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Knowledge means nothing if you can’t apply it. Knowledge without
action is useless. This book is the result of over 100 global
interviews with professionals in various industries, businesses of
all sizes.From low tech to high tech, but professionals who are all
using data to improve results. Extending on the foundations
introduced in the first two books in the It’s All Analytics
Series, this book illustrates how professionals in healthcare,
business, and government are applying these disciplines, methods,
and technologies. The goal of this book is to get leaders and
practitioners to start thinking about how they may deploy
techniques outside their function or industry into their domain.
Application of modern technology into new areas is one of the
fastest, most effective ways to improve results. By providing a
rich set of examples, this book fosters creativity in the
application and use of AI and analytics in innovative ways.
Knowledge means nothing if you can’t apply it. Knowledge without
action is useless. This book is the result of over 100 global
interviews with professionals in various industries, businesses of
all sizes.From low tech to high tech, but professionals who are all
using data to improve results. Extending on the foundations
introduced in the first two books in the It’s All Analytics
Series, this book illustrates how professionals in healthcare,
business, and government are applying these disciplines, methods,
and technologies. The goal of this book is to get leaders and
practitioners to start thinking about how they may deploy
techniques outside their function or industry into their domain.
Application of modern technology into new areas is one of the
fastest, most effective ways to improve results. By providing a
rich set of examples, this book fosters creativity in the
application and use of AI and analytics in innovative ways.
It's All Analytics! The Foundations of AI, Big Data and Data
Science Landscape for Professionals in Healthcare, Business, and
Government (978-0-367-35968-3, 325690) Professionals are challenged
each day by a changing landscape of technology and terminology. In
recent history, especially in the last 25 years, there has been an
explosion of terms and methods that automate and improve
decision-making and operations. One term, "analytics," is an
overarching description of a compilation of methodologies. But AI
(artificial intelligence), statistics, decision science, and
optimization, which have been around for decades, have resurged.
Also, things like business intelligence, online analytical
processing (OLAP) and many, many more have been born or reborn. How
is someone to make sense of all this methodology and terminology?
This book, the first in a series of three, provides a look at the
foundations of artificial intelligence and analytics and why
readers need an unbiased understanding of the subject. The authors
include the basics such as algorithms, mental concepts, models, and
paradigms in addition to the benefits of machine learning. The book
also includes a chapter on data and the various forms of data. The
authors wrap up this book with a look at the next frontiers such as
applications and designing your environment for success, which
segue into the topics of the next two books in the series.
The Problem? Companies are failing to deliver on AI and analytics
with over half stating they are "not yet treating data as a
business asset". Over half admit that they are not competing on
data and analytics. Seven out of 10 companies in a 2020 MIT study
reported minimal or no impact from AI so far. Among the 90% of
companies that have made some investment in AI, fewer than 2 out of
5 (40%) report business gains from AI in the past three years. And
only about 25% of organizations have actually forged this
data-driven culture. Is investment lacking? No. Companies now are
spending more than ever in data, analytics, and AI technologies. Is
it a lack of technology? No. There are fascinating breakthroughs
occurring on all fronts with image, voice, and streaming pattern
recognition on the forefront. Is it a lack of technical talent? Not
really. While some studies cite that we need to train more data
scientists, developers, and related professionals, the curve of
demand by supply is dampening. Is it a lack of creating an
executable strategic plan? Yes. While there has been a lot of
strategic wishing, organizations lack meaningful strategic plans.
Specifically, the development of executable strategies and the
leadership to see these strategies brought to fruition. This is the
problem. Lack of execution and lack of incorporating key components
that align and enable execution of the business strategy to
delivery is killing AI and analytics programs. Scott Burk and Gary
D. Miner have written this book for executives at all levels who
are charged with executing on analytics that need to address this
issue. The book provides unique insights into repairing the gaps
that programs need to fill to provide value from analytics
programs. It complements their three-part series, It's All
Analytics! by focusing on leadership decisions that augment data
literacy, organizational architecture, and AI case studies.
The Problem? Companies are failing to deliver on AI and analytics
with over half stating they are "not yet treating data as a
business asset". Over half admit that they are not competing on
data and analytics. Seven out of 10 companies in a 2020 MIT study
reported minimal or no impact from AI so far. Among the 90% of
companies that have made some investment in AI, fewer than 2 out of
5 (40%) report business gains from AI in the past three years. And
only about 25% of organizations have actually forged this
data-driven culture. Is investment lacking? No. Companies now are
spending more than ever in data, analytics, and AI technologies. Is
it a lack of technology? No. There are fascinating breakthroughs
occurring on all fronts with image, voice, and streaming pattern
recognition on the forefront. Is it a lack of technical talent? Not
really. While some studies cite that we need to train more data
scientists, developers, and related professionals, the curve of
demand by supply is dampening. Is it a lack of creating an
executable strategic plan? Yes. While there has been a lot of
strategic wishing, organizations lack meaningful strategic plans.
Specifically, the development of executable strategies and the
leadership to see these strategies brought to fruition. This is the
problem. Lack of execution and lack of incorporating key components
that align and enable execution of the business strategy to
delivery is killing AI and analytics programs. Scott Burk and Gary
D. Miner have written this book for executives at all levels who
are charged with executing on analytics that need to address this
issue. The book provides unique insights into repairing the gaps
that programs need to fill to provide value from analytics
programs. It complements their three-part series, It's All
Analytics! by focusing on leadership decisions that augment data
literacy, organizational architecture, and AI case studies.
Up to 70% and even more of corporate Analytics Efforts fail!!! Even
after these corporations have made very large investments, in time,
talent, and money, in developing what they thought were good data
and analytics programs. Why? Because the executives and decision
makers and the entire analytics team have not considered the most
important aspect of making these analytics efforts successful. In
this Book II of "It's All Analytics!" series, we describe two
primary things: 1) What this "most important aspect" consists of,
and 2) How to get this "most important aspect" at the center of the
analytics effort and thus make your analytics program successful.
This Book II in the series is divided into three main parts: Part
I, Organizational Design for Success, discusses ....... The need
for a complete company / organizational Alignment of the entire
company and its analytics team for making its analytics successful.
This means attention to the culture - the company culture
culture!!! To be successful, the CEO's and Decision Makers of a
company / organization must be fully cognizant of the cultural
focus on 'establishing a center of excellence in analytics'.
Simply, "culture - company culture" is the most important aspect of
a successful analytics program. The focus must be on innovation, as
this is needed by the analytics team to develop successful
algorithms that will lead to greater company efficiency and
increased profits. Part II, Data Design for Success, discusses
..... Data is the cornerstone of success with analytics. You can
have the best analytics algorithms and models available, but if you
do not have good data, efforts will at best be mediocre if not a
complete failure. This Part II also goes further into data with
descriptions of things like Volatile Data Memory Storage and
Non-Volatile Data Memory Storage, in addition to things like data
structures and data formats, plus considering things like Cluster
Computing, Data Swamps, Muddy Data, Data Marts, Enterprise Data
Warehouse, Data Reservoirs, and Analytic Sandboxes, and
additionally Data Virtualization, Curated Data, Purchased Data,
Nascent & Future Data, Supplemental Data, Meaningful Data, GIS
(Geographic Information Systems) & Geo Analytics Data, Graph
Databases, and Time Series Databases. Part II also considers Data
Governance including Data Integrity, Data Security, Data
Consistency, Data Confidence, Data Leakage, Data Distribution, and
Data Literacy. Part III, Analytics Technology Design for Success,
discusses .... Analytics Maturity and aspects of this maturity,
like Exploratory Data Analysis, Data Preparation, Feature
Engineering, Building Models, Model Evaluation, Model Selection,
and Model Deployment. Part III also goes into the nuts and bolts of
modern predictive analytics, discussing such terms as AI =
Artificial Intelligence, Machine Learning, Deep Learning, and the
more traditional aspects of analytics that feed into modern
analytics like Statistics, Forecasting, Optimization, and
Simulation. Part III also goes into how to Communicate and Act upon
Analytics, which includes building a successful Analytics Culture
within your company / organization. All-in-all, if your company or
organization needs to be successful using analytics, this book will
give you the basics of what you need to know to make it happen.
Practical Data Analytics for Innovation in Medicine: Building Real
Predictive and Prescriptive Models in Personalized Healthcare and
Medical Research Using AI, ML, and Related Technologies, Second
Edition discusses the needs of healthcare and medicine in the 21st
century, explaining how data analytics play an important and
revolutionary role. With healthcare effectiveness and economics
facing growing challenges, there is a rapidly emerging movement to
fortify medical treatment and administration by tapping the
predictive power of big data, such as predictive analytics, which
can bolster patient care, reduce costs, and deliver greater
efficiencies across a wide range of operational functions. Sections
bring a historical perspective, highlight the importance of using
predictive analytics to help solve health crisis such as the
COVID-19 pandemic, provide access to practical step-by-step
tutorials and case studies online, and use exercises based on
real-world examples of successful predictive and prescriptive tools
and systems. The final part of the book focuses on specific
technical operations related to quality, cost-effective medical and
nursing care delivery and administration brought by practical
predictive analytics.
It's All Analytics! The Foundations of AI, Big Data and Data
Science Landscape for Professionals in Healthcare, Business, and
Government (978-0-367-35968-3, 325690) Professionals are challenged
each day by a changing landscape of technology and terminology. In
recent history, especially in the last 25 years, there has been an
explosion of terms and methods that automate and improve
decision-making and operations. One term, "analytics," is an
overarching description of a compilation of methodologies. But AI
(artificial intelligence), statistics, decision science, and
optimization, which have been around for decades, have resurged.
Also, things like business intelligence, online analytical
processing (OLAP) and many, many more have been born or reborn. How
is someone to make sense of all this methodology and terminology?
This book, the first in a series of three, provides a look at the
foundations of artificial intelligence and analytics and why
readers need an unbiased understanding of the subject. The authors
include the basics such as algorithms, mental concepts, models, and
paradigms in addition to the benefits of machine learning. The book
also includes a chapter on data and the various forms of data. The
authors wrap up this book with a look at the next frontiers such as
applications and designing your environment for success, which
segue into the topics of the next two books in the series.
A long-awaited companion volume to T.K. Pratt's Dictionary of
Prince Edward Island English, this delightful collection includes
more than 1,000 proverbs, folk sayings, catchphrases, and idioms
characteristic of the speech and attitudes of Prince Edward
Islanders. Laid out in 72 themes ranging from food and mood, to
work and weather, the volume is instructive, easy to use, and
entertaining. Meticulously researched, Prince Edward Island Sayings
offers a unique blend of the scholarly and popular. The book
features a table of themes, full cross-references, maps, and an
extensive index. It is the only book of its kind for this unique
part of the world.
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