2. Big Questions About
BIG DATA
What is New about BIG DATA?
Why is BIG DATA Important?
Why aren’t More Enterprises
Practicing BIG DATA?
What Will it Take to Spur
Adoption and Democratize BIG DATA ?
5. What’s the BIG
Problem?
VOLUME
TYPE
SPEED
BIG DATA
What is New about Big Data?
6. What is NEW …
Technology to Process,
Store & Analyze
BIG DATA
In a TIME-EFFICIENT and
COST-EFFECTIVE Way
What is New about Big Data?
7. New Approaches to
BIG DATA
Hadoop is an open
source framework
for processing,
storing and
analyzing massive
amounts of
distributed, multi-
structured data.
What is New about Big Data?
8. New Approaches to
BIG DATA
Next Generation Data
Warehouses use
massively parallel
processing, columnar
architectures and data
compression to analyze
not-quite-so-massive
data in close to real-
time.
What is New about Big Data?
9. New Approaches to
BIG DATA
Multiple flavors of NoSQL
[Not Only SQL] Databases
emerging to support specific
types of Multi-Structured
Data and Real-Time Web
Applications.
What is New about Big Data?
10. New Approaches to BIG DATA
Are …
Complimentary
NOT Competitive
Right Tool, Right Job
What is New about Big Data?
11. The Result?
Enterprises Now Have The
Technologies Needed to
Turn
BIG DATA
Into Actionable, Timely
Insights.
What is New about Big Data?
12. So Why is BIG DATA Important?
Process and Analyze ALL Your Data
Ask NEW Questions
Ask MORE Questions
Get ANSWERSFASTER
Get CLEARER Insight
MAKE BETTER BUSINESS
DECISIONS
Why is Big Data Important?
13. BIG DATA
THE New, DEFINITIVE Source
of COMPETITIVE ADVANTAGE
Across ALL Industries.*
* Wikibon Big Data Manifesto, 2011
Why is Big Data Important?
15. But … BIG DATA
Adoption is Slow
BIG DATA Technologies Difficult to
Deploy, Manage and Use
Dearth of Skilled BIG DATA
Practitioners and Data Scientists
Enterprises Lack Right MINDSET to
Exploit BIG DATA
Why aren’t More Enterprises Practicing Big Data?
16. What Will It Take to …
DEMOCRATIZE
BIG DATA?
How Will We Democratize Big Data?
17. Democratizing BIG DATA
NEEDED: TOOLS TO ABSTRACT AWAY COMPLEXITY
DEPLOY ANALYZE
ADMINISTER VISUALIZE
SECURE AUTOMATE
INTEGRATE
PROCESS
MANAGE
How Will We Democratize Big Data?
19. Democratizing BIG DATA
NEEDED: BIG DATA TRAINING AND EDUCATION
BIG DATA
INFRASTRUCTURE DATA SCIENCE
ADVANCED BIG DATA PROCESSING &
PREDICTIVE INTEGRATION
ANALYTICS
How Will We Democratize Big Data?
22. Democratizing BIG DATA
NEEDED: A CHANGE OF MINDSET
EXPERIMENTATION
IMAGINATION Rinse
COLLABORATION
COMMUNICATION
&
WILLINGNESS TO FAIL Repeat
PERSEVERENCE
STORYTELLING
TRUST
ACTION
DATA-DRIVEN ENTERPRISE
How Will We Democratize Big Data?
23. THANK YOU
Democratizing
BIG DATA
Jeff Kelly
Big Data Analyst
Wikibon
jeff.kelly@wikibon.com
@jeffreyfkelly
wikibon.org/bigdata
Notas del editor
Dawn of record keeping, data volumes have been increasing.In the more recent path, pharmaceutical, telecomm and other industries dealing with data growth before the term Big Data arose.
People been trying to turn data to knowledge since ancient times, 7500 BC when Mesopotamians created the first accounting system.Again, looking to more recent times, in the 1980s and 1990s decision management and business intelligence software arose. Largely failed.
Volume is increasing, but more importantly the types of data – multistructured data – are expanding and being created at greater speeds than ever before, the need to make sense of the data in near real time to maximize value is greater than ever before. Confluence of three trends.
We now have the tech to deal with it. Not impossible before, but impractical due to time or money constraints.
QuickHadoop history.
Quick MPP data warehouse history (commodity hardware, data compression, columnar arcitectures)
Quick overview of NoSQLDBs.
Iterate.
Examples – Sears for dynamic pricing, CIA to understand terrorist chatter, Netflix to determine which movies to recommend, BoA to detect fraud in real time, Philips healthcare to understand outcomes, research the genome.
McKinsey 140,000 – 190,000 data scentists needed 2018.