink
before running out
Smart ink cartridges
Sensors inform when ink is
getting low
Your printer tells us
when to send ink
Customer outcomes
• Never run out of ink
• Provide user satisfaction
• Get financial savings
Business outcomes
• Increased operating income
• Improved financial forecasting
• Improved supply chain operationsE-commerce
Seamless integration to billing
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The business
opportunities
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The challenges
Where to start
12. Enterprises
Enterprises have so much to gain from IoT and Big Data analytics. The unpredictable nature of equipment
can make it a liability, but with new insights from contextual data, equipment can truly be an asset.
Instrumentation and sensor data collection across operations can provide visibility and light to what
was previously dark. Enterprises with real-time, historical, and predictive insight to all their devices and
equipment can ensure their businesses are operating at peak levels for maximum financial gain and lowest
risk. Not only is the integrated intelligence from devices and equipment valuable, but also discovering how
these new sources of data can integrate within a larger Big Data strategy unlocks even more value.
For instance, in retail the mission is clear—provide the best customer experience across multiple channels
to increase customer loyalty and grow the business. Retailers face countless pressures including stock
outs, competitive prices, and workforce productivity. New data from IoT—customer mobile phones
with GPS, video cameras, location positioning such as iBeacon, weather, and RFID tags—analyzed with
transactional data creates integrated intelligence that improves customer experiences with seamless
customer service across all channels. Optimized multi-channel operations from understanding intent to
purchase, identifying cross-sell opportunities, improving product placement and workforce productivity,
and optimizing inventory and pricing can increase customer loyalty.
In the oil and gas industry, operators are responsible for ensuring safe and efficient operations across their entire
supply chain from drilling, transportation, refinement, and distribution. Globally, there are more than 183,000 miles
of crude oil pipelines, 155,000 miles of petroleum product pipelines, and 600,000 miles of natural gas pipelines
transporting highly volatile substances.8
More and more disparate data sources such as high-pressure water
pumps, flow meters, in-line inspection devices, and aerial surveys providing information about the performance
and operation of pipelines are becoming available to operators. The sensor data from pipelines integrated with
other operational databases enabled by a Big Data analytics platform can provide operators with integrated
intelligence of their operations for maximum efficiency and safety of employees and the community.
Business white paper
8
Pipeline Industry Growth Fueled By Increasing Global Energy Demand, Shale Gas Exploration, Pipeline and Gas Journal (Ganesh Dabholkar), March 2014.
Executive summary
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opportunities
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The challenges
Where to start
13. Service-based businesses
Across all industries, there is a wide-range of service-based businesses providing services from equipment
services, engineering services, integration services, software applications, business and operations consultancy
to professional services that extend an enterprise’s workforce. Their goals are the same—provide superior
service and solutions that maximize their customers’ business outcomes.
The IoT promises to solve significant challenges and create new business value. But with any new technology
investment, there may be concerns of the actual value achieved falling short of expectations. This can limit
technology adoption and hinder the investments that can drive outcomes. Service-based businesses are often
experts in their customers’ business. They understand the challenges and can leverage their domain expertise
with Big Data analytics. The expertise of both Big Data and the company’s business model often lower the
barriers to technology adoption, and creates growth opportunities for their customers and themselves.
For instance, companies servicing healthcare equipment or providing services to an oil field can improve
its competitive differentiation by offering solutions. They can offer IoT-enabled solutions that monitor
or diagnose remotely, manage assets, support in real-time, proactively maintain, and optimize across all
operations—packaged in innovative business models.
Here are just some of the business outcomes possible with the right enabling technologies:
• Increased revenue
• Optimized operations
• New products and services
• Workforce productivity
• Reduced risks
• Reduced operating cost
• Optimized maintenance
• Optimized network throughput
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opportunities
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The challenges
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14. The challenges
Unlocking the value to the Internet of Things doesn’t come without challenges and requires business
leaders to partner with their organizations’ technology leaders. Creating intelligent devices with sensors
and processing as well as enabling secure and reliable connectivity are just the beginning. Enabling secure
data, managing the data, processing insights, timely insight delivery, and cost management are some of the
big challenges.
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The challenges
Where to start
15. Technology leaders need to be at the center of the enterprise leading the company and overcoming the
hurdles for the business. The following table contains significant business challenges with corresponding
technology requirements to maximize the value and minimize the financial and operational risks from IoT:
Business white paper
Table 1: The business challenges and technology requirements for the Internet of Things
Business challenges Technology requirements
Delivering business value
when it’s needed
• Balancing compute at network edge and cloud to optimize latency, insights, and TCO
• Batch, real-time, and interactive analytics
• Flexible languages for writing queries, scripts, or machine learning
• Flexible schemas
• Predictive analytics
• Multiple data sources and aggregating data lakes
• Enabling multiple technology stakeholders—BI analysts, programmers, data scientists
• Access to apps when device or user is offline
Delivering business value
where it’s needed
• Operationalizing the insights in real time, closed loop to operations
• Multi-tenancy
• Environment for agile application development
• Enabling the various stakeholders and departments that need the insights
• Flexible app deployment—Web and mobile platforms
• Connectors to multiple business intelligence tools
Securing and governing the
data, apps, and users end
to end
• Managing cybersecurity threats with new data from sensitive equipment
• Securing data across the network and cloud
• Securing control systems from intruders
• Compliance and regulatory requirements from data
• Regulation and authorization of applications and data
• Managing data lifecycle
• Privacy of sensitive data
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16. Business white paper
Table 1: The business challenges and technology requirements for the
Internet of Things (continued)
Storing and analyzing
massive data at scale
• 100 percent of data structured and unstructured with exponential growth
• High-velocity data
• Accessibility, performance, and speed for multiple applications
• Flexible, scale-out infrastructure
• Minimizing total cost of ownership (TCO)
• Control of IT operations, but enablement of data
• Leveraging existing investments, but optimizing for new value potential and future requirements
• Minimizing data center footprint and energy consumption
• Open, standards-based architecture
Breaking down the
data silos
• Analytics across multiple data types and sources
• Single pane view and dashboard of all critical assets for operations, consolidated across multiple
vendors
• Data ownership and access for new IoT data
• Connectors to large ecosystem of data types
• Data lake across multiple clouds
• Open APIs
These challenges directly impact the viability of business outcomes and financial return from investments in IoT.
In consideration of this long list, technology leaders can’t do it alone and need a partner who understands the
business challenges, technology requirements, and has end-to-end capabilities, solutions, services, and partner
ecosystems to help deliver the business outcomes.
Hewlett Packard Enterprise studied the top home security
systems using HPE Fortify On Demand and found 100 percent
displayed significant security deficiencies.9
9
HP (now Hewlett Packard Enterprise) Study Finds Alarming Vulnerabilities with Internet of Things
(IoT) Home Security Systems, HP (now Hewlett Packard Enterprise), February 2015.
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opportunities
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The challenges
Where to start
17. Your partner
As your trusted partner for enterprise information technology, Hewlett Packard Enterprise has a
comprehensive portfolio of infrastructure hardware, software, and services and a partner ecosystem to
enable your organization to unlock the value of the IoT across all industries. Hewlett Packard Enterprise
is uniquely positioned with proven experience, leadership, technology breadth, and has complete
understanding of the challenges with integrated solutions needed to maximize the value and minimize the
risks from IoT.
The HPE IoT Reference Model is a functional view of our extensive portfolio of products, solutions, and
services for IoT from connectivity of devices to delivering business outcomes, securely and efficiently
at scale, across the technology stack. Utilizing a data-centric approach, the HPE IoT Reference Model
integrates best-in-class Hewlett Packard Enterprise technologies and services from the edge, network, and
core to deliver actionable insights and business outcomes from IoT.
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18. Solution highlights:
Big Data and analytics platform for IoT
With the ever-growing proliferation of data available to organizations, Hewlett Packard Enterprise recognizes
that the industry needs an enterprise-grade software platform that can ingest, aggregate, manage, and
analyze all of your data. That platform should also deliver insights at the scale and speed required by your
organization. HPE Haven is a pioneering Big Data platform that can harness all data types, ingesting data
from multiple, disparate sources, and analyze it at the speed and scale of enterprise needs. Whether the data
is structured transactional business, machine, and sensor data or unstructured human information such as
video, audio, and text, Hewlett Packard Enterprise has the storage and analytics processing capabilities to
deliver value on the data.
Across industries, speed of insights makes the difference in competitive advantages, business outcomes,
and even safety. In oil and gas, each in-line inspection devices (known as “pigs”) generates terabytes of data,
measuring and monitoring for potential cracks and leaks throughout a pipeline.
Business white paper
Figure 2: HPE IoT Reference Model
Devices
Edge Network Core Industries
Applications
Data
Big Data and
analytics platform
Security
Services
Business
intelligence
Data
integration
Network
Data
Wireless/Wired/
Remote
Device and service
management
Network interworking
Data acquisition and
verification
Distributed
mesh
computing
Infrastructure
• Compute
• Storage
Analytics
Governance Backup
recovery
Cloud application platform
Cloud management orchestration
Infrastructure—Compute | Storage | Network
Archiving Records
management
Master data
management
Apps Infrastructure
Application ESM/SIEM
Authentication/
Encryption
Real-time
analytics
Batch analytics
Advanced
analytics
Context
Visualization/
Reporting
Vertical apps
Operations
management
Manufacturing
Retail
Healthcare
Oil Gas
Transportation
Financial
service
Public sector
Utilities
Aviation
....
Information governance and management
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19. While pigs are intended to deliver actionable insights into the pipeline health and integrity, the Wall Street
Journal reported that analyzing the reams of data collected can take months10
using traditional technologies,
which is too long when safety is on the line. In drilling, a single oil well can generate one terabyte of
production data daily, but it has been reported that engineers spend 60 percent of their time mining data.11
Imagine if a software platform can mine data quickly, delivering insights in real time. Now engineers can
focus on data science and operationalizing the intelligence. With HPE Haven, you have a platform capable of
delivering actionable insights for all your data at enterprise-grade performance.
In a recent analytics benchmark study, HPE Vertica ingested,
stored, and analyzed 22.8 trillion rows of smart meter data at an
unprecedented speed and in the smallest hardware footprint in
the industry.12
Case study: Trane, a leading global provider of indoor comfort solutions and services and a brand of
Ingersoll Rand, partnered with Hewlett Packard Enterprise for an equipment data analytics solution. The
Trane Intelligent Services group offers offsite monitoring, event mitigation, and energy performance services
to improve the efficiency and reliability of installed systems and helps their customers reduce energy and
maintenance costs. As this group has grown, Trane recognized a need for a data platform to handle the
increased analytics workloads and turned to HPE Vertica for a solution. HPE Vertica presented an efficient
platform that accommodates time series data, is horizontally scalable, enables flexible cloud deployment,
and offers superior compression and speed. Trane Intelligent Services is now utilizing HPE Vertica for
30 sites, in hundreds of buildings and expects to expand this service for 700 sites, a total of over 1,000
buildings worldwide.
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From PDF
10
The Wall Street Journal, Oil-Pipeline Cracks Evading Robotic “Smart Pigs,” August 16, 2013.
11
“Innovation and Growth in the Oil Gas Industry,” Oil Gas Monitor, John Brantley, May 29, 2012.
12
IoTAbench: an Internet of Things Analytics benchmark, HP (now Hewlett Packard Enterprise), December 2014.
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opportunities
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The challenges
Where to start
20. Built on an open platform with a flexible architecture, HPE Haven integrates with all the common Hadoop
distributions (Hortonworks, Cloudera, and MapR) and over 500 industry connectors to hundreds of content
repositories. It leverages common languages for developers, business analysts, and data scientists to write
SQL queries, scripts, or develop predictive machine-learning algorithms. Open APIs and our vast ecosystem
enable partners to build advanced analytics and applications on top of HPE Haven. Additionally, HPE Haven
is available on-premises or in the cloud, providing flexible deployment options.
The HPE Haven platform includes three analytics engines:
• HPE Vertica is a massively scalable database platform, purpose-built from the ground up for real-time
analytics from terabyte to petabyte data sets on industry-standard hardware. As a distributed, columnar
database, HPE Vertica delivers insights 50 to 100 times faster than traditional relational databases and
easily scales out with growing datasets on industry-standard servers, storage, and networking. Built-in
compression reduces infrastructure cost while dramatically increasing performance. HPE Vertica is an
efficient database for real-time loading and querying of high volume time-series data for sensor and
machine data.
• Distributed R is an HPE open source framework for predictive analytics, enabling scale-out, parallel
processing of the popular R language for developing machine-learning algorithms. Distributed R provides
data scientists with a platform to create predictive algorithms from large, petabyte-sized datasets.
Organizations can develop predictive foresight its big datasets originating from operations, equipment
performance, or IoT. Additionally, Distributed R can be utilized on industry-standard hardware, which
eliminates rework of standard R libraries that support massive scale out.
• HPE IDOL is our analytics platform for unstructured data, powering analytics, information management,
and governance solutions by enabling enterprises to categorize, index, search, and analyze human
information at scale with context. It can process more than 1,000 file types including video, images, audio,
email, and social media. Hewlett Packard Enterprise has a tremendous portfolio in advanced video and
image processing analytics. Traditionally, industry use of video analytics has been limited to surveillance.
However, our deep expertise of video and image analytics solutions coupled with our Big Data platform
can enable even greater business and operations optimization across all industries.
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opportunities
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The challenges
Where to start
21. Infrastructure for IoT
To lead in this market opportunity, technology leaders need to be agile and focus resources, human, and
capital, on delivering business outcomes that will lead to competitive advantages and breakout growth. We
must drive agility, cost control, and performance while ensuring the highest levels of security, throughout
our infrastructure and platforms.
We recognized that companies need infrastructure and architectures that are flexible and enable the enterprise
to be agile, delivering data insights to the right people at the right time at the right ROI. High availability,
performance, latency, scale, cost, and security need to be optimized so the promise of IoT is delivered.
For the edge: Speed and value of insights, capital costs, and operating costs are critical technology
implications. Sensors and devices in remote locations may have inadequate bandwidth available for the data
to move and the volume of data produced on the edge may be too large and cost prohibitive to move. So
there should be a balance of edge and cloud data processing based on the analytics, data sets, performance,
and costs. A distributed compute platform optimizes both performance and costs. When near-equipment
processing is feasible, analytics can be delivered faster and at a lower cost than transmitting and processing
all data to the cloud. The Hewlett Packard Enterprise infrastructure portfolio includes server technologies
optimized for edge—including HPE Moonshot and HPE ProLiant MicroServer Gen8.
For the network: The promise of insights derived from IoT is tremendous, but the data moving across the
communication network must be accessible and trusted to be reliably used. Today’s IoT isn’t trustworthy
with increasing threats to the authenticity and integrity of devices, data, applications, and networks.
HPE networking solutions address these issues and enable IoT to be a reliable element of business-critical
decisions and processes. The solutions enable a secure, scalable, and flexible network infrastructure that
unifies access and enables secure connectivity to all devices in virtually any operating environment. So
whether you need an explosion-proof wireless solution for an oil rig, a secured wired LAN for factory
automation, or remote VPN with cellular backhaul for supervisory control and data acquisition (SCADA)—
Hewlett Packard Enterprise has you covered with a network that is reliable, scalable, and trustworthy.
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The challenges
Where to start
22. Another challenge at the network is the heterogeneity of devices that need to be connected, configured, and
managed. Hewlett Packard Enterprise provides an end-to-end remote management function for mobile and
wireless devices that include dynamic device discovery, over-the-air device configuration, and fine-grained
control of IoT traffic to and from the device. Via this device, vendor independent and connectivity agnostic
function, millions of IoT devices for smart applications can be remotely managed on the same multi-tenant
platform in the cloud. The solution operates at a low total cost of ownership with high scalability and flexibility
due to the network interworking proxy and oneM2M/OMA-DM standards compliance.
Case study: The city of Auckland, New Zealand selected Hewlett Packard Enterprise to deliver a visionary
Big Data project designed to provide a safer community and more efficient roadways for its citizens.
Auckland Transport, Auckland’s government agency responsible for all of its transportation infrastructure
and services, deployed video analytics powered by HPE IDOL on servers and storage from HPE Enterprise
Group, and with support from HPE Software Professional Services. Auckland Transport uses HPE Haven to
analyze, understand, and act on vast quantities of data of virtually any type including text, images, audio,
and real-time video. The system leverages data from a variety of sources, including thousands of security
and traffic management cameras, a vast network of road and environmental sensors, as well as real-time
social media and news feeds.
“The safety and well-being of our citizens is always our top priority
and the Future Cities initiative is a big step in the right direction. Only
Hewlett Packard Enterprise could comprehensively deliver the
custom solution, expertise, and ecosystem at this scale to transform
our vision into reality.”13
– Roger Jones, CIO Auckland Transport
Business white paper
13
City of Auckland, New Zealand Selects HP (now Hewlett Packard Enterprise) to Drive Groundbreaking
Future Cities Initiative, HP (now Hewlett Packard Enterprise), September 2014.
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23. For the core: HPE servers, storage, and networking technologies, architected for Big Data and IoT, serve
as our core infrastructure to empowering a data-driven organization to scale out and scale up. Based on
open standards, the HPE server portfolio includes industry-leading x86 servers optimized for Big Data
workloads. The recently published HPE Big Data reference architecture for Hadoop increases performance
and minimizes the cost of infrastructure and management.
For hybrid infrastructure: HPE Helion is our unified portfolio of products and services that makes it
easy for your organization to build, manage, and deploy applications in a hybrid IT environment. Within
HPE Helion, HPE CloudSystem is a fully integrated, end-to-end, private cloud solution built for traditional
and cloud-native workloads, and delivering automation, orchestration, and control across multiple clouds.
Cloud application platform for IoT
Equipment manufacturers, enterprises, and service-based businesses can quickly develop their own private
cloud platform, deploying IoT cloud applications to internal and external customers through the HPE Helion
Development Platform. HPE Helion Development Platform is a cloud application platform built on open-source
technologies that makes it faster to develop, deploy, and deliver highly available and scalable cloud-native
applications.
Architectural decisions for IoT applications must provide developers flexibility and instant access to
environments that support agile development and evolving DevOps practices. With this developer-centric
solution, your teams will be able to deploy applications faster, enabling accelerated innovation, decreasing
time to business outcomes, and optimizing infrastructure resources. With HPE Helion Development
Platform, your team can focus on delivering business outcomes.
Analytics and data management services for IoT
We recognize that the journey to becoming a data-driven organization and capitalizing on the IoT market
opportunity while managing the challenges and risks can be daunting. As one of the world’s leading services
organizations, HPE Enterprise Services has more than 50 years of experience building a strong reputation of
industry expertise helping our clients to engage better with their customers, manage risk, and win with the
explosive growth of data.
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The challenges
Where to start
24. The HPE Analytics and Data Management practice provides an advisory-led transformation that bridges
traditional business intelligence with new Big Data technologies and practices—enabling enterprises to become
data-driven and agile, maximizing business outcomes across all industries. With 3,500 consultants and 1,200
data scientists and analytics professionals, Hewlett Packard Enterprise has the depth of knowledge, industry
expertise, and breadth of capabilities globally to help companies capture the full benefit of IoT.
Hewlett Packard Labs for IoT
Data growth is exploding with IoT and it will eventually be impossible for current computing architectures to
keep up and make sense of all the data. To combat this future data explosion, Hewlett Packard Labs is breaking
down barriers and inventing completely new computing paradigms that will change the way we manage and
interact with information. Known as “The Machine,” Hewlett Packard Labs is creating technology that will make
it possible to manage millions of computing nodes, perform exabyte-scale calculations, and deliver a fully
dimensional data experience that is intuitive and collaborative for the future IoT.
However, for many IoT use cases, it may not be possible to move data off the edge into a centralized
machine. For this reason, we are also exploring how to take “The Machine” out of the core and shrink it down
for computing on the edge. Known as distributed mesh computing, the approach involves sprinkling novel
low-power, large-memory compute nodes throughout a network so that data can be stored, processed,
and analyzed locally throughout a mesh. The nodes then proactively collaborate to harness the same level
of intelligence collectively that might be availed from a large data lake, but without the costs or challenges
of having to centralize the data in one location. As the proliferation of data from IoT increases, innovations
from the Hewlett Packard Labs will enable the necessary quantum leaps in performance and efficiency while
lowering costs and improving security—ensuring that the future IoT will continue to enable new business
outcomes, even at unprecedented scale.
“We believe that the implications of the Machine will be
dramatic. When we say, HP (now Hewlett Packard Enterprise)
invents the future, this is what we’re talking about.”
– Meg Whitman, CEO and Chairwoman of Hewlett Packard Enterprise
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25. Partner ecosystem for IoT
The Internet of Things can enable new business outcomes and value creation, but the value cannot
be delivered by one technology provider alone. Maximizing the benefit and minimizing the risks to
IoT is a team sport. That is why Hewlett Packard Enterprise has an open ecosystem and has created
strategic partnerships and integrated solutions throughout the technology stack. Building on our
technologies and services in the HPE IoT Reference Model, our ecosystem partners integrate their products
and services so that together we can help our customers be successful.
Business white paper
Figure 3: HPE’s breadth of solutions for the IoT
HP’s technologies and services empowering a data driven organization
Devices
The HPE technologies and services empowering a data-driven organization
Network OutcomesEdge Core
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26. Where to start
The world is changing. The Internet of Things can create countless opportunities for the transformation
of businesses and industries. In this idea economy, companies need to become data-driven organizations,
leveraging new and old data to create new value and insights that will lead to breakout growth.
We recommend starting first with identifying your desired business outcomes both near term and in the
more distant future. Then, you will want to develop a Big Data and IoT strategy by understanding your
existing data and the devices, equipment, and machines critical to your operations. Be inquisitive starting
with these questions:
• What impact do devices and equipment have on our business and operations?
• How does our organization incorporate sensor data today and are we getting the most value possible?
• What does integrated intelligence mean for our business?
• How can we integrate contextual insights from disparate data sources to achieve integrated intelligence
across our business operations?
• How would more instrumentation or better insights and intelligence of our assets impact products and services?
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27. You’ll need a partner that understands the value, the business opportunities, and the end-to-end challenges
and has the breadth and depth of technologies, services, and ecosystem to solve these challenges and help
deliver the value. Thankfully, you have Hewlett Packard Enterprise.
Reach out to us and we will be glad to partner with you to help design, develop, and implement IoT solutions
for your business.
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