IIOT FOR THE INTELLIGENT ENTERPRISE - "LEVERAGING BIG DATA, TELEMETRY, AND AI" TONY MARESCO - MICROSTRATEGY
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
IIoT for the Intelligent Enterprise
“Leveraging Big Data, Telemetry, and AI”
Tony Maresco
1 Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .
Topics • By Many Names • Telemetry • Analytics • Value Through Outcomes • Big Data Architectures • Use Cases • An Example • Summary • Q&A
Safe Harbor Notice
This presentation describes features that are under development by MicroStrategy. The objective of this presentation is to provide insight into
MicroStrategy’s technology direction. The functionalities described herein may or may not be released as shown.
This presentation contains statements that may constitute “forward-looking statements” for purposes of the safe harbor provisions under the
Private Securities Litigation Reform Act of 1995, including descriptions of technology and product features that are under development and
estimates of future business prospects. Forward-looking statements inherently involve risks and uncertainties that could cause actual results
of MicroStrategy Incorporated and its subsidiaries (collectively, the “Company”) to differ materially from the forward-looking statements.
Factors that could contribute to such differences include: the Company’s ability to meet product development goals while aligning costs with
anticipated revenues; the Company’s ability to develop, market, and deliver on a timely and cost-effective basis new or enhanced offerings
that respond to technological change or new customer requirements; the extent and timing of market acceptance of the Company’s new
offerings; continued acceptance of the Company’s other products in the marketplace; the timing of significant orders; competitive factors;
general economic conditions; and other risks detailed in the Company’s Form 10-Q for the three months ended September 30, 2018 and
other periodic reports filed with the Securities and Exchange Commission. By making these forward-looking statements, the Company
undertakes no obligation to update these statements for revisions or changes after the date of this presentation.
Copyright © 2019 MicroStrategy
Copyright Incorporated.
© 2019 MicroStrategy Incorporated. AllAll Rights
Rights Reserved.
Reserved .
By Many Names Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .
Internet of Things or ….. • Internet of Everything • Web of Things • Industrial Internet of Things • Enterprise Internet of Things • Consumer Internet of Things • Your Internet of Things • Smart Planet • Second Digital Revolution • Industrial Revolution 4.0 • Industry 4.0 • Second Machine Age • Thingalytics
Industrial and Enterprise Internet of Things
A working definition
The Internet of Things (IoT) is the internetworking of physical devices,
vehicles, buildings and other items—embedded with electronics, software,
sensors, actuators, and network connectivity that enable these objects to
collect and exchange data. The enterprise IoT focuses on the use by
corporations and businesses and adds analytics, smart decisions and actions.
The Industrial Internet of Things (IIoT) is the use of Internet of Things (IoT)
technologies in manufacturing. ... In manufacturing specifically, IIoT holds great
potential for quality control, sustainable and green practices, supply chain
traceability and overall supply chain efficiency.
Efficiency Predictive Prescriptive Recommendations Costs Revenue Personalized
Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .
The Basic Idea • High volume/velocity data from sensors, machines, smartphones and social media is continuously captured and stored. • Stored “Big Data” is accessible for traditional Historical Analysis • In addition, models are built with advanced analytics to surface actionable insights in a form that can be run on live data. • Models execute in the data streams to trigger actions when opportunities or threats are predicted. • This process is forever repeated so existing models are refined and new ones discovered.
IoT Reference Architecture for MicroStrategy
Sensors – Streaming using Rules – Operational Analytics and Alerts – Historical for model building
Stream Processing Actions
Sensor Data Applications Actuators Controllers
Operations
Transactions Real-Time Monitoring
Social Media Intelligence Server
Transaction Services
Analysts
Alerts
Semi/ MicroStrategy Web Operational Monitoring
Unstructured
DM Models MicroStrategy Mobile
DM Models MicroStrategy Library
Real-time
Events Data Scientists
MicroStrategy Badge Historical Analysis/EDA
Streaming Analytics Platform Analysis
Relational Hadoop & Cloud- Enterprise Personal or
Databases Big Data Based Data Applications Departmental
Enterprise Data & Business Applications Machine Learning
We Could Monitor..
Connected Car
Smart Grid
Support Case Metrics
We Can Look Back at History
How Can We ?
• .
With Analytics….Telemetry Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .
Like Mission Control … But Affordable
Small and Low – Cost Sensors
I
Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .…Low Cost Fast Networks + Big Data Analytics
Exponential increase in the ability to gain insight and take action – Analytical Nirvana
Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .My Chevy Volt OnStar Report
Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .Analytics Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .
Characteristics of I/EIoT Related to BI and Analytics
Optimization Get the most bang for the buck.
Predictions What is likely to happen based on past history?
Aggregation What is happening in the aggregate – windows of time ?
Time Series How are we doing now vs. yesterday, last week, last year ?
Trend Analysis What direction are we headed in?
Correlation What factors influence activity or behavior?
Analytics in the Stream There isn’t time to store and then calculate
Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .Drivers
Low-latency From monthly loads to a seconds or less…
Descriptive to Prescriptive Descriptive -> Predictive -> Prescriptive.
Alerts and Action Driven Ready, optimized actions to problems and opportunities.
Analyze the Population Actual and all behavior, product use.
Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .The IIoT Opportunity for Different Analytics Types
Economic value by creating smart devices, prescriptions and actions - not just collecting data
Ingest Historical Predictive Prescriptive Actions
New revenue potential by tying into more of the product life cycle with timely advice and actions.Value Through Outcomes Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .
Some Early Economic Predictions
• 4.9 billion devices in 2015 and 25 billion by 2020 (Gartner)
• 14.8 billion devices in 2015 and 50 billion by 2020 (Cisco)
• 2.77% of estimated 1.9 Trillion potential connected devices
• Economic value of $14.4 Trillion in private sector and $4.6 Trillion in the public sector in the
next decade (Cisco)
• $1.46 Trillion market in 2020 up from $700B in 2015 (IDC)
• $1.9 Trillion Economic value add (Gartner)
• $ 7.1 Trillion IoT Solutions Revenue (IDC)
This just scratches the surface – see http://postscapes.com/internet-of-things-market-size
Numbers have been and are all over the spectrum….and what do they mean ?Breakdown of Early Cisco Economic Value
• Private Sector $14.4 Trillion
• Asset utilization $2.5 Trillion
• Employee productivity $2.5 Trillion
• Supply chain and logistics $2.7 Trillion
• Customer Experience $3.7 Trillion
• Innovation, including reduced time to market $3.0 Trillion
• Public Sector $4.6 Trillion
• Employee productivity $1.8 Trillion
• Connected militarized defense $1.5 Trillion
• Cost reductions $740 Billion
• Citizen experience $412 Billion
• Increased revenue $120 Billion
Focus on the areas impacted…not necessarily the numbers…The Benefits • Optimize operations to increase efficiency • Avoid Threats • Increase revenue • Supercharge the customer experience • Compress and inform product and service design and development
Big Data Architecture Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .
IoT Reference Architecture for MicroStrategy
Sensors – Streaming using Rules – Operational Analytics and Alerts – Historical for model building
Stream Processing Actions
Sensor Data Applications Actuators Controllers
Operations
Transactions Real-Time Monitoring
Social Media Intelligence Server
Transaction Services
Analysts
Alerts
Semi/ MicroStrategy Web Operational Monitoring
Unstructured
DM Models MicroStrategy Mobile
DM Models MicroStrategy Library
Real-time
Events Data Scientists
MicroStrategy Badge Historical Analysis/EDA
Streaming Analytics Platform Analysis
Relational Hadoop & Cloud- Enterprise Personal or
Databases Big Data Based Data Applications Departmental
Enterprise Data & Business Applications Machine LearningIIoT Architecture Incorporating Streaming Data From Sensors
Kafka is the most popular approach replacing proprietary technologies and slower message brokers
Application
Servers
Kafka Cluster
PROCESS, ANALYZE, SERVE
BATCH STREAM SQL SEARCH SDK
UNIFIED SERVICES
RESOURCE MANAGEMENT SECURITY Enterprise
DATA Data Warehouse
OPERATIONS
MANAGEMENT
FILESYSTEM RELATIONAL NoSQL
STORE
BATCH REAL-TIME
INTEGRATE
Cloudera, Inc.Example Applications at Each Level
Latency requirements are shorter the closer you are to the device
From https://www.gerenewableenergy.com/wind-energy/technology/digital-wind-farm.htmlEdge Analytics Analytics exists at the edge for real-time action and in the data center for historical analysis and modeling
A New Real-Time Approach
Push to Cube – Push to Viz
Kafka Notification Receiver Real-time engine Data Broker
MSTR Cube
Cube Socket Connected visualizations
Definitions/Data rooms
Broadcast data
Push data Success
to cube response
Execute
Cube
Create or New visualization request
join room
Data Stream Success
notification
Drop zones
Attributes Metrics
Request room to
kafka satisfy data request
31 Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .REST Allows You to Push Data Into Cubes
Integration of R with Spark using SparkR or SparklyR
Storage: HDFS, RDBMS, Hive, HBase,
Cassandra, MongoDB, SOLR, Elastic,
Other NoSQL
Sources :
unstructured, semi-
structured, structured
Leveraging Spark
• R integrates to Spark with SparkR or SparklyR
• MicroStrategy can execute RScript that includes packages using a metric
• You can separate model generation from model scoring
• These scripts can be used for algorithm training as well as to generate statistics
for exploratory data analysisLeveraging alerts and transactions for actionable insights A streaming application can trigger alerts and transactions can invoke processes or control devices
Use Cases Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .
An Example Taxonomy of the IOT
Machina ResearchPredictive – Condition Based Maintenance • Features that predict failures are identified to build models and then equipment is monitored in real-time using on-board sensors • Historical data is captured for continuous modeling. Did we miss a failure ? Did we prematurely predict a failure ? • Different instances of the same equipment type may behave differently and require unique models • It is cheaper to maintain when needed than at prescribed times. • It is cheaper to outfit products for predictive maintenance than to face machine failure, loss of business or other • Once you can predict failure, you can evolve business models that sell uptime
Guaranteeing On-Time Arrival of Trains at Renfe Get there on-time or get your money back on the Alta Velocidad Espanola train. • Contracts maintenance of trains and tracks to Siemens AG. • Siemens maintenance operations monitor and anayze data from hunderds of sensors on both trains and tracks to detect malfunctions that cause service disruptions • Schedules necessary remedial maintenance in advance of failure • Monitor vibration, heat, sound and many other sensor based measurements http://www.siemens.com
Selling Time-On-Wing for Jet Engines Rolls-Royce’s TotalCare service • Suite of maintenance and repair services for company’s commercial jet engines including continuous monitoring of engine health metrics • Sells time-on-wing availability rather then the engines themselves • Assumes all costs for maintenance and repair • Airline replaces operational costs for capital costs • Airline reduces operational costs for maintenance and repair and streamlines parts inventory. http://www.rolls-royce.com
Frictionless Retail • Beacons locate shoppers in the store and determine which products are close by and the identity of the shopper • A smart shelf enables RFID/NFC-tagged products to be automatically sensed when picked up by a shopper. The combination of store shelf sensors, smart displays, digital price tags and high resolution cameras makes it possible for retailers to see what is on the store shelf and in the back stock room and link these two sets of data. • Identity, location and historical information allow personalized offers to be offered in realtime. • Behavior is captured to improve merchandise and merchandising • Remove delays or frustrations from the supply chain and best-serve the customer in the store.
Improving Banana Crops Production • Deployed wireless sensor network • Measure digital humidity & temperature, soil moisture, soil temperature, trunk diameter, fruit diameter, pluviometer, solar radiation, ammonia • Cut down costs, increase quality and quantity of harvesting, ease routine activities of farmers • Guarantee production and competitiveness • Harvesting projections, optimize water usage, prevent plagues and diseases, reduce fertilize consumption, catalog soils depending on climate and culture http://www.libelium.com
Fish Farm Monitoring • Vietnam fish farms needed to establish tougher control measures on the quality of fish and the farming conditions. • PHA Distribution deployed wireless sensor network • Monitor different parameters to monitor water quality and prevent diseases • Real-time monitoring raises awareness of preventable diseases to save disease treatment costs, keep fish in good health until harvesting and to minimize fish loss • Monitors temperature, conductivity, dissolved oxygen, oxidation-reduction potential, pH, ammonium ion, nitrate ion, nitrite ion. http://www.libelium.com
Aria Systems Connected Car Offering • Connected cars are linked to the cloud by way of wireless technologies, smart chips, onboard computers and mobile apps • In the next five years, the number of connected cars may exceed a quarter of a billion worldwide • enhanced navigation, real-time traffic and parking information, streaming infotainment and integration between dashboards, smartphones and wearable devices such as health trackers and smart watches • New sources for monetization for carmakers, service providers and many other travel- related industries • Data plans and subscription services • Revenues from connected car services are expected to top $40 billion (U.S.) in the next five years https://www.ariasystems.com
IOT Applications • Predictive Maintenance • Network Performance Management • Loss Prevention • Capacity Utilization • Asset Utilization • Capacity Planning • Inventory Tracking • Demand Forecasting • Supply Chain Optimization • Pricing Optimization • Disaster Planning and • Yield Management Recovery • Load Balance Optimization • Downtime Minimization • Smart City • Energy Usage Optimization • Health Monitoring • Device Performance • Agriculture Effectiveness
An Example Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .
Predictive Maintenance Basic Idea • Equipment failures can be very costly, both unscheduled and catastrophic at the extreme • Early and overscheduling of routine maintenance likely can be costly by tending to be conservative • Routine maintenance also will tend to adapt slowly to changing conditions, leading to more costs. • By leveraging low cost sensors, high speed networks, wireless technology, big data and analytics we can study failure scenarios • Based on historical analysis of known failure conditions, we can learn to estimate when a part or group of parts is likely to fail and head off issues in a timely and cost effective manner.
The Test and Training Data • Available at https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic- data-repository/ • Donated by various universities, agencies, or companies. • The data is real world and there are many published analyses and evaluation of approaches. • It provides a template for collecting and using data in the real world. • Success in these exercises requires appropriate digitizing of relevant machinery and devices in order to establish predictors for failures. • Many iterations may be required • The opportunity exists to move from scheduled maintenance to condition based maintenance to predictive maintenance.
Real-Time Stream of the Training Data
Cost Model
https://svds.com/predictive-maintenance-iot/Example Academic Research In this Area • Damage Propagation Modeling for Aircraft • Investigating Computational Geometry for Failure Prognostics in Presence of Imprecise Health Indicator: Results and Comparisons on C-MAPSS Datasets • Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset
Develop a Model with Jupyter and Python
https://github.com/duyetdev/iot-predictive-maintenanceDeploying the Model on a Stream
• Create UDF and add to KSQL and monitor or alert on the stream
MicroStrategy for visualization, alerts,
and historical analysis.The Real World is Much more Difficult
• There are many parts that can fail on an
aircraft and on any machinery
• There are many failure scenarios that
you will need to account for
• Grouping service together to save on
downtime
• Often Landing Gear problems are
discovered after pushing away from the
gate
• A delay can cost 25K to 40K and greatly
impact customer satisfaction
From “Architecting the Industrial Internet”, By: Shyam Nath; Robert Stackowiak; Carla Romano, Pakt Publishing,Limits of Wired Sensors • Position (extension or retraction) • Wheel speed • Weight on wheel • Skid (and antiskid)
Wireless Allows More Sensors • Failing to retract/extend • Failing to get up-locked after retraction / down-locked after extension • Exceeding retraction/extension time limits • Failing to give indications in cockpit of down-locking, transit, and up-locking • Loss in nitrogen pressure and oil in oleos due to leak • Loss in pressure in tires due to leak • Binding of wheel bearings and brakes • Fully worn out friction pads • Brake unit-related issues, such as overheating of brake unit • Leakage of brake fluid and sponginess in brake pedals • Failure of antiskid • Leakage of nitrogen in emergency extension cylinder • Low brake pressure in emergency accumulator • Low line pressure in emergency system • Low brake line pressure • Low battery voltage in emergency system
Sensor Locations and Use
• Early detection of wear or
malfunctions
• Brake pads and hydraulic oil
pressure
• Digital twin of the landing gear
is updated with new data and
analytics applied
• Diagnose current issues
• Calculate Remaining Useful
Life
From “Architecting the Industrial Internet”, By: Shyam Nath; Robert Stackowiak; Carla Romano, Pakt Publishing,Summary Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved .
Summary • High volume/velocity data from sensors, machines, smartphones and social media is continuously captured and stored. • Stored “Big Data” is accessible for traditional Historical Analysis • In addition, models are built with advanced analytics to surface actionable insights in a form that can be run on live data. • Models execute in the data streams to trigger actions when opportunities or threats are predicted. • This process is forever repeated so existing models are refined and new ones discovered.
You can also read
