Table of Contents for

Big Data Analytics: Turning Big Data into Big Money

Index

A

Abstraction tools

Access to data

Accuracy of data

Activity logs

Algorithms

Amazon

Amazon S3

Analysis of data. See Data analysis

Anomalies, value of

Apple

Applications

Archives

Artificial intelligence

Astronomy

Auto-categorization

Automated metadata acquisition systems

Availability of data

B

BA. See Business analytics (BA)

BackType

Backup systems

Batch processing

Behavioral analytics

Benefits analysis

Best practices

BI. See Business intelligence (BI)

Big Data and Big Data analytics

Big Science

BigSheets

Bigtable

Bioinformatics

Biomedical industry

Blekko

Business analytics (BA)

Business case

Business intelligence (BI)

Business leads

Business logic

Business objectives

Business rules

C

Capacity of storage systems

Cassandra

Census data

CERN

Citi

Classification of data

Cleaning

Click-stream data

Cloud computing

Cloudera

Combs, Nick

Commodity hardware

Common Crawl Corpus

Communication

Competition

Compliance

Computer security officers (CSOs)

Consulting firms

Core capabilities, data analytics team

Costs

Counterintelligence mind-set

CRUD (create, retrieve, update, delete) applications

Cryptographic keys

Culture, corporate

Customer needs

Cutting, Doug

D

Data

Data analysis

Database design

Data classification

Data discovery

Data extraction

Data integration

Data interpretation

Data manipulation

Data migration

Data mining

Data modeling

Data protection. See Security

Data retention

Data scientists

Data sources

Data visualization

Data warehouses

DevOPs

Discovery of data

Disk cloning

Disruptive technologies

Distributed file systems. See also Hadoop

Dynamo

E

e-commerce

Economist

e-discovery

Education

80Legs

Electronic medical records

Electronic transactions

EMC Corporation

Employees

Encryption

Entertainment industry

Entity extraction

Entity relation extraction

Errors

Event-driven data distribution

Evidence-based medicine

Evolution of Big Data

Expectations

Expediency-accuracy tradeoff

External data

Extract, transform, and load (ETL)

Extractiv

F

Facebook

Filters

Financial controllers

Financial sector

Financial transactions

Flexibility of storage systems

4Vs of Big Data

G

Gartner

General Electric (GE)

Gephi

Goal setting

Google

Google Books Ngrams

Google Refine

Governance

Government agencies

Grep

H

Hadoop

HANA

HBase

HDFS

Health care

Hibernate

High-value opportunities

History. See Evolution of Big Data

Hive

Hollerith Tabulating System

Hortonworks

I

IBM

IDC (International Data Corporation)

IDC Digital Universe Study

Information professionals

Information technology (IT)

In-memory processing

Input-output operations per second (IOPS)

Integration of data

Intellectual property

Interconnected data

Internal data

International Biological Program

International Data Corporation

International Geophysical Year project

Interpretation of data

J

Jahanian, Farnam

JPA

K

Kelly, Nuala O’Connor

Kogan, Caron

L

Labeling of confidential information

Latency of storage systems

Legal issues

LexisNexis Risk Solutions

Liability

Life sciences

LivingSocial

Location-based services

Lockheed Martin

Log-in screens

Logistics

Logs, activity

Loyalty programs

M

Maintenance plans

Manhattan Project

Manipulation of data

Manufacturing, in-memory processing technology

Mapping tools

MapR

MapReduce

Marketing campaigns

Memory, brain’s capacity

Metadata

Metrics

Mining. See Data mining

Mobile devices

Modeling

Moore’s Law

Mozenda

N

NAS

National Oceanic and Atmospheric Administration (NOAA)

National Science Foundation (NSF)

Natural language recognition

New York Times

Noisy data

NoSQL (Not only SQL)

O

Object-based storage systems

OLAP systems

OOZIE

OpenHeatMap

Open source technologies

Organizational structure

Outsourcing

P

Parallel processing

Patents

Pentaho

Performance measurement

Performance-security tradeoff

Perlowitz, Bill

Pharmaceutical companies

Pig

Pilot projects

Planning

Point-of-sale (POS) data

Predictive analysis

Privacy

Problem identification

Processing

Project management processes

Project planning

Public information sources

Purging of data

Q

Queries

R

RAM-based devices

Real-time analytics

Recruitment of data analytics personnel

Red Hat

Relational database management system (RDBMS)

Research and development (R&D)

Resource description framework (RDF)

Results

Retailers

Retention of data

Return on investment (ROI)

Risk analysis

S

SANS

SAP

Scale-out storage solutions

Scaling

Scenarios

Schmidt, Erik

Science

Scope of project

Scrubbing programs

Security

Semantics

Semistructured data

Sensor data

Silos

Sloan Digital Sky Survey

Small and medium businesses (SMBs)

Smart meters

Smartphones

Snapshots

Social media

Software. See Technologies

Sources of data. See Data sources

Space program

Specificity of information

Speed-accuracy tradeoff

Spring Data

SQL

Stale data

Statistical applications

Storage

Storm

Structured data

Success, measurement of

Supplementary information

Supply chain

T

Tableau Public

Taxonomies

Team members

Technologies

Telecommunications

Text analytics

Thin provisioning

T-Mobile

Training

Transportation

Trends

Trusted applications

Turk

Twitter

U

United Parcel Service (UPS)

Unstructured data

U.S. census

User analysis

Utilities sector

V

Value, extraction of

Variety

Velocity

Vendor lock-in

Veracity

Videos

Video surveillance

Villanustre, Flavio

Visualization

Volume

W

Walt Disney Company

Watson

Web-based technologies

Web sites

White-box systems

Worst practices

Wyle Laboratories

X

XML

Y

Yahoo