Data Analytics in your IoT SolutionFukiat Julnual, Technical Evangelist, Microsoft (Thailand) Limited ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND

1. Data Analytics in your IoT Solution
Fukiat Julnual, Technical Evangelist, Microsoft (Thailand) Limited
The First NIDA Business Analytics and Data Sciences Contest/Conference
วันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์
https://businessanalyticsnida.wordpress.com
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How we can feed our data stores from IoT Data for Data Analytic?
นวมินทราธิราช 3003
1 กันยายน 2559
14.45-15.45 น.

3. Connected data
CLOUD
MOBILE

4. IntelligenceCloudData

5. Decision

6. Systems of Intelligence
Transform your
products
Engage your
customers
Transforming key aspects of business
Optimize your
operations
Empower your
employees

7. IoT is key to achieving digital transformation
Source: Redefining the Connected Conversation, IoT Trends, Challenges & Experience Survey. James Brehm & Associates, 2016.
60%
Of those working on IoT are aiming to
grow revenue and profits
73% Of the companies surveyed are currently
active in IoT
50%
Reduction in downtime with predictive
maintenance
According to a recent IoT survey…

8. Innovation at work – real world IoT use cases
Electric
charging
stations
Street
sweepers
Postboxes
Aircrafts
Auto
Elevators
Factory floor
Oil equipment
Cows
Engines
Vending
machines
Buildings
Fryers
Medical devices
Vaccine
dispensers
Trucks
BusesDogs
Oil distribution
Smart meters
Internet
of Things
Power plant
Surveillance
Power tools
Racing
Mining
equipment
Smart grids

9. From endpoint to insight to action
From endpoint to insight to action, across the enterprise, and around the world
Built on the industry’s leading cloud
Secure
End-to-end
From endpoint and connection
through to data and the cloud
Open
Connect anything
Any device, OS, data source,
software, or service
Fast
Start in minutes
Preconfigured solutions for the
most common IoT scenarios
Magic Quadrant Leader, Business Intelligence and Analytics Platforms*
Scalable
Grow effortlessly
Millions of devices, terabytes of
data, on-premises and in the
cloud, in 30 regions worldwide
PeopleData Insights ActionGatewaysDevices

10. Rich data storage and analytics ecosystem
Gartner Magic Quadrant for
Operational Database Management Systems
Data Analytics
Machine Learning
Stream Analytics
HDInsight
Data Factory
Data Lake & Analytics
Data Platform
SQL Database
Redis Cache
DocumentDB
SQL Data Warehouse
Search
Tables
*February 2015. The Gartner Magic Quadrant for Business Intelligence and Analytics Platforms is the property of Gartner, Inc. and available upon request from Microsoft. Gartner does not endorse any vendor, product or service depicted in its research
publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of
fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. The above graphics were published by Gartner, Inc. as part of a larger research document
and should be evaluated in the context of the entire document.

11. EXAMPLE SOLUTIONS

12. Preconfigured Solutions:
Remote Monitoring Remote
Monitoring
Predictive
Maintenance
More to come…

13. What you get with remote monitoring preconfigured solution
Devices Azure IoT Suite Remote Monitoring
Back end
systems and
processes
Event Hub
Storage blobs DocumentDB
Web/
Mobile App
Stream
Analytics
Logic Apps
Azure
Active Directory
IoT Hub Web Jobs
C# simulator
Cell-based
connectivity

14. Preconfigured Solutions:
Predictive Maintenance
More to come…
Predictive
Maintenance
Remote
Monitoring

15. What you get with predictive maintenance solution?
Devices Azure IoT Suite Remote Monitoring
Back end
systems and
processes
Event Hub
Storage blobs DocumentDB
Web/
Mobile App
Stream
Analytics
Logic Apps
Azure
Active Directory
IoT Hub Web Jobs
C# simulator
Azure ML
Cell-based
connectivity

16. Predictive Maintenance
1 Identify the
target
outcome
2 Inventory
data
sources
3 Capture &
combine
data
4 Model, test
and
integrate
5 Validate
model in a
live
operational
scenario
6 Integrate
into
operations
Imagine if you could automatically identify and fix potential problems
before they happen
Azure IoT Suite solutions come with pre-built sample scenarios that include:
• Background information on the business need and objectives
• Simulated devices and sample data
• Pre-set rules and alerts, pre-defined dashboards, and more

17. Predictive Maintenance framework
1
Identify the target outcome
I want to understand how much time each AC
unit has left before it needs maintenance so
we can prevent unplanned failures
Last time an AC unit failed, it cost
thousands of dollars and operations
were down for days
AC unit
out of order

18. Predictive Maintenance framework
2
Inventory data sources
100101011000
101000101101
010011001110
101000110011
Performance
data
Maintenance logs
Weather data Failure logs
Sensor data
Include data from a variety of
sources – you may be surprised
about the places where key
information can come from

19. Predictive Maintenance framework
3
Capture and combine data
Lay the groundwork for
a robust predictive model by
pulling in data that includes
both expected behavior and
failure logs

20. Predictive Maintenance framework
4
Model, test and iterate
Identify unexpected patterns by developing statistical models using advanced
analytics techniques. Stank-rank models to determine which model is best at
forecasting the timing of AC unit failures.
Make your model
actionable by understanding
how much advance notice the
maintenance team needs in
order to respond
 Model A
 Model B
 Model C

21. Predictive Maintenance framework
5
Validate model using your latest data
Be willing to refine your
approach based on the data
you gather during the real-
world pilot
100101011000
101000101101
010011001110
101000110011

22. Predictive Maintenance framework
6
Integrate into operations
Strengthen your
processes and procedures to
take advantage of what you
learn
3 weeks until failure: Order
replacement part
2 weeks until failure: Send
repair team

23. System Integrators Solution Providers (ISVs)Devices & Connectivity
A partner eco-system

24. Retail & Consumer Products
Healthcare
Financial Services & Insurance
Government
Manufacturing
€

25. Data
Science Team
Data
Engineering
Data
Science
Application
Development
Business
Acumen
Data
Management
Data
Dividend

26. • Academic Rigor
• Talent Competition
• Integration Complexity
• Tool, Skill & Culture Gaps
• Data Volume, Diversity
• Security & Governance Constraints
• Rapid Platform Evolution
• Low Experimentation Rate
• Complex Operationalization
Talent
Scarcity
Low
Productivity
Complex
Infrastructure
Slow
Innovation
• Legacy Products
• Irregular WorkloadHigh Cost

27. People
+
Data
Sources
Apps
Sensors
and
devices
On Prem or in the Cloud
INTELLIGENCEDATA ACTION
Automated
SystemsMicrosoft R Server & SQL R Services
Apps
Cortana Intelligence

28. Transform data into intelligent action
INTELLIGENCE
Intelligence
Dashboards &
Visualizations
Information
Management
Big Data Stores Machine Learning
and Analytics
Event Hubs
HDInsight
(Hadoop and
Spark)
Stream
Analytics
SQL Data
WarehouseData Catalog
Data Lake
Analytics
Data Factory
Machine
Learning
Data Lake Store
Power BI
Cortana
Web
Mobile
Bots
Bot
Framework
Cognitive
Services

29. People
+
Data
Sources
Apps
Sensors
and
devices
From Data To Action On Premises
INTELLIGENCEDATA ACTION
Automated
SystemsMicrosoft R Server & SQL R Services
Apps
Cortana Intelligence

30. SQL Server
R Services
Red Hat SUSE
Hadoop Teradata
Windows
CommercialCommunity
R ServerR Open

31. Write Once – Deploy Anywhere
R Server portfolio
Cloud
RDBMS
Desktops & Servers
Hadoop & Spark
EDW
R Server Technology

32. People
Data
Sources
Apps
Sensors
and
devices
INTELLIGENCEDATA ACTION
Automated
SystemsMicrosoft R Server & SQL R Services
Apps
Cortana Intelligence

33. Convergence with Flexibility
Scalable Algorithms
R: Write Once Deploy Anywhere
Templates & Samples
Microsoft R Server Family
R & Python to ML Interop.
Cortana Intelligence

34. • Embrace Open Source
• Evolutionary Path to Cloud
• Democratize Data Science
• Skill Re-Use
• Transparent Scaling
• Facilitate Collaboration
• Decouple Data Science from Platforms
• Leverage Hybrid Cloud Architecture
• Accelerate Experimentation
• Streamline Deployment
Broaden The
Talent Pool
Increase
Productivity
Modernize
Infrastructure
Maximize
Innovation
Drive Down
TCO

35. Systems of Intelligence
Transform your
products
Engage your
customers
Transforming key aspects of business
Optimize your
operations
Empower your
employees