Don't just think about IoT as the way we implement connected smart devices. The lessons and technologies that have lead to IoT are an excellent tool for rethinking how solve some of the same business challenges we've had for decades. What can the lessons from IoT teach us about how to approach data warehousing and analytics, data integration, and business applications?

1. Why You Should Be Using IoT
Technologies for More Than Just IoT
October 2017 – Paul Boal, VP of Delivery – @paulboal

2. What is the Internet of Things?
The Internet of things (IoT) is the inter-networking 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.
https://en.wikipedia.org/wiki/Internet_of_things
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3. 3

4. Sensors
A+ Magazine
March 1986
4

5. 5

6. Wearable Devices
inCider (The Apple II Magazine)
October 1986
6

7. Networks Artificial Intelligence
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8. 8
1986
1996
2006
2016
IoTApplications

9. Technologies Making IoT Possible?
• Physical Improvements in Electronics
• Miniaturization of sensors
• Low power networking (BLE, Zigbee, NFC)
• Information Processing Improvements
• Big data and NoSQL databases
• Stream processing and analytics
• Distributed processing and cloud computing
• Microservices architecture
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10. 10
1986
1996
2006
2016
Data warehousing
EDI/B2B
Business Applications
IoTApplicationsBusinessSolutions

11. Presentation / GUI
Tier
Application Logic
Tier
Data
Tier
Classic Architectures Worth Reviewing
• Business Applications
• Batch EDI
• Data Warehousing
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12. What Architecture Should I Use?
Round 1
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13. Presentation / GUI
Tier
Application Logic
Tier
Data
Tier
Business Applications
• Round 1: You have a business application that allows business users to
manage customer transactions as they go through their engagement and
purchasing experience. Examples:
• Web storefront
• Point of sale system
• Electronic health system
• Utility billing system
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MySQL,
SQL Server,
Oracle
Java, .NET,
Python
HTML, Swift,
JavaScript

14. Business Applications – N-Tier or Microservices
Architectural Advantages
• Clear segregation of duties
• Centralized storage of data
• Reusability of application logic
• Create customized interfaces
Enhanced with IoT
• Pushing logic to the edges allows them
to respond to unexpected conditions.
• Streaming data allows the database to
become an event communication layer as
well as a storage layer.
• Using a non-relational database
increases the flexibility in future
enhancements.
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15. The NorthWind Database
transponder
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16. Examples of IoT-Tech in Business Applications
Document-store
database allowing
flexible schema
evolution.
Streaming allows
all applications to
be notified when
changes occur.
Think of business
users as edge
nodes in the
system
Users and the system behave
asynchronously, notifying each
other when they make decisions or
have information to share rather
than following a fixed workflow.
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17. 17
”You’re a participant, not a user”
Donald Farmer @DonaldTreeHive

18. What Architecture Should I Use?
Round 2
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19. Information Exchange
• Round 2: You have a business partner with whom you need to be
exchanging information about products, services, customer verification,
inventory levels, service availability, and sales transactions:
• Health Insurance Member Eligibility
• Billing Transactions
• Healthcare Orders and Prescriptions
• Funds Transfer
• Order Fulfillment
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20. Batch EDI / Integration
Architectural Advantages
• Message encapsulation and
standardization
• Simple text-based data exchange
• Auditability and confirmation of
transactions
Enhanced with IoT
• Generation of EDI messages during
business processes allows for real-time
quality assurance and feedback to
operations.
• Document store databases alleviate the
impedance mismatch between RDBS and
messaging.
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21. Examples of IoT-Tech in Integration
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Systems of record
publish all updates to
streams.
Integrated outputs can
be produced at
multiple intervals.
Extracts leverage
a collection of
shared and some
independent
transformations.
Flume

22. What Architecture Should I Use?
Round 3
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23. Data Warehousing and Business Intelligence
• Round 3: You have an analysis and reporting system that takes
information from several source systems and external data, merges and
summarizes that information, identifies key metrics, and makes that
information available to users and other downstream processes.
Examples:
• Operational Data Store
• Data Warehouse
• Data Extracts / Data Integration
• Business Intelligence
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Teradata, Oracle,
SQL Server
Informatica,
IBM, IBI
Business Objects,
Microstrategy,
Tableau

24. Data Warehousing and Analytics
Architectural Advantages
• Data quality controls
• Metadata management
• Data standardization
• Value through data integration
• Business view of information
• Self-service data access and reporting
tools
Enhanced with IoT
• Switch to streaming data integration to
minimize outages and hours of batch
processing.
• Use streaming data quality checks to
send near real-time feedback to business
users and improve data quality same-day.
• Use messaging to feed detail tables
and aggregations simultaneously rather
than serially.
• Use graph database to understand
complex business models like
networked relationships.
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25. Examples of IoT-Tech in DW/BI
Business
application
streams data
to Kafka
Data warehouse modeled
as a knowledge graph to
capture complex relationships
between transactions
Streaming data quality checks
give real-time feedback
to improve business processes
Graph analysis
leads to easier
root cause
analysis
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26. 26
”The natural order of the world is a
graph not a spreadsheet.”
Kirk Borne @KirkDBorne

27. Many Sources of the Truth?
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The best way to build a data
warehouse was to create a
single database to control a
single version of the truth.
Ubiquitous distributed
processing, flexible data
stores, and standard
communication protocols
could allow a collection of
analytics to be reliably
shared without having to put
them all in a monolithic,
specially built database.

28. Fight the Myth that New = Hard
Saying “this solution doesn’t need that
new technology” promotes the myth that
“new technology” is necessarily harder
and more expensive.
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29. Top Myths
• The transaction overhead for real-
time / streaming is too high.
• NoSQL and Big Data is only for
unstructured data.
• Businesses want well-defined
workflows they can control
• There isn’t enough expertise
around to build this way.
• Distributed processing and
databases make this irrelevant.
• NoSQL is straightforward to work
with in un- and structured forms.
• Managers want well-defined
workflows. User do not.
• Open Source and cloud trends
make this easy to learn and
growing rapidly.
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30. Thank You!
Paul Boal
@paulboal
• Healthcare data and analytics solutions
• Big data, IoT, and advanced analytics
• Data strategy and data governance
• Drive change through data insights
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 VP Delivery
http://amitechsolutions.com
@AmitechSolution

31. Questions?
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