The document discusses the concept of 'big fast data,' which refers to managing large volumes of diverse data rapidly and accurately to facilitate business decision-making. It outlines the importance of utilizing advanced data management strategies, emerging technologies, and predictive analytics to harness insights from data for competitive advantage. Various use cases demonstrate the application of big fast data in industries such as finance, healthcare, and retail to improve operational outcomes and customer engagement.

1. Analyze This! Best
Practices For Big And
Fast Data
Judith Hurwitz, President
Hurwitz & Associates
Bill Schmarzo, CTO
EIMA Practice, EMC Consulting
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2. What is Big Fast Data?
The Transition in Data
Management
Judith Hurwitz

3. What Is Big Fast Data?
Big Fast Data is the ability to
manage a huge volume of
disparate data at the right velocity
within the right timeframe
Characteristics of Big Fast Data
• Must be verified based on
accuracy and business
context
• Must incorporate variety of
data types including
structured unstructured data
3

4. Why Is Big Fast Data Important?
• Businesses need to gain
insights from massive
amounts of stored data
• Businesses need to be able
to make decisions faster to
impact outcomes
• Need to find answers
without asking the question
4

5. What Is The Business Looking For?
1. Ability to gain access to
vast amounts of available
data from multiple sources
2. Ability to identify anomalies
3. Ability to predict the future
4. Ability to react in real time
based on analysis
5

6. How Did We Get Here?
• Early online commerce sites and search
engines began pushing boundaries of data
management
• Successful companies found ways to
monetize huge volumes of customer data
to upsell
• The massive data had to be managed
efficiently and in the right context
6

7. Waves Of Data In Context With Usage Patterns
Wave Examples Characteristics
Relational Database System of Record Used for structured, transactional data, strict definitional controls.
Content Management Claims Document Management Used with unstructured/semi-structured text, derived value,
System System, Web content management context driven.
Data Warehouse Customer and account data Used for structured data. Subject oriented system optimized for
warehouse querying. Integrated, well-defined parameters, optimized for
storage, focused on timely access to corporate data.
Complex Event Monitoring sensor data in real time Large streams of data focused on managing and analyzing
Processing/Streaming data to determine process changes business processes.
In-Memory Databases Used in ecommerce engines to Uses main memory to cache data to improve speed. Fast
reduce latency and speed analytical processing that can transform decision making in real-
transaction processing. time or near real-time.
Hadoop Software Used to process massive amounts A non-relational software framework based on Google’s
Framework of highly distributed disparate data. MapReduce Framework. It includes a distributed file system
Examples include fraud processing, based software framework. Allows very large data files (both
image processing structured and unstructured data) to be distributed across all
nodes of a very large grid of servers.
NoSQL Databases Designed to process massive Supports various database models including graph, object, key
amounts of data in a flexible form. value, and document. Document oriented rather than relying on
Used in ecommerce to process joins, scale out model for scalability.
massive amounts of data flexibly.
7

8. How Infrastructure Supports The Reality Of Big Fast Data
• Availability of commodity
servers
• Horizontal scaling because
of virtualization
• Emergence of Cloud
Computing
• Advanced data
management including
predictive analytics and
big data analysis
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9. Making Big Data Fast Data A Reality
• Create a well defined business and IT strategy
• Focus on the business problem such as identifying
buying opportunities at point of engagement or reducing
fraud through an early warning system
• Understand the characteristics of your own data that you
need to leverage for the future
• Identify your bottlenecks in your current data architecture
• Create a strategy so you can use massive data at the
right speed and the right context to anticipate new
opportunities 9

10. The Elements Of A Data Architecture
• Foundational Data Services- support for relational, in-memory
databases, structured and unstructured data
• Middleware Services – allow for communication and integration
between data sources
• Big Data Analytics – ability to analyze huge volumes of data
• Data Warehousing Capabilities – used to apply analytics to huge
volumes of complex data
• Management Services – deliver the right performance levels
• Virtualized Infrastructure – ability to optimize the environment
• Runtime Services – support for mobile computing and other user
environments
10

11. The Business Initiative For Big Fast Data
• Capture, transform, and
manage huge volumes of
information in near real time
• Capture data at the point of
creation and then combine data
sources to create context to
deliver on the business
objective
• Leverage data assets to gain a
competitive advantage
11

12. The Business Potential
Of
Big Fast Data
Bill Schmarzo
CTO, EIM&A Practice
EMC Consulting
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13. Big Fast Data Requires An Architecture For High-
velocity Data To Accelerate Operational Execution
Mobile-Enabled Application
Web Clients Performance
Manager
Key Architecture Capabilities
 Scale out compute and storage
Cloud Application Platform  Distribution: real-time WAN
App Director Installer
Application Logic
 Data Diversity: SQL and NoSQL
In-memory Database  Mobile enabled
Fast Ingest
vFabric Data Director Greenplum  In-memory computing
Postgres Oracle Greenplum Hadoop  In-database analytics
Cloud Platform  Cloud friendly architecture
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14. Big Fast Data Use Cases
Algorithmic Stock Trading Identify risk and pricing nuances in stock trading
Real-time
Ad Serving Serve right ad to right person at the right time
Cyber Security Flag potential security breach behaviors and situations
Fraud Detection Identify potential fraud situations at purchase time
High-end Product Failure Predict high-end product failures (planes, trains, power plants)
Next Best Offers Recommend products based on current shopping occurrence
Churn Detection Flag customer behaviors that are indicative of attrition
Medical Treatment Recommend appropriate medical treatments in urgent situations
Right-time
Money Laundering Flag suspicious financial transactions
Claims Adjudication Approve insurance claims at time of filing
Loan/Insurance Approval Calculate financial scores and risks to approve loan or policy
Oil & Gas Exploration Track sensor feeds to identify potential drilling problems
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15. Use Case: Financial Trading And Real-time Operational
Analytics
 Develop risk and pricing
algorithms against
historical data in
Greenplum Database using
analytical methods such as
linear regression,
clustering, etc.
 Serve up analytic results
and scores to SQLFire for
real-time execution
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16. Use Case: Retail Location-based Marketing And Next
Best Offers
 Develop analytic models
on detailed customer
loyalty and Point of Sale
(POS)data to create
“next best offer” scores
for each customer
 Leverage “right-time”
feeds based upon
customer geo location to
deliver most appropriate
offers
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17. Use Case: Healthcare And Readmission Score At Initial
Admission
Out of 1000 patients,
1124 admissions
• Score patient at
expected within next 12 point of admission
months for the probability of
readmission based
upon patient history
and current health
factors
• Create custom
• Admissions increase with the
level of cholesterol treatment and
• Admissions decrease with the monitoring programs
Max Heart Rate
• Cholesterol and Max Heart for high-risk patients
Rate uncorrelated
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18. Greenplum And EMC Consulting Provide Big Fast Data
Strategy And Implementation Services
Identify big data
Vision
analytics business
Workshop
use cases
Analytics Deploy analytics sandbox
to quantify the business
Lab
case
Identify current state, determine required
Analytics
state and conduct gap analysis to develop
Operationalization
analytics implementation roadmap
Repeat the process for
identified business cases
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19. Questions and Answers
To type a question via WebEx, click on the Q&A tab
Please select “Ask: All Panelists”
to ensure your questions reach us. Thank you!
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20. Learn More…
 See us at…
– Oct. 16-17 O’Reilly Strata Rx Conference, Santa Clara, CA
▪ Oct. 16 9:40 am It’s an Exciting Time in the Industry
▪ Oct. 16 3:35 pm Big Fast Data in Health Sciences: A Panel of Experts Discusses What and Why
▪ Oct. 17 2:05 pm A Predictive Approach to Real-Time Detection of Fraud, Waste, and Abuse in Healthcare
– Oct. 23-25 O’Reilly Strata New York Conference
▪ Oct. 23 11:15 am Great Debate: The Old Models are Broken
– On-demand webinar: Transform Your BI and Data Warehouse for Big Data
– Upcoming webinar Sept. 18, 11am PT/2pm ET Using Greenplum to Deliver Big Data Analytics
 Contact Judith Hurwitz
– Email: judith.hurwitz@hurwitz.com
– LinkedIn: http://www.linkedin.com/pub/judith-hurwitz/0/18/405
– Twitter: @jhurwitz
 Contact Bill Schmarzo
– Email: william.schmarzo@emc.com
– LinkedIn: http://www.linkedin.com/in/schmarzo
– Twitter: @schmarzo
– Blog: http://infocus.emc.com/author/william_schmarzo/
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21. THANK YOU
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