Software and the IoT: Platforms, data, and analytics

Transcription

1 Internet of Things - Volume 2 Software and the IoT: Platforms, data, and analytics The role of software across the enterprise and consumer IoT Equity Research The Internet of Things takes shape as the next mega-trend The Internet of Things (IoT) is taking shape as the next mega-trend, following the major shifts to mobility and cloud computing that have shaped the last decade in technology. This report is second in our Internet of Things series, with a focus on software and platform opportunities. Development of standards as key to widespread adoption The development of standards that allow heterogeneous IoT devices to communicate and leverage common software applications is critical for the functionality and adoption of the IoT. On the enterprise side, setting of standards will be a drawn-out process over the next decade, particularly with competing consortiums. The consumer IoT has already begun to see de facto standards emerge as defined by mobile titans attempting to link and leverage their large installed bases. Enterprise opportunity centers on data flow and analytics We believe the most successful enterprise IoT software vendors initially will target specific verticals or seek to master a particular stage of the machineto-machine (M2M) data flow process: 1) managing the communication with connected devices/sensors, 2) providing middleware for integration to data repositories, 3) storing and securing the data, and 4) analyzing and visualizing the data. Emerging vendors and technologies such as Hadoop will play a major role. CL-Buy and Buy-rated companies with exposure to the trend include Ericsson, Gemalto, Hexagon, Inmarsat, Opower, Oracle, Qlik, Salesforce.com, SAP, and Tableau. Heather Bellini, CFA (212) heather.bellini@gs.com Goldman, Sachs & Co. Bill Shope, CFA (212) bill.shope@gs.com Goldman, Sachs & Co. Greg Dunham (212) greg.dunham@gs.com Goldman, Sachs & Co. Michael Bang +82(2) michael.bang@gs.com Goldman Sachs (Asia) L.L.C., Seoul Branch Mohammed Moawalla +44(20) mohammed.moawalla@gs.com Goldman Sachs International Matthew Cabral (212) matthew.cabral@gs.com Goldman, Sachs & Co. Shateel Alam (212) shateel.alam@gs.com Goldman, Sachs & Co. Mark Grant (212) mark.grant@gs.com Goldman, Sachs & Co. Mobile leaders will likely shape the consumer opportunity As de facto standards come into place in the consumer IoT, we expect the pace of innovation to accelerate as new functionality is enabled by communications compatibility and clearly-defined APIs across various third party hardware (HW) and software (SW) providers. We take a platformcentric approach to the consumer IoT with mobile OS vendors (i.e., Apple, Google and Microsoft) already beginning to shape the landscape. CL-Buy and Buy-rated companies with exposure to the trend include Samsung and Apple. For a more exhaustive list of companies with exposure, see Exhibit 1. Goldman Sachs does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision. For Reg AC certification and other important disclosures, see the Disclosure Appendix, or go to Analysts employed by non-us affiliates are not registered/qualified as research analysts with FINRA in the U.S. The Goldman Sachs Group, Inc. Global Investment Research

2 Portfolio manager s summary For further reading: Internet of Things Volume 1: Making S-E-N-S-E of the next megatrend - Framing the IoT opportunity for CommTech and Semiconductors June 25, 2014 The Internet of Things (IoT) is taking shape as the next mega-trend, following the major shifts to mobility and cloud computing that have formed the last decade in technology. While those two mega-trends had profound and industry-changing implications for the technology industry, the implications of the IoT will likely prove even more far-reaching, as by its very nature it is a trend that will reach beyond tech to touch every industry, from healthcare to retail to oil and gas exploration. This report is the second in our Internet of Things series, focusing on the software and platform opportunities in the IoT. The emergence of standards will drive a shift from proprietary to platform-based solutions. The development of standards that allow heterogeneous IoT devices to communicate and leverage common software applications is critical for the functionality and adoption of IoT. In the early days of a new market, proprietary integrated solutions tend to ramp faster as horizontal platform-based solutions tend to lag given the need for standardization and other coordination which may not exist in a market s infancy. We look towards the emergence of standards as a major catalyst of growth ahead. Within this, it is important to keep in mind that the enterprise and consumer opportunities will take markedly different paths. On the enterprise side, setting of standards will be a drawn-out process over the next decade, particularly with competing consortiums. The consumer IoT, on the other hand, has already begun to see de facto standards emerge as defined by mobile titans (i.e., Apple and Google) attempting to link and leverage their large installed bases. Enterprise IoT: Capitalizing on the interconnectivity of devices and the data growth explosion. While the consumer software and internet markets have historically led enterprise IT in innovation, businesses have been early pioneers of leveraging sensor data to improve their supply chains, optimize field service operations, and even lower risk of asset pools. Software has been critical to enable IoT use cases to proliferate as implementation of a business case typically requires embedded software within the object, server or cloud-based applications to manage the devices, and middleware which ensures the data is transported to existing IT infrastructure. An enterprise that wants to implement an IoT strategy needs solutions to help manage four key steps of that process: (1) Managing communication with connected devices/sensors (2) Middleware (3) Storing and securing the data (4) Analyzing and visualizing the data We believe that the most successful IoT enterprise software vendors in the near- to midterm will be the ones that carve out one or two of these steps and seek to master the solution, rather than those (if any emerge) who aggressively try to create a one-stop-shop to manage the entire data flow process. Consumer IoT: A platform-centric approach; mobile leaders shaping the opportunity. In looking at the consumer IoT as it pertains to software vendors, the early stages of the market have been led by proprietary solutions vendors that focused on the key subcategories (i.e., Crestron, Savant, and Control4 for home automation). In addition, point solutions that integrate proprietary hardware and software for a specific use case have gained popularity; in home automation, this has included Nest Labs (acquired by Google), home music veteran Sonos, and home security solutions like ADT and alarm.com. As standards (or de facto standards) come into place, however, we expect the pace of innovation to accelerate as new functionality is enabled by communications compatibility and clearly defined APIs across various third party hardware and software providers. We take a platform-centric approach to the IoT, with vendors looking to define over-arching Goldman Sachs Global Investment Research 2

3 platforms for devices and sensors to attach onto. Our framework is built around the view that some things are smarter than others as platform vendors look to control key strategic device categories while leaving the long tail of devices and sensors to 3 rd parties. We expect the IoT theme to impact many public and private companies in varying degrees and over varying timeframes. Exhibit 1 lists those companies that we believe could have exposure to the theme, but note that IoT exposure does not necessarily constitute a positive impact within the 12-month timeframe for the price targets of most of our covered companies, hence the inclusion of Neutral and Sell rated names. Exhibit 1: Companies with exposure to the IoT GS Covered Companies Company Ticker Rating Company Ticker Rating Apple AAPL Buy Nokia NOK Not Rated Denso 6902.T Neutral Nuance NUAN Neutral Ericsson ERIC Buy Opower OPWR Buy Garmin GRMN Neutral Oracle ORCL Buy Gemalto GTO.AS Buy Qlik QLIK Buy Google GOOGL Neutral Salesforce.com CRM CL-Buy Hexagon HEXAb.ST Buy Samsung KS Buy Hewlett-Packard HPQ Neutral SAP SAPG.DE CL-Buy IBM IBM Neutral Sony 6758.T Neutral Informatica INFA Neutral Splunk SPLK Neutral Inmarsat ISA.L Buy Tableau DATA Buy Microsoft MSFT Sell Teradata TDC Sell NETGEAR NTGR Sell TIBCO TIBX Neutral Public companies not covered by GS Research ADT (ADT) control4 (CTRL) Digi International (DGII) Harman (HAR) PTC Inc. (PTC) TomTom NV (TMOAY) ARCHOS (JXR) DeviceWise (Telit: TELT.L) Eurotech (E5T.MI) LogMeIn (LOGM) Sierra Wireless (SWIR) Trimble Navigation (TRMB) Private Companies Aeris Communications Cleversafe Fitbit Loggly Pebble SmartThings alarm.com Cloudera GoodData M2Mi Pentaho Sonos Axeda Clustrix Hadapt MapR Pivotal Sumologic Basho Couchbase Hortonworks MarkLogic Polar Talend Bime Crestron icontrol MetaLayer RelateIQ VoltDB Birst Cumulocity Jasper Wireless MongoDB revolv WeMo (Belkin) C3 Energy Dataspora (Via Science) Jawbone Mykoots roku Wink centrifuge Datastax karmasphere Net4Things Savant Systems Withings Chartio DeviceHive Kognitio Neul ScaleArc Cirro Domo Kore Palantir SeeControl Source: Goldman Sachs Global Investment Research. Goldman Sachs Global Investment Research 3

4 The IoT Software opportunity: platforms, data flow, and analytics The IoT, while still nascent, presents significant opportunity in coming years. Indeed, IDC forecasts that the IoT will include nearly 30 billion connected objects by 2020, a more than 3-fold increase from the 9.1 billion objects in To be included in the projection, IDC looks at devices that feature connectivity (including cellular, WiFi, Bluetooth, Zigbee, etc.) and don t necessitate human interaction for communication. Along with such a large unit TAM comes a significant revenue opportunity, as IDC expects the worldwide market for IoT solutions to reach $7.1 trillion in 2020 from $1.9 trillion in Their forecast includes intelligent and embedded systems shipments, connectivity services, infrastructure, purpose-built IoT platforms, applications, security, analytics, and professional services and highlights why most major IT vendors are aiming to stake their claim within the market. In breaking down the market, IDC attributes 40% to hardware, 40% to services, and 20% to software spend. Over time, as IoT standards develop we expect the software opportunity to become increasingly significant. Exhibit 2: The IoT provides significant opportunity for IT vendors Revenue and installed base for the IoT $ Trillions $8.0 $7.0 $6.0 $5.0 $4.0 $3.0 $2.0 $1.0 $ e 2015e 2016e 2017e 2018e 2019e 2020e Billions of Objects IoT Revenue (lhs) IoT Installed Base (rhs) Source: IDC Why now? The sparks igniting the IoT A number of significant technology changes have come together to enable the rise of the IoT. These include the following. Low-cost sensors According to the SIA, the average cost of a sensor now stands at $0.60 vs. $ years ago. Sensors vary widely in price, but in general they are now cheap enough and small enough to justify new business cases. Smartphones With a large and growing installed base, smartphones are now becoming the personal gateway to the IoT, serving as a remote control or hub for the connected home, connected car, or for the health and fitness devices consumers are increasingly starting to wear. Goldman Sachs Global Investment Research 4

5 Cheap bandwidth The cost of bandwidth has also declined precipitously, by a factor of nearly 40X over the past 10 years. Cheap processing Similarly, processing costs have declined by nearly 60X over the past 10 years, enabling more devices to be not just connected, but smart enough to know what to do with all the new data they are generating or receiving. Expanding wireless coverage With Wi-Fi coverage continuing to expand around the world, wireless connectivity is available for free or at a very low cost in many places, given Wi-Fi utilizes unlicensed spectrum and thus does not require monthly access fees to a carrier. Big data As the IoT will by definition generate voluminous amounts of unstructured data, the availability of big data analytics is a key enabler. IPv6 Most networking equipment now supports IPv6, the newest version of the Internet Protocol (IP) standard that is intended to replace IPv4. IPv4 supports 32-bit addresses, which translates to about 4.3 billion addresses a number that has become largely exhausted by all the connected devices globally. In contrast, IPv6 can support 128-bit addresses, translating to approximately 3.4 x addresses an almost limitless number that can amply handle all conceivable IoT devices. Exhibit 3: Cost of compute is a fraction of its 1990 s level Exhibit 4: so is cost of bandwidth $1, $ $10,000 $ $1,000 $1, $ per 1 MM transistors $10.00 $1.00 $ per 1000 Mbps $100 $16.00 $0.10 $0.05 $10 $ $ Source: John Hagel, Deloitte, 5/14, Mary Meeker, KPCB. Note: y-axis is on a logarithmic scale Source: John Hagel, Deloitte, 5/14, Mary Meeker, KPCB. Note: y-axis is on a logarithmic scale Like the mobile revolution, the IoT will create new companies and new categories The importance of the IoT expands beyond pure play enablers to a wide ecosystem of vendors, including existing companies and a host of new players likely to emerge as the market begins to take shape. The opportunity set reminds us of the mobile revolution that has played out over the past decade with many established companies reinventing themselves in addition to an entire new category of software and internet companies spawned from the growth of mobile platforms. As the IoT market continues to evolve, we expect a similar ecosystem of software vendors to develop. Goldman Sachs Global Investment Research 5

6 Exhibit 5: The mobile revolution has led to the development of a wide variety of software companies and services Bubble size indicates the most recent valuation of company Twitter Uber WhatsApp (Facebook) Yelp Dropbox Groupon Instagram (Facebook) Square Pandora Zynga Spotify Beats King Digital Entertainment Launch of iphone... OpenTable AirWatch MobileIron Source: Goldman Sachs Global Investment Research, company data, Wall Street Journal, Bloomberg. Note: Valuation for public companies is current market cap. M&A value used for acquired companies. Valuation for private companies is the value ascribed to the company s latest round of external funding as reported in the press. Where does software fit? As discussed above, we see IoT spend bucketed into hardware, software, and services, with the software component as a key source of value and critical factor of success. In discussing software s role in the IoT, we use Coca-Cola s use of the IoT to elucidate the software value proposition. Coca-Cola IoT use case Coca-Cola is using IoT deployments to gain intelligence from its vending machines to both drive revenue and optimize its operations. Currently, Coke monitors its vending machines inventory levels and system uptime. Should a problem be detected, the vending machine s manager is notified. In this use case, Coke is using the IoT to both save costs and optimize its operations. Coke is taking its IoT deployment a step further with its new Freestyle vending machines, which allow customers to make their own soft drinks by blending Coke products. By leveraging the Coca Cola Freestyle mobile app, the company has personalized the experience for its customers. At the same time, it is gaining intelligence at a corporate level to drive future decision-making. Mixing soda flavors (e.g., getting a Diet Coke with a splash of lemonade) is a popular preference and varies by each customer, but the exact concoction is often difficult to replicate. A consumer could then use this Coca-Cola Freestyle app to save their favorite concoction, allowing it to be replicated at any of Coke s other Freestyle vending machines. It also enables consumers to share their favorite blends with friends and on social media. Earlier this year, Coke acquired 16mn network IDs which are usually reserved for Wi-Fi cards and networking equipment. In the future, if Coke monitored the concoctions across its soda fountains and a popular mixture emerged, the company could then decide to create it as a new standalone flavor. Goldman Sachs Global Investment Research 6

7 In terms of the value proposition of the software component of the IoT, we highlight the following attributes: Management In IoT deployments, enterprises can be connecting to millions of devices around the world and reliability of this communication can be critical to a company s success. In our Coke example, the company needs to track communications with its vending machines and in turn is able to serve its customers better, optimize its operations and increase revenues. We believe enterprises will primarily use a software-based approach manage IoT devices with vendors. Efficiency We see software adding efficiency for both consumers and enterprises in the IoT. In our Coke example, the consumer will be able to quickly create their soda concoction of preference and share their exact preference with friends. From an enterprise perspective, Coke is able to closely monitor its vending machine, reduce downtime, and ensure that soda inventory are always in stock. New Functionality As the IoT adds intelligence to dumb devices and as communication and interoperability is enabled among these devices, we see new functionality and use cases forming. The idea of saving your favorite soda mixture is an entirely new concept and brings an evolving experience to a customer s relationship with Coke. Analytics By enabling communication with devices, large amounts of data is produced and this produces a need for analytics. We see software providing analytics for both consumer and enterprises, but through different platforms, as the analytics that a consumer would be interested in will differ from enterprise analytics needs. In our Coke example, the company is gaining new information on customer preferences, and given the intelligence baked into smartphone and smartwatches, Coke can gain insights by factors like location and age. With the IoT, we also see a new level of analytics emerging for consumers in which they can track things like soda consumption and pull this information into broader nutrition apps for a more holistic view of their health. Enterprise vs Consumer: different paths on the same mega-trend As the IoT continues to take shape, we see the enterprise- and consumer-facing IoT markets developing very differently, which changes the way software vendors must approach these opportunities. We see two key catalysts to the adoption of the IoT across both marketplaces: (1) standardization; and (2) a move from proprietary and integrated to platform-based solutions. The emergence of standards is critical for the IoT software and platform opportunity The development of standards that allow heterogeneous IoT devices to communicate and leverage common software applications is critical for the functionality and adoption of IoT. Unfortunately, these standards have been slow to evolve. We believe this may be changing, and we are nearing key inflection points for standardization particularly for consumerfacing IoT solutions. We look towards the emergence of standards as a major catalyst for the IoT opportunity s evolution, and the path to standardization will be a critical determinant of the market dynamics for software in the IoT. Goldman Sachs Global Investment Research 7

8 Enterprise IoT standardization to be drawn out. In the enterprise, we believe standards are likely to emerge from consortiums of industry leaders and traditional standards setting bodies. This will be a drawn-out process over the next decade, particularly with competing consortiums. De facto standards already emerging in the consumer IoT. In the consumer segment, the standardization process may unfold quite differently. Indeed, with the recent introduction of sub-platforms for IoT from Apple (HomeKit and HealthKit) and Google (Google Fit and Works With Nest), it seems that the mobile titans are attempting to link and leverage their large installed bases to set de facto standards for consumer-facing IoT. While this is not quite the open approach to standards that the enterprise players seem to be pushing, the mobile platform leaders may be able to spawn common APIs and inter-device compatibility in a far more rapid manner. Platform-based solutions could unleash a torrent of value and innovation As noted in our Internet of Things Volume 1 report, in the early days of a new market, proprietary integrated solutions tend to ramp faster. Horizontal platform-based solutions tend to lag given the need for standardization and other coordination which may not exist in a market s infancy. We expect the integrated-first dynamic to be even more pronounced in the IoT, given the inherent specialization by vertical (e.g., very different solutions are required for a fitness band vs. a connected car vs. an oil rig). We look towards the emergence of horizontal platform-based solutions as a major catalyst of growth ahead. Indeed, providing a common interface and well-defined APIs allows multiple vendors to innovate at multiple points in the process, often yielding far more innovation than vertically-integrated vendors alone are likely to produce. Further, introducing additional vendors into the mix will drive increased competition and drive down the total cost of implementation; this, in turn, can drive further user growth. An example of an early-stage shift away from integrated solutions To put some real-world context around integrated vs. platform-based IoT solutions, we explore two approaches to a hypothetical home automation solution (Exhibit 6). We include audio, TV, lighting, and thermostat functionality within our example and add in the cost of professional installation for the vertically-integrated approach. Using a suite of products from Crestron (a leader in high-end home automation), we price the total cost of the project at roughly $34,000 which includes parts plus a week of labor for installation. There is likely flexibility in this total as it may be possible to knock 10%-20% off through product selection and discounting. We then attempt to re-create the core aspects of the Crestron solution using an ipad and a theoretical HomeKit app (launching this fall) as the control point. Our ios-based solution totals just over $3,700 or roughly 10% of the total system cost of the vertically-integrated solution. While the user experience between the two solutions is likely to differ at this stage of the market s development, the dramatic difference in cost highlights the value in disaggregating systems once standardization provides a consistent platform for 3 rd parties to attach onto. Exhibit 6: Platform-based solutions tend to drive down industry pricing Crestron vertically-integrated solution Apple HomeKit self-built solution Control system with touch panel & related cabling $6,900 ipad + theoretical Apple HomeKit control app (Free) $499 Multi-zone audio control $5,200 Sonos BRIDGE (audio control) $49 Speakers throughout the home $4,000 Sonos speakers (2 Play:5, 2 Play:3, 2 Play:1) $1,794 Cable TV infrastructure and control $3,000 Apple TV $99 Lighting and shade control $3,500 Philips hue (starter pack + 10 individual lightbulbs) $800 2 thermostat controls ($600 each) $1,200 2 Nest Thermostats ($249 each) $498 Labor and programming (1 week, 2 workers) $10,000 Labor and programming (self-assembled) DIY Total cost $33,800 Total cost $3,739 Source: Vendor data checks, company websites. Goldman Sachs Global Investment Research 8

9 Enterprise IoT: Capitalizing on the interconnectivity of devices and the data growth explosion While the consumer software and internet markets have historically led enterprise IT in innovation, businesses have been early pioneers of leveraging sensor data to improve their supply chains, optimize field service operations, and even lower risk of asset pools. Software has been critical to enable IoT use cases to proliferate as implementation of a business case typically requires embedded software within the object, server or cloudbased applications to manage the devices, and middleware which ensures the data is transported to existing IT infrastructure. Enterprises also leverage software to analyze the data collected by devices and we see this category of spend as just as critical given the analytics often supports the business case to invest in IoT technologies. Historically, most IoT projects have leveraged a combination of custom-built software with machine-to-machine (M2M) platforms and services which provide the management of the devices and often the communication services to enable deployments. We would expect M2M platform providers (e.g., Jasper, Axeda) to continue to benefit as enterprises look to enable more devices with internet connectivity. So what s new? The emergence of Big Data technologies for analytics has enabled enterprises to glean insights from significantly larger data sets created by IoT use cases in a more economical manner. Our research indicates that Hadoop, an open sourced Big Data technology, can cost significantly less than 1/10 th of the cost of traditional database technology. With the cost of storing and analyzing larger data sets falling, businesses have seen the returns on investment in IoT use cases increase substantially. We expect that emerging Hadoop and NoSQL data platforms alongside visualization technologies (i.e., Splunk, Tableau, Qlik, TIBCO) to benefit from the growth in IoT use cases. Exhibit 7: Worldwide data growth projections Exhibit 8: Interest in Internet of Things has shot up according to Google Trends Google Trends; relative index based on search activity 120 Relative level of interest (Google searches) Source: IDC The Digital Universe December Source: Google Trends. To see who benefits; we should look to what s needed Our IoT framework for enterprise software looks at the IoT phenomenon as a data flow process. An enterprise that wants to implement an IoT strategy needs solutions to help manage four key steps of that process: 1. Managing the communication with connected devices/sensors. Platform vendors like Jasper and Axeda who specialize in customized solutions that enable the customer to control and interface with deployed sensors, gathering data from the sensors, and where applicable pushing firmware updates out to the sensors. Goldman Sachs Global Investment Research 9

10 2. Middleware. MDM platforms or off-the-shelf middleware solutions help enable the integration to data repositories for storage and analysis. 3. Storing and securing the data. Solutions like Hadoop, NoSQL, RDBMS or other systems that can manage the structured and unstructured raw data that comes from the sensors, organize that data, and analyze the data sets. 4. Analyzing and visualizing the data. Next-gen analytics platforms like Splunk and business intelligence and visualization solutions like Tableau and Qlik allow business users to query the data. These front-end solutions enable users to derive insights and inform business decisions. Exhibit 9: The process of data flow in the enterprise allows for separate point solutions to succeed in each step: managing device communications, manipulating the data, storing and securing the data, and analyzing the data. Some devices will have custom applications that collect and store raw data from sensors, in an unstructured, unformatted context. Platform vendors market solutions to collect and aggregate data from connected devices, as well. Additional tools store and secure the data Analytics software allows for queries, reports, and visualizations that drive insight from the data Tools manipulate and organize the data, and load into a data store Source: Goldman Sachs Global Investment Research We believe that the most successful IoT enterprise software vendors in the near- to midterm will be the ones that carve out one or two of these steps and seek to master the solution, rather than those (if any emerge) who aggressively try to create a one-stop-shop to manage the entire data flow process. There are point solutions available that have been purpose-built for each step in the IoT data flow process, but some vendors and solutions like Oracle and Hadoop can overlap between the middleware and the data storage/security phase. We see the most overlap between the 2 nd and 3 rd steps of the process, and we believe that these two steps will warrant more spend than the other steps in many IoT implementations. We also see opportunities for vendors of SaaS solutions like Opower that leverage IoT data to create vertical-specific solutions for end markets like utilities. Goldman Sachs Global Investment Research 10

11 Exhibit 10: Sample list of vendors tied to IoT trends across our enterprise framework M2M Applications/Services (Data collection, Device mgmt, Telco provisioning) Analytics Technology Vendors (ETL, Data Warehousing, Hadoop, RDBMS, NoSQL, Visualization) Aeris Communications Jasper Wireless Basho dataspora Kognitio Qlik Axeda Kore Bime Datastax Loggly RelateIQ Cumulocity LogMeIn Birst Domo MapR SAP DeviceHive M2Mi centrifuge GoodData MarkLogic ScaleArc DeviceWise Mykoots Chart.io Hadapt MetaLayer Splunk Digi International Net4Things Cirro Hortonworks MongoDB Sumologic Ericsson Neul Cleversafe HP Oracle Tableau Eurotech PTC Cloudera IBM Palantir Talend Gemalto SeeControl Clustrix Informatica Pentaho TIBCO Hexagon Sierra Wireless Couchbase karmasphere Pivotal VoltDB Inmarsat Trimble Navigation Source: Goldman Sachs Global Investment Research. Managing the interconnectivity of devices One of the challenges that has emerged with the IoT is connecting to and managing 2-way communication with devices all over the world. Though we consider the IoT to still be in its infancy, the number of machine-to-machine (M2M) connections grew 23% yoy in CY14 to 1.7bn and are expected to grow at a 26% CAGR through CY18 to 5.3bn, according to Infonetics Research. Japan provides an excellent case study in M2M adoption, with NFC (Near Field Communication) technology being used by telecom carriers, home appliance manufacturers, and others, to enable machine communication. Japan leads the world in adopting M2M in vending machines, storage lockers, and other areas. Tanita and other medical equipment manufacturers have adopted M2M in healthcare equipment such as scales. Japan also utilizes M2M for gathering growing- and loading-related information in agricultural areas, and in river and coastline surveillance. As the IoT gains traction and more enterprises engage in deployments, we see the need for a platform to manage these connections becoming increasingly critical. That said, building the foundation for this communication in a highly scalable and secure platform can be a costly and complex task for enterprises to undertake. The communication to and from IoT devices needs to be reliable, fast, and secure; and so far no single standard or vendor has emerged to satisfy these requirements for an IoT communication platform. Further, IoT deployments have been highly customizable making it challenging to define a single communication standard. The need for M2M communications platforms Despite the plethora of challenges, innovative companies have emerged to help enterprises manage their communication with IoT devices and many of the large infrastructure vendors have partnered with these companies in trying to offer end-to-end IoT solutions. We currently don t see a large push from the large enterprise vendors to develop and offer their own IoT communication platforms which may speak to their perception of the size and profitability of the product offering. That said, while the large enterprise vendors don t seem to be currently developing their own IoT communication platform offerings, we don t leave out the possibility for these players to begin such initiatives in the future or acquire the startups as the market develops. Goldman Sachs Global Investment Research 11

12 What an IoT M2M communications platform does The companies that are developing IoT communications platforms are largely developing their offerings as cloud solutions. In our view, cloud deployments for this offering make the most sense (versus an on-premise solution) as a cloud deployment essentially outsources the management of IoT device communication and is more capital efficient, especially if an enterprise were to scale back on its IoT initiatives. We see the M2M platform companies providing the following solutions and services to enterprises: Agent software for the IoT device to make it easy to connect Cloud-based applications to manage devices, deliver remote services, and collect data Secure and scalable cloud platform to store machine data While M2M platform providers extend their services to next steps in the process such as analytics, integration with business applications (e.g., ERP, inventory systems), we see the core functions of the platform as stated above. We note that M2M platform providers partner with telecom operators to deliver M2M connectivity, which we see as a part of the service they provide to enterprises. M2M platform provider examples 1. Coca-Cola uses M2M platform vendor Jasper to monitor vending machines. Coca-Cola monitors the vending machines inventory levels and system uptime. Should a problem be detected, the vending machine s manager is notified. 2. Medical equipment provider, Leica Microsystems, uses startup vendor Axeda to monitor the uptime of its high-tech equipment. This IoT deployment reduced Leica s system downtime by 40% and increased and cut onsite visits by 33%. Monetizing the data growth explosion Perhaps the greatest impact on businesses from the IoT will come from the explosion of data generated by these connected devices. Gartner estimates that by 2020, there will be 26 billion connected sensors and devices constituting the IoT, globally. If each of those devices generated a daily amount of data equivalent to the first two sentences in this paragraph, IoT devices would be creating a volume of raw data roughly equal to the size of Netflix s entire video library every year. Gathering, aggregating, and analyzing this data in real time requires robust and versatile solutions, and many of the legacy database and analytics platforms are not well suited for use with the high volume of semi-structured and unstructured data. We believe the IoT will drive growth in Big Data and analytics solutions through three types of use cases: back-end database and data management tools, front-end visualization and intelligence tools, and data-driven vertical-specific software solutions. Determining which platform and solution is ideal for a given use case will be heavily influenced by the volume and type of data being generated. Goldman Sachs Global Investment Research 12

13 Back-end database and data management tools As we discussed in our January 28, 2014 note Big Data Storm clouds brewing, in our view, the software industry is undergoing the most transformational shift in data architectures in the last 30 years. This shift is driven primarily by the need for new architectures to handle the scale and complexity of data generated by machines, web traffic, social networks, and the ever-increasing number of connected devices. While much of the data generated by these connected devices will be at least semi-structured, we believe the sheer volume, if not the complexity, will make innovative Big Data solutions necessary for many IoT analytics implementations. Internet pioneers have driven the development of two new types of data platforms to handle the Big Data phenomenon analytic and transactional. Hadoop is a software framework that supports large-scale computing, and is largely used for analytic workloads. NoSQL (or Not only SQL ) database solutions encompass a next-generation of databases that are often used for transactional use cases. New technologies like Hadoop, NoSQL, and others have been developed specifically to deal with the large volumes of varying data types that are generated by multiple connected devices. These next-generation data platforms are not typically replacing legacy technologies, but they do represent much of the growth in new application development and will correspondingly impact some legacy vendors ability to grow. As these new technologies are largely being used for new deployments and new applications, we see them seeing outsize benefit from data generated via IoT initiatives. Exhibit 11 outlines where different projects and vendors play. Exhibit 11: Big Data technologies like Hadoop and NoSQL are meaningfully different, with Hadoop better suited for analytic use cases, and NoSQL better suited for transactional use cases. Both can be deployed to work with IoT data. Analytic Transactional Goal: Scale- out data platforms in which users can process and analyze large data sets economically for better decision making Use cases: Data warehousing offload Natural Language Processing Fraud analysis Threat Detection Call log analysis Data sandbox Recommendation Engine Goal: Data platforms that can be accessed at high speed with scale-out storage and query capabilities for unstructured data types Use cases: Social Media Sensor Data Smart Phone Data Machine Data Web logs Click Stream Video Technologies: Hadoop Map Reduce YARN Technologies: Mongo DB Cassandra Hbase Couch DB Redis Hadoop Stack Analysis & Visualization Vendors & Tools Palantir, Platfora, Tableau, Qlik Tech Database Type & Vendors Mongo DB: Mongo DB Couch DB: Couchbase SQL & Search Query File Management Hadoop Distribution Hive, Pig, Impala, Presto Cloudera Distribution with Hadoop (CDH) Hortonworks Data Platform (HDP) Map R Distribution for Hadoop (MapRFS) Cassandra: DataStax Riak: Basho Hbase: Cloudera, Hortonworks, Map R Source: Gartner, IDC, Apache.org; aggregated by Goldman Sachs Global Investment Research Goldman Sachs Global Investment Research 13

14 Use Cases in back-end data management 1. Monsanto uses Cloudera Hadoop for data processing and analysis. Monsanto has a number of sequencing and genotyping machines (level 2 devices, in our framework), all of which generate significant amounts of data, as Monsanto executes its genetic research on seeds and other life matter. To manage and perform analysis on this data, Monsanto maintains several Hadoop clusters, and utilizes Cloudera s management tools to run them. 2. UPS uses a proprietary ORION system for route planning, and driver training. UPS has developed an internal system, coupled with what it claims to be the world s largest DB2 deployment, to capture and analyze data it receives from wireless package scanners and sensors/gps devices on trucks (level 1 sensors). UPS s ORION system (On-Road Integrated Optimization and Navigation) began development in 2003, and uses proprietary analytics technology to plan optimal routes and monitor trucks, driving down fuel costs and delivery times, while providing insights into driver habits. For example, UPS can track speed, acceleration/braking habits, and the number of U-turns a driver makes to determine if that driver may need additional training. 3. NCR uses Aster for predictive maintenance and inventory management. NCR manufactures point-of-sale devices, ATMs, self-service kiosks, and other devices that would fall under our framework s level 2 criteria. NCR collects large amounts of sensor data from its machines that regularly report the condition of the device, operational status, and network performance, and marries that data to the help desk information it has, as well as service technician call log data. Analytics on this data helps NCR predict when certain parts are likely to need replacement before they fail, and allows NCR to know which parts fail most frequently so they can be kept at higher inventory levels and the machines will require less downtime. 4. SAP and SK Solutions Anti-Collision System. SK Solutions (anti-collision software vendor) developed an anti-collision system with the help of SAP s navigator 3D anti-collision system for construction site safety and efficiency. The SK Asteroid platform solution powered by HANA helps monitor the position, movement, weight, inertia, wind speed/direction and other equipment through a network of sensors. This data is analyzed in real time to assess potential risk and establishes a dynamic and temporal safety cocoon. The system then takes corrective measures via automatic pilot controls over each piece of equipment, avoiding potentially hazardous situations and preventing crashes. It has automatic alerts and provides easy navigation to the drivers that are in charge of the cranes. It also provides an interface for the relevant and important information to reach the designated vehicle without loss of efficiency. This minimizes crane downtime and maintenance and increases site safety and customer profitability. Who will handle the storage and security? The proliferation of connected devices, communications, and massive expansion in data create a challenge for data management and security professionals. Traditional security and data management vendors may benefit from the proliferation, but we see the greatest benefit to vendors who solve the security problems leveraging Big Data solutions like Hadoop. We see IoT implementations in many industries posing specific security risks, as each industry faces unique challenges and regulatory requirements. While a device like a Fitbit may not be required to abide by the HIPAA requirements of a more medically oriented monitor from a healthcare provider, there will still be privacy issues and reputational risk to Goldman Sachs Global Investment Research 14

15 either device manufacturer should a security breach occur. Additionally, command and control modules that interface with sensors at power plants or telco equipment could present particularly attractive targets for cyber attacks. We believe the security vendors who are most likely to benefit from IoT initiatives will be those who develop solutions to secure what are viewed as the most vulnerable points of the IoT infrastructure. In the SANS Institute s Securing the Internet of Things Survey in January 2014, roughly 75% of security professionals indicated that the most vulnerable points were the internet connection itself, or the command and control channel for the device vs. less than 20% device OS or the device firmware. This indicates that the number and variety of connected devices doesn t appear to be as big a concern to security professionals as the connections and infrastructure of the enterprise as it interacts with these devices. Exhibit 12: Half of survey respondents indicated that the connection itself would be the most vulnerable area for IoT implementations % of responses when asked to indicate the most vulnerable point of the IoT implementation Other, 7% Device firmware, 8% Device OS, 11% Device connection to the Internet, 50% Command and control channel to device, 24% Source: SANS Institute While we acknowledge that patch management and device/sensor control will be very important to enterprises that implement more complex sensors and devices, our view is that much of the necessary investment in securing these devices will take place within the enterprise infrastructure next-gen firewalls, APT protection, application control software, sandboxing/virtualization, and perhaps new technologies that are as-yet unknown. Analyzing and visualizing the data Managing, storing, structuring, and manipulating the data covers most of the heavy lifting involved in IoT data, but we see other opportunities in this theme as well. Being able to analyze that data and find trends and insights requires powerful, purpose-built applications. As such, we see significant tailwinds for successful business intelligence vendors that are able to effectively enable users to visualize and analyze data in legacy relational databases, as well as using Big Data technologies like Hadoop and NoSQL. We also see opportunities for companies that offer embedded business intelligence (BI), or white-labeled visualization products that can then be used as front-end interfaces or to add Goldman Sachs Global Investment Research 15

16 functionality to web portals. There are vendors, like Yellowfin, who specialize in whitelabeled embedded BI, but other BI vendors like Qlik, Tableau and SAP (Business Objects/Fiori) also offer co-branded, embedded solutions. We believe embedded BI could see a tailwind from IoT trends, as device manufacturers (particularly of consumer-oriented, level 2 devices) may not want to develop their own visualization tools. For example, the manufacturer of a health monitor/pedometer device could use an embedded BI tool to generate the graphs and visualizations on its web portal that show users their steps, calories burned, or other trends in activity. Tableau and Splunk partner for visualization of machine data Earlier this year, Splunk and Tableau announced a partnership whereby an ODBC driver would be released to allow access to data in the Splunk Enterprise directly from within Tableau. This will allow business users to see graphic representations of machine data maps showing GPS locations of a fleet of freight trucks in near real time, charted trends in downtime for machine parts, or graphed time series of biometric data from a health monitor and use those visualizations to drive business decisions or patient treatments. Tableau requires no IT involvement, and provides a highly intuitive software interface, allowing business unit managers to access the data-driven power of IoT initiatives without necessarily augmenting the IT budget of the enterprise. Vertical-specific analytics applications Vertical specific applications will bring together the two worlds of machine/sensor data with existing business processes, applications and practices. Theoretically, an infinite number of sensors connected to an infinite number of machines not only vastly increase data output but create the problem of an infinite number of outcomes that require multiple decisions. We believe this should create healthy demand for a new category of vertical applications which combine analytics to enable and help automate decision making around business processes. We have seen several companies that have developed solutions specifically tailored for a given end-market vertical, and while not a comprehensive list, we look to these examples as indicative of the large opportunity facing IoT software developers: Opower uses Cloudera Hadoop to run analytics on thermostat and meter data to drive energy efficiency for utility customers. Teradata uses data gathered from sensors in Volvo cars to run checks against diagnostic trouble codes and predict maintenance needs and warranty claims, lowering warranty costs, and improving customer relationships. IBM s Smarter Cities initiative is aimed at utilizing connected sensors across a city s infrastructure to optimize traffic flow, utilities, and a host of other use cases specific to urban environments. HP s Vertica offering powers Cerner s Millennium health care platform which helps providers optimize processes to speed the delivery of care and eliminate waste and error. Salesforce.com and Philips announced an alliance to develop an open cloudbased platform which enables the interoperability of medical devices and data and focuses on relationship management between caregivers and patients. The platform is intended to be open to developers and will enable workflow and collaboration on data on like electronic medical records. For example, the ecarecoordinator application enables healthcare providers to monitor patients with chronic conditions in their homes. Goldman Sachs Global Investment Research 16

17 Framing the consumer opportunity: a platform-centric approach In looking at the consumer IoT as it pertains to software vendors, the early stages of the market have been led by proprietary solutions vendors that focused on the key subcategories (i.e., Crestron, Savant, and Control4 for home automation). In addition, point solutions that integrate proprietary hardware and software for a specific use case have gained popularity; in home automation, this has included Nest Labs (acquired by Google), home music veteran Sonos, and home security solutions like ADT and alarm.com. In most of these initial use cases, hardware and software has been tightly integrated and crossvendor device communication has been limited to non-existent. As standards (or de facto standards) come into place, however, we expect the pace of innovation to accelerate as new functionality is enabled by communications compatibility and clearly defined APIs across various third party hardware and software providers. While it may seem natural to assume this process will destroy value for the early-stage vendors that offered vertically-integrated solutions, the potential increase in user adoption and available ecosystem innovation suggests this is not necessarily the case. New vendors and IoT veterans alike should have an opportunity to share in the value creation that should occur as the consumer IoT market takes off. Exhibit 13: Vendors will attack the consumer IoT from several different angles Sample of vendors touching the consumer IoT opportunity Automotive Connected Home Home Automation Control Wearables / Health Denso ADT Control4 Fitbit Garmin alarm.com Crestron Jawbone Harman ARCHOS Icontrol Nike Nokia Dropcam (Google) Revolv Pebble Nuance Nest (Google) Savant Philips TomTom NETGEAR SmartThings Polar Philips Wink Withings roku Sonos WeMo (Belkin) Source: Goldman Sachs Global Investment Research Some things are smarter than others A look at Exhibit 14 visualizes our view of the consumer IoT. We expect over-arching platforms to serve as central points for the wide variety of devices and sensors to aggregate. Nevertheless, given the vertical-specific nature of IoT applications, a broadbased platform approach may not capture the intricacies of individual use cases. As a result, we are seeing the development of sub-platforms which will exist under the wider umbrella of a vendor s over-arching offering. In addition, in order to more readily understand which layers of IoT sensors necessitate central platform ownership and which layers will likely be left to third-parties, we categorize sensors in three types according to the intelligence level of the device collecting the data. We see a device s level of intelligence corresponding to the strategic value of the platform vendor supporting the device s software and/or hardware. Goldman Sachs Global Investment Research 17

18 Exhibit 14: Our platform-centric approach to the consumer IoT Source: Goldman Sachs Global Investment Research. Digging into the three levels of sensor intelligence Categorizingg devices according to their levels of intelligence is central to our consumer IoT framework as we believe it will help investors understand the strategic value to a platform vendor s initiatives. Areas with low strategic value are best left to 3 rd parties, whereas areas of high strategic value will likely demand direct participation from the platform owner (i.e., Google, Apple, etc.). In defining the strategic value of an IoT device, we considered the value of the device s attributes as it pertains to i) operating system, ii) the data collected, and iii) the physical hardware. The following lays out analysis of the categorization of sensors. Level 1 sensor device single application, no user interface. We define a level 1 device as having a single application/function with no user interface, such as a heart rate monitor or running shoe sensor. We see the overall strategic value of a level 1 sensor device as being low to platform vendors according to the following attributes: Operating system value: Low. Given that level 1 sensor device don t have a user interface, we see low strategic value to owning the operating system. That said, we see the strategic value of an operating system increasing if the OS can be leveraged across more than one device and essentially enable devices to talk to each other. Information value: Low. On a standalone basis, we believe the data collected from level 1 sensors will have a low strategic value as the data cannot be easily linked to other IoT data (e.g., the shoe sensor may not be able to talk to the home security system). That said, if the OS can be leveraged across devices, the data will be able to as well, thereby enabling the building of richerr profile data on a customer. Goldman Sachs Global Investment Research 18

19 Hardware value: Low. Given the simplicity of these hardware devices and their single application functionality, we see the price points for these devices staying low. Level 2 sensor device single application, user interface. We define a level 2 device as having a single application/function but with a user interface, such as a smart thermostat or connected scale. We categorize the overall strategic value of a level 2 sensor device as medium to platform vendors according to the following attributes: Operating system value: Medium. Given that level 2 sensor devices have just one application, we see the operating system having limited strategic value. As is the case with level 1 devices, we see the OS increasing with value as it can be leveraged across multiple devices. Information value: Medium. On a standalone basis, we believe the data collected from level 2 sensors will have a low strategic value as the data only pertains to a single application. Again, if the OS can be leverage across devices, the data will be able to as well, thereby enabling the building of richer profile data on a customer. Hardware value: Medium. We see the user experience driven by the operating system as increasing the hardware value of level 2 devices, but see a high risk of commoditization for this sensor category. Level 3 sensor device multi-application, user interface, potential IoT hub We define a level 3 device as having a multiple applications with a rich user interface, such as Google Glass or Apple s iwatch. Ironically, this can also include traditional mobile devices such as smartphones and tablets as well, and Level 3 sensors can also be IoT hubs, serving as management platforms for multiple sensors. We see the overall strategic value of a level 3 sensor device as being high to platform vendors according to the following attributes: Operating system value: High. We see the use experience driven by the operating system as a critical factor to the device s adoption. Information value: High. On a standalone basis, we see the data collected by level 3 devices as having the highest strategic value as data collected across applications can be easily correlated. Further, we see the data value increasing if the OS can be leveraged across multiple devices. Hardware value: High. We see high price points for level 3 devices given the convenience and value-add of joining multiple applications under a single device. Across our three levels we see two over-arching themes; intelligence rises as sensors get closer to level 3 while platform agnosticism falls. Intelligence rises as sensors push toward level 3. Within our device/sensor framework, intelligence rises from level 1 to level 2 to level 3. This is to say that the more intelligent a sensor, the more likely it is to fall within level 3. Platform agnosticism falls as sensors push toward level 3. Similarly, with heightened intelligence in level 3 sensors, devices in the category are much more likely to benefit from attaching to a particular platform. Using our platform-based approach where key vendors provide a platform while simultaneously attempting to control strategic pieces of the ecosystem, we would expect major platform vendors to extend downward into the sensor space, while leaving the rest to third parties. While strategic certainly will have different meanings to different vendors, we would expect the biggest focus from platform vendors in level 3 sensors with Goldman Sachs Global Investment Research 19

20 select focus in level 2 as well. With low OS, information, and hardware value in level 1 we would expect limited interest from platform vendors, and third parties will likely dominate the software and hardware value in this segment. Smartphone OS evolution as a prelude to the IoT Before the advent of modern smartphones, the mobile phone application opportunity was dominated by the hardware vendor with preloaded programs to control the phone's functions (phone calls, messaging, calendar, etc.) as well as peripheral applications and services (games and music, for example). This essentially created a narrow platform for apps, controlled by the hardware vendor and with limited input from select 3 rd party partners. One of the biggest events that sparked the modern smartphone wars was Apple's introduction of the App Store in July For the first time on a sizable scale, a handset vendor opened up the device's operating system with easy-to-adopt APIs and provided a delivery mechanism for 3 rd party apps to flourish. Google followed suit in October of 2008 with the launch of the Android Marketplace (later renamed Google Play). Proliferation of apps was rapid, and by late 2009 the number of apps available on Apple's App Store had surpassed 100k. Exhibit 15: Both ios and Android saw rapid growth in apps driven by 3 rd party developers Apps available, ios and Android 1,400,000 1,200,000 1,000, ,000 ios Android Android and ios cross the 1 million mark 600, , ,000 0 Jul-08 Jul-09 Jul-10 Jul-11 Jul-12 Jul-13 Jul-14 Source: Company data and press reports. With these new app stores, the smartphone wars evolved from a fight over hardware feature sets to a platform-centric battle where success was not merely defined by an individual vendor's actions but rather by their ability to assemble a robust array of 3 rd party apps. There was strategic importance in platform vendors controlling several core apps that are important to both monetize and drive stickiness within the user base. As a result, the major OS vendors (namely Apple and Google) had to persistently decide where to draw the line between strategically important app categories they would need to own and the long tail of apps they would leave to the legions of 3 rd party developers. Apple s ios as an example Using Apple as a specific example, ios immediately leveraged the strength of the music platform developed around itunes and the ipod as a key differentiator. While 3 rd party music apps were also available (Pandora, Spotify, etc.), Apple clearly still views music as a Goldman Sachs Global Investment Research 20