The term "big data" seems to be everywhere these days. With the ever growing number of attendees at big data and Hadoop events, it’s clear big data is here to stay. But what does that mean for the analytics market, and how does big data fit into the picture? This session, featuring Mark Davis, Sr. Product Architect at Dell, will explore what big data means in a practical sense to the IT department. It will also explore the many ways that big data affects an organization’s picture of performance. Plus, see how big data analytics, using technologies like Cassandra and Hadoop, will converge with traditional business intelligence to create a complete picture of the enterprise's information assets, thereby giving the business a complete and insightful view of its operational efficiency.

1. Big Data Analytics – Realize the Investment from Your Big Data Clusters
Mark Davis| Senior Architect and Principal Engineer, Dell Inc.

2. Big Data and Society
How Is Big Data Affecting Our World?

3. 200EB = 1018 B
1ZB = 1021 B
10EB
100TB
2000198519001750
Industrial
Revolution
#1
Industrial
Revolution
#2
Industrial
Revolution
#3
Industrial
Revolution
#4
R. J. Gordon: Is US economic growth over? Faltering innovation confronts
the six headwinds. CEPR Policy Insight No 63

4. Distributed
File System
MapReduce
Eventually
Consistent
Column Store
Analytics
Database
NoSQL
Structured
Semi-structured
Unstructured
Text Analytics
Machine
Learning
The Big Data “Zoo”

5. Big Data Use Cases
How Is Big Data Being Consumed Today?

6. SourcesKAS
GOAL: Improve force effectiveness
SOURCES: Situation reports and acquired multi-
source intelligence
ANALYSIS: Extract named entities and
relationships, classify and label, normalize
geospatial and temporal metadata; visually
understand relationships and trends
ACTION: Identify mission objectives and create
priorities
Defense Intelligence
Visualization
metadata
relationships
data
Visual
Understanding
entities

7. * Current system doesn’t scale
* Oracle with text plug-in
* Overwhelmed by intelligence needs
* Need analytic capability with search
US Army

8. SourcesKAS
GOAL: Be more competitive
SOURCES: Patents, PR announcements, legal
documents, whitepapers, crawled websites
ANALYSIS: Extract named entities and
relationships, classify and label; visually
understand relationships and trends
ACTION: Change R&D priorities and improve
marketing approaches
Competitive Intelligence
Viz/Search
metadata
relationships
data
Understanding
entities

9. * Understand IP among competitors
* Assist legal team with litigation
* Custom search experience
* Custom extractors:
Electronic parts
Memory types
Flash memory
Customer: Technology Company

10. SourcesKAS
GOAL: Discover new drugs, detect side-effects,
speed R&D
SOURCES: Published research reports, patents,
adverse effects databases, genomics and
proteomics databases
ANALYSIS: Extract named entities and
relationships, classify and label; visually
discover trends and relationships
ACTION: Change R&D priorities
Drug Discovery
Viz/Seach
relationships
data
Understanding
entities
pathways
sequences

11. * Lousy search
* Internal regulators can’t find by accession number
* Custom extractors:
Accession number
Ontology of active ingredients
Drug names
FDA

12. SourcesKAS
GOAL: Scalable analysis of customer relationship
engagements
SOURCES: Call center and web help contact
narratives
ANALYSIS: Ingest massive data sets; visually
discover trends, novelty, and relationships
ACTION: Predict new product issues
CRM Analytics
Viz/Search
relationships
data
Understanding
My iPhone is
very hot…

13. SourcesKAS
GOAL: Scalable analysis of network
failures
SOURCES: Uploaded syslog data and
configuration for routers and switches
ANALYSIS: Ingest massive data sets;
visually discover trends and relationships
ACTION: Solve network problems
Network Analytics
Viz/Search
relationships
data
Understanding

14. * Unable to manage customer network signals
* RDBMS
* Tiger team dumps database and runs Perl scripts for analysis
Router/Switch Vendor

15. SourcesKAS
GOAL: Reduce fraud
SOURCES: Analysis customer data
ANALYSIS: Extract patterns of web and service
usage, classify, label with normalized
geospatial and temporal metadata; visually
understand relationships and trends.
ACTION: Indentify fraudulent transactions and
patterns
Financial Services: Fraud
Viz/Search
metadata relationships
data
Understanding

16. SourcesKAS
GOAL: Identify what people want to buy
SOURCES: Crawl Twitter, blogs, and websites
ANALYSIS: Extract sentiments about products,
classify, label with normalized geospatial and
temporal metadata; visually understand
relationships and trends.
ACTION: Target sales and enhance offerings
Buy Signals
Viz/Search
metadata relationships
data
Understanding
sentiments

17. SourcesKAS
GOAL: Find case-supporting and actionable
information
SOURCES: Email repositories, Office
documents, patents, memos
ANALYSIS: Extract named entities and
relationships, classify and label; visually
discover trends and relationships
ACTION: Develop legal theories and prepare for
arguments
Legal Informatics
Viz/Search
metadata
relationships
data
Understanding
entities

18. Dell’s Kitenga Analytics Suite

19.  Aggregate
 Count
 Extract
 Transform
 Chart
 Graph
 Model
 Visualize
 Search
 Predict
Transform Big Data into Actionable Intelligence

20. Search
Facetted Search,
Visualization
Analytics
Extract, Crawl, Index,
NLP, Transform,
Machine Learning
Analytical
Producer
Analytical
Consumer
Visualization
Visualize, Model,
Interact

27. Cassandra in the Zoo
How Dell Is Integrating Cassandra

28. Cassandra Integration
Toad
IC
Cassandra
RDBMS
Salesforce
KAS
Cassandra
Crawls
Feeds

29. THANK YOU