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Connectivity to business outcomesBecome a data-driven organization with the Internet of Things

Executive summaryPersonal 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

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1 The Internet of Things: Mapping the Value Beyond the Hype, McKinsey Global Institute, June 2015.

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.

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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?

The valueIn order to justify the investment in R&D, 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?

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4 The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things, IDC, April 2014.

Contextual insightsYour 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.

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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 intelligenceLet’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.

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5 Jeff Immelt on GE Oil and Gas Strategy and the Power of One Percent, ARC Advisory Group, May 2014.

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.

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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.

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 insightsNew 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.

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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.

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The business opportunitiesBy 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 manufacturersEquipment 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.

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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.

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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

EnterprisesEnterprises 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.

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8 Pipeline Industry Growth Fueled By Increasing Global Energy Demand, Shale Gas Exploration, Pipeline and Gas Journal (Ganesh Dabholkar), March 2014.

Service-based businessesAcross 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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The challengesUnlocking 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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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:

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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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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.

Your partnerAs 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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Solution highlights:Big Data and analytics platform for IoTWith 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.

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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

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.

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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Infrastructure for IoTTo 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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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

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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.

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 IoTEquipment 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 IoTWe 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 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 IoTData 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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Partner ecosystem for IoTThe 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.

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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

Where to startThe 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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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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Resources Papers

• IoT—Turning ordinary things into extraordinary business outcomes—Brochure

•How to Choose an IT Platform to Empower Your Internet of Things—White paper

• Connect-and-Protect: Building a trust based IoT for business-critical applications—Technicalwhite paper

• Capitalize on the untapped potential of sensor data—Business white paper

• Sensor data analytics—Brief

•Put Data to Work

• Be a smart city—Business white paper

•HPE Internet of Things Smart Metering—Solution overview

Videos

• If Machines Could Talk

• IoT Highlights from Mobile World Congress 2015

Learn more athpe.com/info/iothpe.com/info/bigdata

Follow us @HPE_BigData

© Copyright 2015–2016 Hewlett Packard Enterprise Development LP. The information contained herein is subject to change without notice. The only warranties for Hewlett Packard Enterprise products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. Hewlett Packard Enterprise shall not be liable for technical or editorial errors or omissions contained herein.

4AA6-0506ENW, February 2016, Rev. 1

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