Finding purpose in the Internet of Things

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1 BUSINESS WHITE PAPER Finding purpose in the Internet of Things An abundance of data from an abundance of things is of little value in itself. The value lies in what you do with it. TABLE OF CONTENTS 1 Introduction 1 Not the what, but the why 1 Sorting out the use cases 3 Capture, analyze, act 4 An architecture for engaging with the IoT 6 Seizing the opportunities in the IoT 6 Summary 7 For further reading Introduction There s a temptation to think about the Internet of Things (IoT) as a vast and formless universe of connectable devices and free-floating data. If we can connect to a large number of devices and capture and store a large quantity of data, perhaps we ll find some interesting things. And surely, because we re also tempted to think that bigger is better, the more device connections we can make and the more data we can store for analysis, the more likely we will be to discover something significant. This temptation drives many of today s initiatives to connect to IoT devices and to consolidate and aggregate data, but at Software AG we believe these efforts to be insufficient. The question is not what you might find if you could connect to and look at everything, but what you can find when you can analyze the right data from the right devices. This white paper looks at the IoT from the perspective of real-world use cases and emerging opportunities. It explores how an organization can interact with the IoT to deliver real value and solve real challenges. Not the what, but the why When we talk about the IoT, implicitly we re talking about a world in which we can capture data from and, at some level, interact with an enormous range of things from the Fitbit on your wrist, to the sensors in your car and phone, to the heating system in your office and the traffic cameras at the intersections. That list doesn t even begin to scratch the surface, either. Factor in everything from smart coffee pots to nuclear power plants and you begin to grasp the scope of the IoT. ABI Research projects that by 2020 the IoT will consist of more than 24 billion connections among devices we use at home, at work, on the farm, in the factory, on vacation, everywhere. Cisco places the number of connections above 50 billion by the same date. Indeed, wherever there is activity there is data, and wherever there is data, there s a very good chance that someone is already trying to snap an Application Programming Interface (API) on it to bring it into the IoT. 1 ABI Research, IoT Analytics: Using Big Data to Architect the Products and Services of Tomorrow. 2 Cisco, The Internet of Things: How the Next Evolution of the Internet is Changing Everything But set aside the sheer volume of data and the number of connected devices for a second. The important question to ask is not whether this or that device is connected. It will be, if it is not already. The important question is why interacting with that device or gaining access to that data is of value. What problem can you solve by accessing that device and its data? What service can you create or enhance as a consequence? Until you have a clear vision of how and why connecting to that device can make a difference, you may be putting the solution before the problem and the history of high tech is littered with great solutions that failed because the problem they were solving had not been identified or understood.

2 Sorting out the use cases There are a number of broad scenarios in which data from the IoT can create and expand opportunities. Four spring to mind immediately: Predictive maintenance Smart metering and manufacturing The connected customer Smart logistics Within each of these areas there may be innumerable discrete opportunities and solutions, but within each broad use case, we find certain similarities. In the predictive maintenance scenario fall those activities that involve the continuous analysis of machines, equipment or infrastructure in order to ensure uninterrupted operation. A wide range of devices, sensors, data types and communications protocols inform this scenario. These contribute to its complexity but also draw attention to the value that predictive maintenance solutions can deliver: The costs that can be saved by averting an outage or minimizing downtime in these complex environments can be enormous. And those costs are not merely measured in dollars, euros and yen. Customer satisfaction and brand reputation are critical currencies that can be influenced by an improvement in equipment maintenance using predictive techniques. The smart metering and manufacturing scenario involves capturing live sensor data from a myriad of devices and systems to gain real-time insights into the state of operations in production environments. The scenario considers everything from the performance of individual machines on a shop floor to the discovery of energy theft in a public utility setting. Here, the ability to gain information from and to interact with a wide range of devices can identify subtle anomalies, identify opportunities to reduce process waste, and improve product quality. In turn, these outcomes can reduce the cost of operations and/or increase the service revenues derived from a product because of its improved quality. One outcome or the other by itself can improve the profitability of operations; if you can achieve both outcomes, you may be able to improve profitability even more. Use cases in the connected customer scenario typically involve capturing and enhancing data from sources that are intimately related to an individual. A connected customer event might be triggered, for example, when a sensor detects a network beacon from a mobile phone. The moment that phone connects to the network in a shopping mall or office building, amusement park, train, etc., a number of processes might be triggered to bring up historical data from internal databases, streaming data from weather feeds, social media feeds or any number of other feeds, and then analyze all these data points, in real time, to perform actions on behalf of that individual or to present offers specifically targeted at that individual, and so on. The effect of your interaction with that data might be to enhance the individual s life by streamlining some activity he has engaged you to manage or to present him with one or more revenue-generating offers he has a high probability of finding worthwhile. Either outcome should improve customer loyalty and brand satisfaction. And the smart logistics scenario? Here there are opportunities built around real-time insights into the movement and location of assets. The assets in question could be anything from a taxi on the street to a tanker on the high seas; it could be a delivery or service vehicle, or it could be a collection of individuals with very specialized skills who can t all be in all places at once. When you combine real-time awareness of an asset s location with relevant situational data, you can improve decisions about next steps or see and seize fleeting opportunities to engage a customer in a meaningful way. In the end, the ability to capture and make use of data from the IoT with the goal of making better logistics decisions enables you to save money and create and deliver services that are more streamlined yet more flexible, more honed to customer satisfaction and the delivery of an excellent customer experience. 2

3 Capture, analyze, act Each of the use cases we ve outlined may involve information arising from a very different set of sensors and sources. Some of the data may come from industrial controls, while other data comes from mobile phones and bar code scanners. Some may arise from local sources; others may arise from sources scattered around the world. What connects each of these scenarios, though, is the need not just to capture the data but also to analyze that data in very particular ways before an action can be taken. Business White Paper Internet of Things The challenge of data capture is not a new one. Companies have been struggling to bring data from one machine or service into another throughout the history of data processing. With increasing reliance on open or industry standards (such as the MQTT or AMQP protocols), an increasing number of devices is capable of producing data for consumption by other machines (and vice versa). Even data from sources that are more or less unstructured from social network feeds, for example can be drawn into the analytical mix. There are conventions and tools that can help a consumer of such feeds understand how to integrate the data in those feeds. Viewed this way, data capture is largely a matter of integration. Moreover, it is only one area of integration among many that will be crucial to an organization. You will also be integrating some number of enterprise systems, whether on-premises or cloud based, in most real-world scenarios. After all, most IoT data adds value when combined with other, more conventional, data such as data on orders, shipments, payments and so on. Integration with enterprise data ensures you don t send out a technician to fix a machine that was replaced by the customer the week before. The area of data analysis poses more challenges and far fewer proven solutions. Until just a few years ago, data analysis focused largely on the search for trends, patterns and answers to specific, challenging questions that could be found in vast pools of static historical data. With more data, it was assumed, we could get better answers, so we developed databases and data warehouses and data marts. Analytical frameworks such as Hadoop came into play, enabling us to look for answers in even larger pools of data yet without the overhead required by the specialized data analysis tools and architectures that had been developed up to that point. Analytical databases and technologies such as Hadoop play an important role in the IoT. They can indeed help us see patterns and trends that might not normally be visible. They can help an organization segment a user population into small groups and analyze behavior at a highly granular level. They can help an organization plow through large volumes of sensor outputs to discover trends and patterns that are lost in the noise of the signal itself. And with those insights an organization can understand what to look for when implementing a predictive maintenance play or even a smart metering and manufacturing solution. What they can t do though is to analyze, in real time, the stream of data rushing in from all the devices being monitored in a predictive maintenance or smart metering use case. Hadoop is a batch mode tool and its use case is confined to an analysis of static data. It can analyze vast pools of data very quickly and efficiently, but it cannot analyze real-time data to spot the trends or patterns that it can see in the static data. Indeed, it is not enough to look for patterns indicating that a significant event has already surfaced. You need tools that can help you take action while there is still time to do something about the impending problem or the soon-to-be-missed opportunity. In a predictive maintenance scenario, for example, you need a probabilistic model of the remaining useful life of the equipment, not just a set to tools that throw an alert when the equipment fails. Such predictive models must leverage historical data as well as real-time performance data in order to learn and re-learn performance patterns and become more accurate. And, the models must be implemented so as to support the low-latency, high-throughput requirements of IoT use cases. This combination of sensors, captured data, existing business systems, and historical and real-time analytical tools can position an organization to act effectively in any of the use cases we ve touched upon from smart metering and manufacturing to the connected customer and 3

4 smart logistics scenarios. You need to: Connect to the appropriate sensors Have some idea of what you are looking for Have a mechanism for watching for those events or indicators in real time Then have a plan that can be executed when those events or indicators are discovered in the course of real-time analysis of the data stream An architecture for engaging with the IoT When described in this manner, it becomes very clear that an architecture, rather than a specific tool, is required to enable an organization to seize the opportunities bound up in any of these IoT scenarios. Given all the different components that must work together to enable an organization to act in any of these scenarios, a conceptual framework that reflects an understanding of how all the different components should work together is critical. Enterprise IoT Solutions & Applications Predictive Maintenance Smart Metering & Manufacturing Connected Customer Smart Logistics Enterprise Applications & Services Partner Networks & Eco-Systems IoT Analytics, Logic and Process Integration IoT Data Collection Business Systems IoT Data Sources Figure 1: A high-level view of an IoT architecture In the figure, the data sources occupy the lowest level of the stack, and they interact with the IoT data collection components. Above that, real-time analytic applications scrutinize data streaming through the IoT data collectors and, when encountering a match, trigger a response in the enterprise applications and services at the top of the stack. Business systems from SAP, Oracle and others, as well as historical analytical tools such as Hadoop, connect into the architecture through integration points in the upper levels of the architecture, as might other tools and industry-specific solutions from the broader ecosystem. As noted, the architecture in Figure 1 provides a very high level view. From the perspective of Software AG, there are additional architectural components that are crucial. 4

5 Enterprise IoT Solutions & Applications Business White Paper Internet of Things Predictive Maintenance Smart Metering & Manufacturing Connected Customer Smart Logistics Application Deployment Portals & APIs Data Visualizations Partner Networks & Eco-Systems Agile Business Process Management Predictive Analytics & Machine Learning IoT Services Enterprise Integration & Enrichment Visual Analytics Complex Event Processing In-Memory Data Management Ad-hoc Historical Analytics Business Systems Lightweight Messaging Embedded Agents/Adapters Data Collection Device Integration Edge Analytics IoT Data Sources Figure 2: A more detailed view of an IoT architecture From the perspective of the IoT experts at Software AG, the details matter. Given the varying types of data that could come from different IoT sources, an IoT architecture needs to consider ingestion adapters and a messaging infrastructure for moving data among the components that are analyzing and acting upon it. The more detailed view also indicates a place for lightweight analytical tools that can capture and extract metadata about the data being passed into the IoT services architecture itself. Within the IoT architecture, Software AG identifies roles for tools and services to integrate and enrich data from sources such as customer profiles and sales histories. If your streaming analytical tools detect that a known customer attaches to a WiFi hot spot in one of your stores, your IoT tools will need to enrich that data point with the known history of that customer s interaction with the store and any profiling information about that customer that may already have been performed. Similarly, the Software AG IoT architecture also identifies places for services that perform in-memory data management functions and ad-hoc historical analytical tools. It incorporates predictive analytics as well as complex event processing tools. These latter tools provide the means to trigger actions based upon the detection of specified patterns, trends and data points within the rushing stream of data from the IoT data sources themselves. Add into this the role for agile business process management tools and visual analytics for providing a way to present the significance of a given event visually and you can begin to appreciate how many components are required, all working together in a coherent, efficient and organized manner, to act in real time, in just the right way, upon the events that present themselves to an organization every day. Seizing the opportunities in the IoT All the data in the world is of little value if you don t know what questions you are trying to answer or what opportunities you are trying to seize. Yet once you know all this, then much of the data in the IoT is superfluous. What you need then is data of a very specific nature from this group of sensors or that group of data feeds, from this customer profile or that public database. The magnificence of the IoT lies not in the sheer volume of data and connections that it contains but in the timely access to the right data that it makes possible. With access to the right data sources, with tools to capture, manipulate and make sense of that data in real time, and with an architecture designed to enable a well-considered, realtime response to the opportunities presented by a specific situation, you are well-positioned to succeed in the IoT. Software AG can help you in each of these key areas. We understand the requisite architecture. We can provide the key components that map into the architecture and enable an organization to build out the infrastructure required for the IoT, including: 5

6 Best-of-breed integration technology to access internal systems and external data sources, bringing together all the context needed for interpreting and reacting to IoT sensor data at high speed Award-winning, optimized streaming analytics for high throughput and low latency Visualization tools to move, manage, analyze and visualize IoT data in real time Tools to link real-time insights and predictions with context-aware actions and resolution processes to enable proactive IoT applications All powered by a high-performance in-memory data fabric The IoT architecture described by Software AG can be tailored to support any different IoT use case scenario, and the rich range of data collection tools and ecosystem partners ensure that it can help any organization build out a powerful IoT solution on-premises, in the cloud or in a hybrid configuration that connects the on-prem and cloud-based worlds. At the same time, Software AG s IoT experts can help you build out that infrastructure in a modular, staged manner that takes into consideration the realities of your existing infrastructure investments as well as your evolving business and financial plans. You can build on what you have to get where you want to go. Summary Ultimately, the promise of the IoT lies not in some mystery that will become revealed if we can connect enough devices and store enough data. The promise of the IoT lies in the novel ability to connect to enough of the right devices and to be able to use the insights enabled by those connections to accomplish certain goals that we have not been able to accomplish as reliably, as effectively or as efficiently in the past. The key to success when considering the IoT is to keep those goals in mind. What are you trying to do better than you could do before? What opportunities does a connected world now enable you to seize? With an awareness of what you want to accomplish, you can move forward with deliberate steps. You can determine which four or five (or 50) devices among the millions from which you could choose will provide the insights you need to succeed. The rest you can ignore. What becomes critical then is the architecture of the solution infrastructure supporting your efforts. The IoT architecture outlined in this paper has been designed with tangible goals in mind. It combines flexibility and agility, the ability to gain insights from vast pools of still data as well as from streams of rushing real-time data. It enables the ability to act in a timely manner to achieve your goals. In the end, it s an architecture designed to help an organization make the most of the IoT to achieve clear and compelling results. For further reading 1. Internet of Things Hype? Video featuring Forrester analyst Mike Gualtieri: 2. Tailor Your Customer Experience Video featuring Forrester analyst Mike Gualtieri: 3. Software AG IoT Predictive Maintenance Solution Brief: 4. The Forrester Wave: Big Data Streaming Analytics Platforms, Q3 2014: 5. Apama Streaming Analytics page on the Software AG website: default.asp 6. Apama Streaming Analytics Fact Sheet: 7. Apama Location Aware Performance Fact Sheet: 6

7 Contact your Software AG representative today to learn more about how Software AG can help you make the most of the Internet of Things while concurrently facilitating the transformation of your organization to meet the dynamic demands of the 21st century. 7

8 Find out how to power up your Digital Enterprise at ABOUT SOFTWARE AG Software AG helps organizations achieve their business objectives faster. The company s big data, integration and business process technologies enable customers to drive operational efficiency, modernize their systems and optimize processes for smarter decisions and better service. Building on over 40 years of customer-centric innovation, the company is ranked as a leader in 14 market categories, fueled by core product families Adabas-Natural, Alfabet, Apama, ARIS, Terracotta and webmethods. Learn more at Software AG. All rights reserved. Software AG and all Software AG products are either trademarks or registered trademarks of Software AG. Other product and company names mentioned herein may be the trademarks of their respective owners. SAG_Internet_of_Things_8PG_WP_Mar15