Hi Friends ,
There is an interesting post on how to leveraging Big data analytics in an Integrated GRC Environment in an Organize to have visibility in core enterprises issues on real time basis . This presentation is from Metric stream -an international and Global GRC soloutioning providers in association with Dr. Kirk. D. Borne - Big data consultant and Adviser .Hope you like it and enjoy as well.
4. Big Data and the fundamental
business conflict:
RISK versus REWARD
http://www.telegraph.co.uk/news/worldnews/europe/russia/10061780/Russian-convicts-beat-Americans-in-cyber-chess-battle.html
6. Big Data: What is it good for?
The 3 D2D’s
Knowledge Discovery
– Data-to-Discovery (knowledge insights)
Data-driven Decision Support
– Data-to-Decisions (decisioning insights)
Big ROI (Return On Innovation) !!!
– Data-to-Dividends (innovation insights)
– Data-to-Dollars (business ROI)
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7. 1) Correlation Discovery
Finding patterns, trends, and dependencies, which
might reveal new principles of behavior
2) Novelty Discovery
Finding new, rare, one-in-a-[million / billion / trillion]
objects and events
3) Class Discovery
Finding new classes of objects, events, and behaviors
Learning the rules that constrain class boundaries
4) Association Discovery
Finding unusual (improbable) co-occurring associations
Data Science in 4 easy steps
(achieving the 3 D2D’s from your Big Data)
8. 1) Correlation Discovery
Finding patterns, trends, and dependencies, which
might reveal new principles of behavior
2) Novelty Discovery
Finding new, rare, one-in-a-[million / billion / trillion]
objects and events
3) Class Discovery
Finding new classes of objects, events, and behaviors
Learning the rules that constrain class boundaries
4) Association Discovery
Finding unusual (improbable) co-occurring associations
Data Science in 4 easy steps
(achieving the 3 D2D’s from your Big Data)
10. Classic Textbook Example of Data Mining (Legend?):
Data mining of grocery store logs indicated that men who
buy diapers also tend to buy beer at the same time.
Business Example #1
11. Amazon.com mines its customers’ purchase logs to
recommend books to you: “People who bought this book
also bought this other one.”
Business Example #2
12. Netflix mines its video rental history database to
recommend rentals to you based upon other customers
who rented similar movies as you.
Business Example #3
13. Wal-Mart studied product sales in their Florida stores in
2004 when several hurricanes passed through Florida.
Wal-Mart found that, before the hurricanes arrived, people
purchased 7 times as many of {one particular product}
compared to everything else.
Business Example #4
14. Wal-Mart studied product sales in their Florida stores in
2004 when several hurricanes passed through Florida.
Wal-Mart found that, before the hurricanes arrived, people
purchased 7 times as many strawberry pop tarts
compared to everything else.
Business Example #4
16. Knowledge Discovery for multi-source Data:
Heterogeneous data collections are the new normal
New Knowledge on
correlations, causal
connections, and
interdependencies
between events,
objects, processes
within any
application domain
Data to Information to Knowledge
17. Knowledge Discovery for multi-source Data:
Heterogeneous data collections are the new normal
New Knowledge on
correlations, causal
connections, and
interdependencies
between events,
objects, processes
within any
application domain
The “first mile” challenge:
integrating multi-source data
The “first mile” challenge:
integrating multi-source data
18. Knowledge Discovery for multi-source Data:
Heterogeneous data collections are the new normal
New Knowledge on
correlations, causal
connections, and
interdependencies
between events,
objects, processes
within any
application domain
The “last mile” challenge:
deriving Actionable Intelligence
from all of your data sources.
19. The MIPS model
for Dynamic Data-Driven Application Systems (DDDAS)
• MIPS =
– Measurement – Inference – Prediction – Steering
• This applies to any Network of Sensors:
– Web user interactions & actions (web analytics data), Cyber network
usage logs, Social network sentiment, Machine logs (of any kind),
Manufacturing sensors, Health & Epidemic monitoring systems, Financial
transactions, National Security, Utilities and Energy, Remote Sensing,
Tsunami warnings, Weather/Climate events, Astronomical sky events, …
• Machine Learning enables the “IP” part of MIPS:
– Autonomous (or semi-autonomous) Classification
– Intelligent Data Understanding
– Rule-based
– Model-based
– Neural Networks
– Markov Models
– Bayes Inference Engines
Alert & Response systems:
• Actionable insights from
streaming business data
• Automation of any data-
driven operational system
http://dddas.org
20. The MIPS model
for Dynamic Data-Driven Application Systems (DDDAS)
• MIPS =
– Measurement – Inference – Prediction – Steering
• This applies to any Network of Sensors:
– Web user interactions & actions (web analytics data), Cyber network
usage logs, Social network sentiment, Machine logs (of any kind),
Manufacturing sensors, Health & Epidemic monitoring systems, Financial
transactions, National Security, Utilities and Energy, Remote Sensing,
Tsunami warnings, Weather/Climate events, Astronomical sky events, …
• Machine Learning enables the “IP” part of MIPS:
– Autonomous (or semi-autonomous) Classification
– Intelligent Data Understanding
– Rule-based
– Model-based
– Neural Networks
– Markov Models
– Bayes Inference Engines
http://dddas.org
Alert & Response systems:
• Actionable insights from
streaming business data
• Automation of any data-
driven operational system
21. From Sensors to Sentinels to Sense:
Take Data to Information to Knowledge
to Insights (and Action!)
From Sensors (Measurement & Data Collection)…
… to Sentinels (Monitoring & Alerts) …
… to Sense-making (Data Science) …
… to Cents-making (Business ROI)
… Actionizing and Productizing Big Data
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22. Smart Engines for Data-Driven
Discovery and Decision Support
• New knowledge and insights are acquired by mining
actionable data from all digital inputs (Sensors!)
• Decisions are based on the new knowledge mined,
prior experience, and your “business” decisioning rules
embedded within the pipeline (Sentinels!)
• “Smart Sensors” act autonomously in real-time, without
human intervention = actionable intelligence (Sense!)
http://legacy.samsi.info/200506/astro/presentations/tut1loredo-7.pdf 22
23. Decision Analytics – based on massive amounts of information
(Big Data – What is it good for? …Decision Support and Innovation!)
From Devices……
… Intentions…
… Location, weather, and
other geographic attributes…
… Demographics…
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24. Automating
Analytics
as-as-Service
(AaaS)
• Based on SYNTASA’s Marketing Analytics-as-a-ServiceTM
(MAaaS)
• “Smart Sentinel in a box”
– Your business rules determine the goals, decision points, alerts, and responses.
– Moving beyond historical hindsight and oversight (Descriptive & Diagnostic
Analytics) to new world of insight and foresight (Predictive & Prescriptive AaaS),
eventually achieving right sight (Cognitive Analytics = the 360 view, enabling the
right action, for the right web user, at the right place, at the right time).
• Mining multi-portal big data streams (across the organization’s departments)
• Personalization and Customization (“segment of one”)
• Decision Automation in a rich content (Big Data) environment
24Based on Marketing Analytics-as-a-ServiceTM
(MAaaS) from http://www.syntasa.com/
Digital user
Behavior
Modeling
25. The New Digital Business:
Big Data Analytics Challenge = Risk Mitigation
• General example of streaming data analytics:
Real-Time Event Mining for Actionable Intelligence:
Identifying, characterizing, & responding to millions of events in real-time streaming data
Deciding which events (out of millions) need investigation and/or response
• Web Analytics example:
Web Behavior Modeling and Automated System Response (from
online interactions & web browse patterns, personalization, user
segmentation, 1-to-1 marketing, advanced analytics discovery,…)
• Many other examples:
Health alerts (from EHRs and national health systems)
Tsunami alerts (from geo sensors everywhere)
Cybersecurity alerts (from network logs)
Social event alerts or early warnings (from social media)
Preventive Fraud alerts (from financial applications)
Predictive Maintenance alerts (from machine / engine sensors)
RiskMitigation
26. The New Digital Business:
Big Data Analytics Rewards = Innovation & Value
• Learning from Data (Data Science)…
– Clustering (= New Class discovery, Segmentation)
– Correlation & Association discovery
– Classification, Diagnosis, Prediction
– Outlier / Anomaly / Novelty / Surprise detection
• … to conquer the 3 D2D challenges:
– Data-to-Discoveries
– Data-to-Decisions
– Data-to-Dividends
(big ROI = Return on Innovation)
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Rewards!