A/B testing
agile analytics
algorithm specialists
algorithms, 2nd, 3rd
Allchin, Jim
AlphaGo program, 2nd, 3rd, 4th
Amazon
Amazon Web Services (AWS)
analytic expertise
analytic models
see also models
analytic software
databases
programming languages
analytic tools
analytics, principle of
analytics, types of
descriptive analytics
diagnostic analytics
predictive analytics
prescriptive analytics
Analytics Ascendancy Model
Analytics Effort document
analytics staffing
Andreasen, Alan
Andreessen, Marc
Apache Flink
Apache HTTP server
Apache Software Foundation, 2nd
applause rate
Apple, 2nd, 3rd
artificial intelligence (AI), 2nd, 3rd
applications
big data and, 2nd
example of failure
machine learning (ML) and, 2nd, 3rd
origins of
reasons for, recent resurgence
words of caution in working with
artificial neural networks (ANNs)
artificial intelligence on
deep learning and, 2nd
examples of architectures
technique
Australian Square Kilometre Array Pathfinder (ASKAP)
AWS, 2nd
Azure, 2nd, 3rd, 4th
Banko, Michele
batch jobs
Beam (Apache)
Bezos, Jeff
big data
applications of business analytics
artificial intelligence (AI) and, 2nd
black box models from
cloud computing, 2nd, 3rd
concept of
consumer activity
content generation and self-publishing
customer journey data, value of
data-driven approaches
analysis
data insights
developments towards start of
disk storage and RAM, plummeting cost of
ecosystem, 2nd
forming strategy for
kick-off meeting
programme team
scoping meetings
growth, for two reasons
importance of
improving analytic techniques
key roles in
machine data and IoT
new way of thinking about
new ways to using
open-source software
as organizations’ digital transformation
processing power, plummeting cost of
proliferation of devices, generating digital data
reasons for discussing as hot topic
role in medical research
role models
scientific research
solution, choosing technologies for
storage
story of
as unstructured data
using to guide strategy
collecting the data
competitors
external factors
own service and product
using the data
see also data; technologies, choosing
Bitbucket
black-box model
from big data
Bork, Robert
Brill, Eric
budget holders
‘build vs. buy’ decision
Bumblehive (data centre)
business analysts, 2nd
business expertise
business intelligence (BI) teams
business units
C++
Caffe
cancer research (case study)
CapEx, 2nd
cart abandonment
Cassandra
central processing unit (CPU)
churn reduction
Cisco
Visual Networking Index™
cloud computing, 2nd
benefits of
choosing technology
clustering
code distribution
collaborative filtering
competitors
CompStat system
computer storage, types of
Comscore
concurrency
consumer activity
content-based filtering
content generation and self-publishing
conversion rate optimization (CRO)
convolutional neural networks (CNNs)
copyrights
corporate strategies
costs
of cloud computing
of disk storage
of processing power
of RAM
saving, 2nd
critical intervention
Critical Path Software
cross-validation
customer data
applying basic analysis and machine learning to
linking
using
customer journey data
segmentation criteria
value of
customer lifetime value (CLV)
customer loyalty
customer segments
customer support, interactions with
D3.js
damage control
dark data
data
additional quantities of
additional types of
and analytics roles
collection of
moving and cleaning
primary concerns for securing and governing
data-driven organization
asking questions about business
challenging basic assumptions
creating and monitoring KPIs
getting new ideas
organizing the data
data engineers
data governance
data initiative programme team
analytic expertise
business expertise
strategic expertise
technical expertise
data insights
data lakes, 2nd
data privacy
data protection
Data Protection Directive of 1995
data science
agile analytics
algorithms
analytic software
analytic tools
analytics, types of
artificial intelligence and machine learning
black boxes, 2nd
implementing
key roles in
models
and privacy revelations
utilizing within organization
data scientists
data silo
data team, recruiting
data warehouses, 2nd
databases
choosing
document-oriented databases
graph databases
key-value stores
relational databases
search engine databases
wide column stores
db-engines.com, 2nd
Deep Blue, 2nd, 3rd
deep learning
artificial neural networks (ANNs) and, 2nd
problems with
DeepMind
demand and revenue
Deming, W. Edward
descriptive analytics
diagnostic analytics
differential privacy
digital platforms, visiting
disk storage
plummeting cost of
distributed computations
distributed data storage
document-oriented databases
eBay, 2nd, 3rd, 4th, 5th
Echo (Amazon)
edge computing. see fog computing
Einstein, Albert
Elasticsearch, 2nd
employee job satisfaction
end users, 2nd, 3rd, 4th
ensemble
ETL (extract, transfer, load) tool
EU–US Privacy Shield
exabyte
expert systems
Facebook, 2nd
fast data, 2nd
Fast Works
feature engineering
Few, Stephen
Flink framework
fog computing, 2nd
Forbes
forecasting
Forrester
Forrester Waves
fraud detection
GA360 (Google Analytics’ premium service)
Gartner
Gartner Hype Cycle
Gartner Magic Quadrants
Gartner’s Analytics Ascendancy Model
Gelly, Flink’s
General Data Protection Regulation (GDPR), 2nd, 3rd, 4th
General Electric (GE), 2nd, 3rd
General Public License (GPL)
genomic data (case study)
Geometric Intelligence
gigabytes (GB)
GitHub
Glassdoor website
Gmail
GNU project
Go (game)
goodness-of-fit test
Google, 2nd, 3rd, 4th, 5th, 6th, 7th
Google Analytics
Google Cloud, 2nd, 3rd
Google Maps
Google ML engine
GoogLeNet program
governance and legal compliance
data governance
data science and privacy revelations
personal data
privacy laws
for reporting
graph databases
graphical processing units (GPUs), 2nd, 3rd
Hadoop (Apache), 2nd, 3rd, 4th, 5th, 6th
Hadoop Distributed Files System (HDFS), 2nd, 3rd
hardware, choosing
Harvard Business Review
Higgs boson particle, discovery of
high-profile project failure (case study)
hiring experts, at scale
hiring process, for lead role
aligning with recruitment team
finding strong candidates
landing the candidate
Hive (Apache)
human resources (HR)
IBM, 2nd, 3rd
ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
Immelt, Jeff, 2nd, 3rd
Impact Areas for Analytics document
Indeed.com
Infrastructure as a Service (IaaS), 2nd, 3rd
Instacart
integer programming
internet, and publishing
Internet Explorer
Internet of Things (IoT), 2nd
machine data and
inventory
IT cost savings
IT teams
Jaklevic, Mary Chris
Java
JavaScript
job satisfaction
JSON format
Kafka (Apache)
Kasparov, Garry
Keras, 2nd
key performance indicators (KPIs), 2nd, 3rd
key-value stores
kick-off meeting
analytics input
business input
output
strategic input
technical input
KNIME (open source data analytics), 2nd
lambda architecture
Laney, Doug
The Large Hadron Collider (LHC), particle physics (case study)
latency
lead data scientist
lead scoring
leadership
ability to deliver results
breadth and depth of technical skills
hiring process for lead role
possession of three unrelated skill sets
legal and privacy officers
licenses, for open-source software
LIME (Local Interpretable Model-Agnostic Explanations) tool
Linden, Greg
linkage attacks, 2nd
Linux
LoRaWAN (Long Range Wide Area Network)
machine data and IoT
machine learning (ML), 2nd
artificial intelligence and
engineers
methods, 2nd, 3rd
MacLaurin, Ian
Mahout (Hadoop)
MapReduce programming model, 2nd
Marcus, Gary
marketing
massively parallel processing (MPP) databases, 2nd
medical research (case study)
MetaMind
micro-conversions, 2nd
Microsoft, 2nd
Microsoft Power BI
Microsoft Research
minimum viable product (MVP), 2nd, 3rd
MLlib (Spark)
model training
model transparency
models
deploying
designing
fitting (training/calibrating), to data
MongoDB, 2nd
Monte Carlo simulations, 2nd
National Security Agency (NSA)
Neo4j software, 2nd
Netflix, 2nd, 3rd, 4th, 5th
Netscape Communications Corporation
neural networks. see artificial neural networks
Nielsen
noSQL databases
Nurego
online customer journey
online publishing
open-source
advantages of
for big data
history of, 2nd
open-source software, 2nd
code distribution
licenses for
operational requirements
OpEx, 2nd
organization, successful deployment in
data-driven
data silos
focus on business value
getting right people on board
measuring results
reasons for, projects failure
remembering to stay agile
Otto group
outsourcing
personal data
personally identifiable information (PII), 2nd
personas, 2nd
petabytes (PB), 2nd
physical movement, records of
Platform as a Service (PaaS), 2nd
platform engineers
The Post
predictive analytics
predictive maintenance
Predix
premier image recognition challenge (case study)
prescriptive analytics
pricing methods
principal component analysis
privacy laws
private clouds, 2nd
Proceedings of the National Academy of Sciences
processing power, plummeting cost of
product customization
programme team
programming languages
public clouds, 2nd
Python (programming language), 2nd, 3rd, 4th, 5th
Qlik
quasi-identifiers
R (programming language), 2nd, 3rd, 4th, 5th
random access memory (RAM)
plummeting cost of
RankBrain
Rapid-Miner (software), 2nd
RASCI model
Realeyes
recommendation engines
recurrent neural networks (RNNs)
relational database management system (RDMS)
reporting specialists
Research & Development (R&D)
REST (representational state transfer) services, 2nd
retargeting
retention, customer
return on investment (ROI), 2nd, 3rd
revenue, demand and
RFM (Recency, Frequency, Monetary)
Safe Harbour Decision, EU
Safe Harbour Provisions
Salesforce, 2nd, 3rd, 4th
SAS Enterprise Miner, 2nd, 3rd, 4th
schema-less databases
Science (magazine)
scientific research
scrum framework
search engine databases
Sedol, Lee
Selenium tool
self-publishing, content generation and
self-service analytics
self-service capabilities, 2nd
sentiment analysis
ShopperTrak
SimilarWeb
Siri (Apple), 2nd
Snowden, Edward, 2nd
social media, 2nd
software, choosing
Software as a Service (SaaS), 2nd, 3rd
software framework
Solr (Apache), 2nd
Spark framework, 2nd, 3rd, 4th, 5th
split testing. see A/B testing
Splunk
SPSS (IBM), 2nd
Square Kilometre Array (SKA)
The Square Kilometre Array (SKA) astronomy (case study)
stakeholders, 2nd
Stallman, Richard
standard query language (SQL)
Stanley, Jeremy
storage
distributed data storage
limitations
types of
strategic expertise
streaming data
supply chain management
Tableau
Target Corporation, 2nd
team building
technical expertise
technologies, choosing
for big data solution
cloud solutions
considerations in
capabilities matching business requirements
extent of user base
freedom to customizing technology
future vision
industry buzz
integration with existing technology
open source vs. proprietary technologies
risks involved with adopting technology
scalability
technology recommendations
total cost of ownership
data pipelines
delivery to end users
hardware, choosing
software, choosing
technology pioneers
technology stack, 2nd
tensor processing unit (TPU)
TensorFlow (software), 2nd, 3rd
terabytes (TB)
Teradata
Tesco, 2nd
‘the God particle’. see Higgs boson particle
three Vs
training. see model training
training data, 2nd
Twitter, 2nd
Uber
University of Washington
unstructured data
variety
velocity
version control system (VCS)
Video Privacy Protection Act of 1988
Visual Networking Index™
visualization
for diagnostic analytics
tools
Vizio
volume
Walkbase
The Washington Post
predicting news popularity at (case study)
waterfall method, for project planning
Watson (computer)
Watson–Anderson failure in 2016 (case study)
Waze software
web analyst(s)
wide column stores
XML format
Yahoo
yottabyte
YouTube, 2nd, 3rd
zettabytes
