Internet of Things and the Value of Tracking Everything
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This presentation was given to an executive MBA session at UCSD in April 2016. The session reviewed big data, internet of things, and how companies are gaining value from location, sensor, manufacturing and other data to make better business decisions.
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Internet of Things and the Value of Tracking Everything
- 1. MAKING BIG DATA COME ALIVE IoT and the Value of Tracking Everything Paul Barsch MAKING BIG DATA COME ALIVE
- 2. 2 Big Data Explained 4/5/2016© 2015 Think Big, a Teradata Company Definition: Datasets so complex and large that they are awkward to work with using standard tools and techniques Location Social Images Weblogs Videos Text Audio Sensor Of the 3 Vs – Which is the Most Important?
- 3. 3 Data Growth Source: IDC - sponsored by EMC. As the Economy Contracts, the Digital Universe Expands. May 2009 Transactions Interactions 1024 1021 1018 1015 1012 109 Yottabyte Zettabyte Exabyte Petabyte Terabyte Gigabyte
- 4. 4 IoT, Sensors, and Tiny Computers Internet of Things SensorsEmbedded computers Industry Specific (eg CatScans, etc.) Data Center systems
- 5. 5 • Events generating data – Vibration – Temperature, humidity – Wind speed, direction – Air/liquid flow or pressure – Location, navigation – Tilt level, rotation – Light, sound – Radiation, chemicals – Biological - Heart rate, blood pressure - Brain activity, chemicals – Inventory, sales (RFID) • Data format: JSON or proprietary The Data Sensors Collect
- 6. 6 • A new wave of devices will help you track health statistics but security and privacy concerns loom for this health “Big Data” Google Glass Jawbone UP Ingestible Sensors The Quantified Self
- 7. 7 Gartner: Growth of the Internet of Things Source: Gartner, Forecast: The Internet of Things, Worldwide, 2013, Nov 2013 Billions of Things in Use Connected PC, smartphone, tablet IoT 0 5 10 15 20 25 30 2009 2020
- 8. MAKING BIG DATA COME ALIVE How it Works
- 9. 9 Two Major Subsystems Operations of Things Analytics of Things © 2016 Teradata Things Gateway The Edge Networks Analytics Data Center Local | Cloud | Hybrid
- 10. 10 Industrial IoT – Reference Architecture Integrated Data Warehouse Data Integration Sensor Data Qualification Engine Streaming Reference Data Open-Source Monitoring and Management Acquisition / Operations Integration & Enrichment Asset (Vehicle/Machine/Engine) – Environment – Owner - Operational Data Connected Asset Notifications and Offers HLQL (Hive,Pig) Applicatio nData Marts Analytics, Discovery and Reporting Notification Services Location-based Services Infrastructure / Traffic Monitoring Services E-Commerce Dashboard Replacement Parts Mgmt Asset Health / Repair / Maint. Quality Warranty Analysis Telematics Tailored Apps/Services Remote Services (Fleet Mgmt, etc) External Information Behavioral Information Location Information Event Data Recorders Biometric Information Real-time Batch Near Real Time (Non Real-time) Onboard Sensors / Sensing Device Aster (Analytics Engine) QueryGrid Listener Governance and Security Onboard Diagnostics Master Data Management Asset Management Device Management Reference Management HDFS/ NOSQL Collectors Alert Filters Operational Rules Subscriber Information Vehicle to X Comm’n Usage/Behavior- Based Services Engine / Vehicle Prognostics Condition-based Maintenance Offerings ServicesAnalytic Apps Product Design/Test Engineering Monitor / Control Analytics Visual Anomaly Prospector App Center TAF Text/Deep Learning Analytics
- 11. MAKING BIG DATA COME ALIVE Case Studies
- 12. 12 A New Level of Intelligent/Safe Driving… © 2014 Teradata
- 13. 13 New Tech for Vehicles Focused on Safety & Autonomy Carmakers are facing seismic change. Suppliers which were largely kept under the hood are set to grow in influence as the industry adds more and autonomous features to vehicles Detects close range objects to aid parking and avoid collision by using radio waves Integrates driver assistance functions: algorithms for every scenario Semiconductors underpin advanced electronic functionality Seeks longer range objects for use in Adaptive Cruise Control systems Used in front and rear parking sensors in modern cars. Will be adapted for assisted parking and short range pedestrian/obstacle detection Enables in-car night vision systems that can detect objects further away than traditional headlights to avoid collisions at night Integrates both directional and distance information in lane departure systems and traffic sign recognition For precise navigation Allows vehicles to communicate with each other Advanced driver assistance systems Ultrasound Front/rear short radar Infrared Vehicle to vehicle communication Advance mapping Semi conductors Long range radar Stereo cameras Software
- 14. 14 • Customer centric experience – connected cars and hazards ahead • Dealer Analytics –Scorecards and Action Plan • Warranty/ Repair/ Failure Predictions • Add New Services • Featured Used/Not Used- learn for the future • Buy and customize online – most of sales activity is done prior to walk- in’s… “Gamechanging” Connected car Driver Model Case Study: IoT at Volvo
- 15. 15 What Does it All Mean? © 2014 Teradata New Applications Industrial Transformation
- 16. 16 Questions (For Discussion Purposes) 1. How can IoT & analytics be leveraged at your business? 2. IoT Readiness Technology People Process 3. What are implications for privacy? Tracking things Tracking people 4. What are implications for security? 4/5/2016© 2015 Think Big, a Teradata Company
Notas do Editor
- 3
- Not all sensors or micro computers are part of the internet of things. IoT mainly points to machines and devices that communicate over the internet via Wifi, 3G, 4G, or Ethernet expecting some receiving computer to use the data and provide assistance. But let’s start with the keyword here: THINGS. IoT does not include all the computer servers in the world. We already have that – its called a data center or a Local Area Network, or a home office network. The IoT name describes the trillions of THINGS that are not full computers but are emitting data. Also notice that sensors range from simple electronic devices to small embedded computers. The embedded computers are constantly growing in capacity due to Moore’s law and economics. So the sensor that detects the refrigerator door opening someday evolves to a micro computer keeping track of inventory inside the refrigerator. Similarly, the on board computer in a Ford Focus evolves to collecting data on driving habits, brake wear, and other maintenance or performance objectives. This can then be used within customer service, engineering and design or warranty to improve insights in these areas. The business question is how valuable is what specific data? There is a lot of redundant data - is all of it needed? If not, then how to determine what is valuable. Additionally, as sensors increase in computational power and calculations are pushed to the sensors, then derived data may become the most valuable.
- Sensors are most frequently used for MEASUREMENT. Types of measurements include: acceleration, tilt, shock, rotation, temperature / humidity / atmospheric conditions, pressure, vibration, strain, force, location, time (traffic movement, usage (minutes/hours of run, dwell, idle time)), engine data (e.g. speed/RPM), miles per gallon, component performance, diagnostic codes, location and status tracking, and more. Data transmission timing varies by sensor and network availability. It may be in real time, or it may be in batch. For example, many of the early onboard diagnostic sensors in automotive are uploaded in batch once they reach a dealership vs streaming. Sensors collect all kinds of data. JSON is one of the more common data formats because its small and easy to implement. However, many manufacturers of sensors emit proprietary data formats along with an API programming manual. Its not hard to work with the proprietary data but its also not as easy as selecting an icon in Informatica’s PowerCenter or in DataStage. If the data format is JSON, use Teradata 15.0 or an ETL tool to easily consume the data.
- The growth in IoT will far exceed that of other connected devices. By 2020, the emergence of mass market smartphones and tablets, combined with the mature PC market, will result in an installed base of about 7.3 billion units, which compares with the expected human population of 7.7 billion in that year (based on information from the United Nations Population Division). In contrast, the IoT will have expanded at a much faster rate, resulting in an installed base of about 26 billion units at that time. Installed base is important because it drives the value of service revenue, aggregate communications bandwidth and data center activity. Due to the low cost of adding IoT capability to consumer products, we expect that ghost devices with unused connectivity will be common. In addition, enterprises will make extensive use of IoT technology, and there will be a wide range of products sold into various markets, such as: Advanced medical devices, including surgical tools, instrumentation and wearable medical sensors/monitors, and ingestible devices, such as smart pills Factory automation sensors and applications in industrial robotics Sensor motes for increased agricultural yield Automotive sensors Infrastructure integrity monitoring systems for diverse areas, such as road and railway transportation, water distribution, and electrical transmission The most popular IoT sensors initially installed are those on items that provide remote monitoring (eg. wind turbines, ATMs/ kiosks, heavy equipment, drilling equipment, etc.) or are mobile (autos, aircraft & engines, people, packages, rail, medical equipment, etc.). This will evolve quickly.
- I want you to take away a VERY SIMPLE view of what Internet of Things is. So here is a very simple diagram that will clarify it. The Internet of things is an expansion of our notion of the internet, to include things….large and small. And the things are able to, through sensors and tiny chips embedded in them, communicate their state. And they do this through a series of networking devices called Gateways, which aggregate and package up the data for sending onward, networks themselves, that convey the sensor data either to Public Clouds, or Private Data Centers. So that companies can do analytics on them. That’s the simple view. What is terrifying companies – our customers, is the prospect that this volume of data is yet again another couple of orders of magnitude bigger then when it was just the internet of people and their devices. The data produced by the IoT is doubling every two years. This creates a "fear" in our customers (budgeting, complexity, executiion, opportunity loss) What kind of analytics willl be done in the edge? That’s the $64k question.
- Event Message (DEMN): DENMs are event-triggered messages broadcasted to alert road users of a hazardous event. Both CAM and DENM messages are delivered to vehicles in a particular geographic region: to the immediate neighborhood in case of CAMs (single hop), and to the area affected by the event for DENMs (multi-hop). Safety Messages – sent from Vehicle Probe Data: Data collected from sensors on the car or on the road.. Summarized vs detailed.. There are several papers on how to “summarize” the data through algorithms and modeling ADAS: Advanced Driver Assist DMA: Smart phones communicating with vehicle in support of safety, transportation data, smarter travel etc.. Slef-Service – schedule service apporintments, user reset alerts Usage based services –used car valuation..cross brand usage if own other brands Telematics tailord services – insurance, parking
- In addition to delivering an enhanced level of in-vehicle consumer services, Teradata also has the ability to capture, analyze and report on vehicle performance sensor data to determine potential mechanical failures BEFORE they happen. This level of predictive maintenance allows Teradata to help car makers improve vehicle safety, maintenance and customer satisfaction.
- Today new vehicles are equipped with hundreds iof diagnostic sensors that are equipped to capture data and alert drivers of pending or actual parts/system failures. These sensors monitor everything from engine performance to breaking, steering, blind spot detection, inclement weather and driver alertness in the vehicle. As these sensors transmit large volumes of vehicle performance data, its important to have an analytics system in place to detect potential failures and determine the size and scope of a potential problem across multiple vehicles or an entire model fleet. Often a vehicle part failure across multiple vehicles or an entire fleet can trigger a root cause investigation. The ability to quickly and easily capture, analyze and triage a maintenance problem is often the difference in both improving driver/passenger safety and mitigating large scale vehicle outage interruptions.