Book Notes: Big Data at Work - Davenport
My reading notes from the book.
Table of Contents
Big Data at Work - Davenport
Why Big Data Is Important to You and Your Organization
what is?
attention to: size
most difficult: lack of structure
big size
not important
biggest obstacle: impose structure
who are you
executives
marketing
supply chain
finance
hr
industries
retail
travel and transportation
telecom
media
finance
ch 02 How Big Data Will Change Your Job, Your Company, and Your Industry
which industries are affected by dig data?
industries that
move things
sell to consumers
employs machinery
sells or uses content
provides service
has physical facilities
involves money
Four future scenarios
business travel
Telecom Firms
ch 03 Developing a Big Data Strategy
ch 07 What You Can Learn from Start-Ups and Online Firms
Use Big Data for Product and Service Innovation
uses in
developing new product/service
mostly in online
difficult in tangible goods
common approaches in
how products are used
inform customers
driving more energy-efficiently
embedded in product
ex: tesla model s
monitor performance remotely
signal need for maintenance
comparison with other drivers
large organizations?
ways
separate business unit
Work on Tools, Not Just Applications
hadoop, hive, cassandra
Give Data Scientists Their Heads
autonomy
big data at linkedin
Address the Productivity of Big Data Work
virtual data marts - ebay
data analysis environments
eases creation of data sets
don't replicate existing data
allow unique views
data lab - teradata
data hub
facilitates sharing of data, algorithms
pivotal chorus - emc greenplum
automated a/b testing - linkedin
1000 tests per day
Contribute to the Commons
Voldemort - distributed storage system
Remember: Agile is too slow
Big Data at Kyruus
provides data to hospitals, insurance, pharma
to understand physician networks
"Bloomberg of physician information"
data sources
hr databases
physician credentialing systems
electronic patient records
supply chain databases
+1000 sources
most compelling: leakage of patient referrals outside of hospital
average: 50%
3 major groups
data acquisition, integration, processing
analytics
applications and ui
Take Advantage of Free and Low-Cost Stuff
cloud
amazon ec2
google appengine
ms azure
cost effective
more important: switching easy
open source software
data management tools
hadoop, pig, hive
R
SAS:
ready speacialized solutions:
to reduce credit card fraud
to optimize revenue in travel
Experiment on a Large Scale
randomized, controlled experiments
a/b testing
google
10K tests a year
on
search
advertising
ebay
web site designs
offline tests
lab studies
home visits
tools
ebay platform
leads process
keep track what's being tested
optimizely
a/b testing of websites
applied predictive technologies
management of testing for offline tests
linkedin
multiple changes in a page
Foster Close Collaboration
gathering skills
assembled teams of different skills
people in one room
use hackathons
Lessons Not Learned by Start-Ups and Online Firms
not sharing data with customers
they don't let you know what data they have about you
collecting data for data's sake
big data at Recorded Future
predicting future events
source:
websites, blogs, twitter
8 billion events and entities
Foresite
linguistic processing
scoring of events, entities
intelligence agencies
predictions for
terror
political unrest
private sector
hedge funds
market intelligence
Operating Analytics - Boston
for hospitals to optimize use of operating rooms
ORs expensive
utilization < 50%
Talking too much about technology
ch08 - What You Can Learn from Large Companies Big Data and Analytics 3.0
how new?
for large firms: it exists for a while
impresses them
lack of structure
power
low cost
it is about variety, not volume
ex:
retail bank
first time: analyzing log files
understanding multichannel customer interactions
easier than transaction systems
hotel firm
analyzing customer waiting lines with video analytics
health insurer
predict customer dissatisfaction by analyzing speech-to-text from call center recordings
sensors and operational devices
more structured data
sensors in devices
GE, UPS, Schneider
estimates
66 B$ savings from more efficient gas turbines
1% fuel reduction in aircraft => 30 B $ savings in 15 years
Big Data at UPS
telematics sensors in 46 K vehicles
speed, direction, braking, drive performance
goal
redesign drivers' route structures
ORION (on-road integrated optimization and navigation)
largest operations research project
savings
8.4 M gal of fuel in 2011
85 M less miles
Examples of Big Data Objectives in Large Companies
ceasars entertainment
data from
loyalty program
web clickstreams
real-time play in slot machines
if customers have bad luck, they will never come back
offer free meal coupon while they are at slot machine
to support internal business decisions
Big Data at United Healthcare
uses NLP to understand customer satisfaction
data source
call center recordings
process
calls -> text -> hadoop -> nlp
mayo clinic medical records
claims from United
to understand treatment of disease
Integrating Organizational Structures and Skills
Big Data at Macys.com
retailer
Kerem Tomak
talent
phd in science
strong programmers with analytical skills
Big Data at Bank of America
emphasis on integrated approach
three buckets
big transactional data
daat about customers
unstructured data
projects
understanding customers
presenting offers to well defined segments
providing cash-back offers
