New technologies are enabling organizations to build applications that were not possible before. A major US city is using MongoDB to cut crime and improve municipal services by collecting and analyzing geospatial data in real-time from over 30 different departments. A global telco built a next-generation machine-to-machine (M2M) platform that can support tens of billions of sensor readings for a single customer. Considerations for Decision Makers: In this webinar, you will learn how different types of Big Data applications – specifically, Online vs. Offline Big Data – shape the technology selection process for these projects.

1. Thriving With Big Data
Part 1
Build Apps You Couldn’t Build Before

2. 2
• 5 Parts, ~15 min each
– Build Apps You Couldn’t Build Before (July 16th)
– Adapt in a Competitive Market (July 23rd)
– Make Customers Happy (July 30th)
– Save Money (August 6th)
– Considerations for Decision Makers (August 13th)
• Fun, lively discussion + QA Session
• Customer examples + considerations
About This Series

3. 3
• Considerations for Adapting with Big Data
• Customer Stories
• Takeaways
Part 1: Build Apps You Couldn’t Build
Before

4. 4
big data big 'dāt-ə noun
referring to technologies and initiatives that involve
data that is too diverse, fast-changing or massive
for conventional technologies, skills and
infrastructure to address efficiently.
Big Data Defined

5. 5
Consideration – Online vs. Offline
• Created, ingested, transformed,
managed, analyzed in real-time
• Low-latency
• High availability
• Ingested, managed, analyzed
as long-running processes
• Jobs run for hours or more
• Availability is lower priority
Operational Batch analytics

6. 6
• WindyGrid
• Crime prediction and
prevention application
• Analyzes data from 30+
departments
• Correlation of real-time
and historic data to
identify potential
problems
Example: City of Chicago

7. 7
• Machine-2-Machine (M2M), AKA Internet of Things
• Real-time processing of billions of readings
• Machines talk to other machines to make intelligent decisions
without human intervention
Example: Telefonica
Fleet
Management
Smart Grid Vehicle
Telematics
Health Care Digital
Displays
Retail Kiosk
Vending
Machines
Transaction
Processing
eReaders Logistics
Devices
Sensor
Networks
$$
$

8. 8
• Fleet Monitoring and
Optimization
• Sensor data, weather,
best practices
• Improves fleet
performance, yields,
uptime, safety
• Informs product design,
warranty and failure
analysis
Example: Industrial Equipment
Manufacturer

9. 9
• Online vs. Offline
– Which are you optimizing for
– These are complimentary
• Build Apps You Couldn’t Build Before
– Many sources: build an operational picture in real-time
– Fast-changing data: high volume, high diversity
– New types of data: sensor, geospatial, social media
Takeaways

10. 10
• Tuesday, July 23rd 10:00AM PDT / 1:00PM EDT / 5:00PM
UTC
• Considerations for Adapting with Big Data
– Agility: The curvy line to success
– Community: Benefit from the experience of others
• Customer Stories
– ADP responds to customers with a personal mobile experience
– Major ISV expands with cloud and collaboration for desktop
– Leading telco goes head-to-head with over-the-top media players
Part 2: Adapt In A Competitive Market