Organizations need to get high value from streaming data to gain new clients and capitalize on market opportunities. Discover how IBM Streams is best suited for use cases that has the need for high speed and low latency.

1. Top industry use cases
for streaming analytics

2. The state of data is changing. Can you
quickly spot the opportunity in your data?
Market Trend Business Impact
1. Movement to include streaming analytics
Faster decisions required to keep pace with
competition, 66% increase in streaming
analytics
2. Organizations can’t keep up with fast data
The value of data decreases over time, 2
weeks to analyze social data on average
3. Missed opportunities/risks despite
analytics
Business analysts waste up to 95% of their
time investigating false positives
4. More data (sensors, social, mobile) but the
ability to make sense of it is declining
Organizations can make sense of less than
2% of their data
5. The rise of machine data
Organizations unable to analyze machine
data, 40% of machines connected by 2020

3. Use cases for streaming analytics
Typical Industry Applications
requiring ultra low latency:
• Responsive advertising
• Continuous model deployments/
updates
• Cybersecurity
• Process control
• Finance trading
• Law Enforcement and Public
Safety
Typical Industry Applications
requiring high volume ingest:
• Tier 1 telco CDR processing
• ICU monitoring
• Smart grid PMU monitoring
• Tier 1 automotive monitoring
Deep: Historic Insight, Context, Model Building
Fast: Detection,
Correlation,
Aggregation, Scoring

4. Continuous and speedy analysis
in context for government
Government
Protect against
threats and
reduce fraud
City of Davao better anticipates impending problems and increases situational awareness
about city events
Smarter surveillance: Analyze data from manned and
unmanned vehicles and cameras to alert law enforcement of
potential issues.
Identification of fraud and terrorist activity: Understand
identities instantaneously to alert officials of persons of interest.
Cyber attack discovery, prediction and prevention: Analyze
events continuously across multiple layers of network traffic to
find malware and track behavior.
Street crime awareness: Mine data on geospatial parameters
to monitor street gangs and proactively prevent crimes.

5. Continuous and speedy analysis
in the context of healthcare
Healthcare
Anticipate disease
onset and deliver
patient data to
make life saving
decisions
Emory analyzes 100,000 IoT data points per second
Link: https://www.youtube.com/watch?v=DgQheTHM5II
Identification of life-threatening conditions: Fuse different
data sources. Analyze physiological streams and electronic
health record to spot life-threatening conditions.
Highly personalized care: Detect signs earlier to improve
patient outcomes and reduce length of stays. Automated or
clinician-driven knowledge discovery to identify new
relationships between data stream events and medical
conditions.
Proactive treatment: Build a profile for each patient based on
personalized data streams and receive insights to continuously
improve care.

6. Continuous and speedy analysis
in context for finance
Finance
Lower risk, cost
and fraud while
enabling faster
more informed
transactions and
greater revenue
Financial institution picks IBM over Storm due to better performance; with latency as low as
100 microseconds
Faster trades: Automate trades in milliseconds to increase
revenue.
Industry knowledge: Connect to widely used market data
sources and industry systems such as FIX and QuantLib to
lower IT costs.
Analytic accelerators: Calculate equity option derivative
values to increase revenue.
Continuous support: Ingest and manage data to support
equities, derivates, commodity and forex trading. Incorporate
additional contextual awareness (news, weather etc) into trading
decisions.
Manage risk: Continuously monitor.

7. Continuous and speedy analysis
in context for automotive
Automotive
Optimize
operations,
improve the
driving experience,
and create safer
roadways
Peugeot integrates data from cars, logs and social media
Link: http://www-03.ibm.com/press/uk/en/pressrelease/43511.wss
More profitable aftermarket for services and products:
Create targeted offers based on driving preferences such as
sound systems and entertainment to increase revenue.
More interactive and safer driving experiences: Alert
approaching drivers of slick conditions that caused previous
drivers to use anti-lock brakes.
Integrated vehicle data: Share data across third parties such
as insurance companies and emergency medical services to
increase collaboration and lower costs.
Improved quality and functionality: Detect problems sooner,
predict breakdowns, and ensure parts are in stock to keep
clients satisfied.

8. Continuous and speedy analysis
in context for telecommunications
Telco
Increase customer
satisfaction,
maximize asset
utilization and
proactively retain
profitable
customers
Asian telco improves marketing effectiveness 600% while lowering development cost by 95%
Link: https://www.youtube.com/watch?feature=player_embeddedv=eg8KSLAZ2HM
Processing of call data: Process CDRs and filter SMS spam to
predict customer churn and fraud.
Timely marketing promotions: Trigger promotions: determine
the success of promotions within minutes and take necessary
corrective actions.
High utilization of expensive network assets: Understand
geospatial location of the callers to target them effectively.
Incremental revenue from newer marketing promotions: Run
powerful geospatial analytics to cross sell additional services.

9. Continuous and speedy analysis
in context for insurance
Insurance
Increase services
for clients and de-
crease cost and
fraud
Ability to model risk throughout the day vs. quarterly
Telematic analysis: Create dashboards of behaviors such as
car speed and location to automatically adjust risk scores.
Speedy fraud detection: Receive incident reports as they hap-
pen and immediately feed into claims processes to streamline
operations.
Cargo protection: Predict accidents or disasters and dynami-
cally update risk models to ensure informed underwriting.
Call center optimization: Automate next best actions and in-
crease automated responses to improve client experience, qual-
ity and performance.

10. Continuous and speedy analysis
in context for energy and utilities
Energy and
Utilities
Optimize energy
usage and reduce
outages
CenterPoint Energy powers 2.3M Smart Meters with IBM Streams
Link: https://www.youtube.com/watch?v=Oz77KOAfRZY
Outage detection and prediction: Monitor grid/plant elements
and networks and rapidly predict and analyze data to detect
grid/plant outages.
Load shedding: Monitor and run streaming analytics on data
from smart meters and sensors.
Condition based maintenance: Identify assets that are
likely to fail in the near term or require maintenance or
operational changes. Take action preemptively to control or
repair equipment.
Smarter Analytics: Run extremely powerful analytics from
smart meters, satellite imagery feeds and weather forecasts for
price fluctuation forecasting, energy trading insights and more.

11. To get started with stream computing and infuse data
streams into your applications, try the
IBM Quick Start Edition.
Also, join the IBM Streams community.