It's all analytics. Part II : designing an integrated AI, analytics, and data science architecture for your organization / Scott Burk, David Sweenor, Gary Miner.

Format:

Book

Author/Creator:

Burk, Scott, author.

Sweenor, David, author.

Miner, Gary, author.

Language:

English

Subjects (All):

Decision making.

Artificial intelligence.

Visual analytics.

Physical Description:

1 online resource (299 pages) : illustrations

Place of Publication:

Boca Raton, Florida ; Abingdon, Oxon : Routledge, [2022]

Summary:

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.

Contents:

Cover

Half Title

Title Page

Copyright Page

Dedication

Table of Contents

Foreword and Tribute to the Authors

Preface

Authors

SECTION I: DESIGNING FOR ORGANIZATIONAL SUCCESS

1 Some Say It Starts with Data-It Doesn't

Introduction

Organizational Alignment

Start with the End in Mind

Remove the Cultural Divide and Establish a Center of Excellence

Innovation-Oriented Cultures

CoE Team Structure

Full Service Team Members

Functionally Oriented Team Members

Data and Analytic Project Team Roles

Data and Analytics Literacy

What Is Data Literacy? Data Literacy vs Analytics Literacy

Designing the Organization for Program Success

Analytics Success Involves More than Technology

People and Process - Not Merely Technology

Ethics

Governance

Technology

Data and Analytics Platform Service Areas

Data and Analytics Architecture

Summary

References

Additional Resources

2 The Anatomy of a Business Decision

The Anatomy of a Business Decision

What Is a Business Decision?

The Value of a Decision Which Uses Data and Analytics?

Before Analytics

After Analytics

Types of Decisions

Strategic Decisions

Tactical Decisions

Operational Decisions

Human vs. Automated Decisions

Speed Is Everything

Well Why Does It Matter?

3 Trustworthy AI

Don't Be Creepy - Be Fair, Unbiased, Explainable, and Transparent

Creepiness

Fairness and Bias

Explainable and Transparent

Framework for Trustworthy Analytics

Ethical Foundations for Trustworthy AI

Key Requirements for Trustworthy AI

Other AI Ethical Frameworks

SECTION II: DESIGNING FOR DATA SUCCESS

4 Data Design for Success

Why Is Data So Important?.

Data Is the Cornerstone of Improvement

Processes Are Everywhere

The Problem - Issues with Data Continue to Persist

Firms Are Failing to Be Data Driven

Data and Analytics Explosion

On a Personal Note

The Potential of Data = Analytics

Framework for Data and Analytics - Some Fundamentals

The Typical Story of Data Growth, Data Complexity, and Data Needs

Data Volume

Data Variety

Data Velocity

Data Value

Data Veracity

The Pieces Are Interdependent and Circular - Keep Looking Forward for Next Generation Data

The Value of Data and Analytics

Data and Analytics Literacy Are Requirements to Successful Programs

How This Part Is Organized

Data and Analytics Literacy References

Additional Terms Related to This Chapter

Process and Data Quality References

5 Data in Motion, Data Pipes, APIs, Microservices, Streaming, Events, and More

APIs and Microservices

The Five Architectural Constraints of REST APIs

Other APIs - RPC and SOAP

API Benefits and Drawbacks

Benefits (Primarily to Developers)

Drawbacks

Microservices

Microservice Benefits and Drawbacks

Benefits

Events, Event-Driven Architectures and Streaming

Some Drivers and Examples of Events, Streaming Events, and CEP (Complex Event Processing)

IoT Is a Big Driver of Real-Time Events

Event Processing Advantages

How Businesses Benefit from Event Processing

Improved Customer Service

Reduction of Costs and More Efficient Use of Resources

Optimized Operations

ETL and ELT

Basic Terms Useful in This Chapter

Additional Relevant Terms

6 Data Stores, Warehouses, Big Data, Lakes, and Cloud Data

Why Data Is so Crucial to the Success of an Enterprise.

Data Storage - Two Designations - Volatile and Nonvolatile Memory

Primer on Data Structures and Formats

Data Stores Topology

Local File Systems and Network Data Storage

Operational Data Stores

Data Marts and the EDWs

Benefits and Drawbacks of the EDW

Benefits of an EDW

Drawbacks of an EDW

Cluster Computing and Big Data

What Is Big Data?

Big Data as a Concept

Big Data as a Technology

Why the Push to Big Data? Why Is Big Data Technology Attractive for Data Science?

Pivotal Changes in Big Data Technology

Optimized Big Data

Cloud Data - What It Is, What You Can Do, Benefits, and Drawbacks

Cloud Benefits and Drawbacks

Cloud Storage

"Other Big Data Promises", Data Lakes, Data Swamps, Reservoirs, Muddy Water, Analytic Sandboxes, and Whatever We Can Think to Call It Tomorrow

Data Lakes and Architecture

Some Terms to Consider Exploring

7 Data Virtualization

DV - What Is It?

A Platform Connecting to Hundreds of Data Sources

A Platform with Searchable Data and Rich Metadata

A Collaboration Tool for Functional Areas and Users

A Pathway for New Systems and System Migration

An IT Tool for Rapid Prototyping

A System for Enhanced Security of Data

The Continuing Quest for the "Single Versions of the Truth" - Motivation beyond the EDW

What Are the Advantages of DV?

A Sustainable Architecture for the Ever-Increasing Complexity of Data

Simplified User Experience

More Collaborative and Productive User Experience

Data in Near Real Time

Source Data and Combine Data Easily

No Need to Replicate and Make Physical Copies of Data

Improved Security and Administration

Positive Impact on the EDW, IT, and the Business.

Governance and Data Quality

DV Is Scalable - Scales Up and Scales Out

Enabling Future Data and Even Technology

What Are the Drawbacks of DV?

Some of the Major Disadvantages of DV

Are You Ready for DV?

8 Data Governance and Data Management

Data Governance - Policies, Procedure, and Process

Goals of Data Governance

Data Integrity

Data Security

Data Consistency

Data Confidence

Compliance to Regulations, Data Privacy Laws

Adherence to Organizational Ethics and Standards

Risk Management of Data Leakage

Data Distribution

Value of Good Data

Moving Data Quality Upstream Reduces Costs

Data Literacy Education

Technology to Support Data Management and Governance

Data Management

Master Data

Reference Data

Data Quality

Security

Some Terms Related to This Chapter to Consider Exploring

Data Quality Resource

9 Miscellanea - Curated, Purchased, Nascent, and Future Data

Data Outside Your Organization

Supplemental Data

Meaningful Data

Data for Free

Publically Available Data

Data Available from Commercial Entities and Universities

Data for Sale

Data Syndicators

Data Brokers

Data Exchange and Data Exchange Platforms

Data Marketplaces

Should You Monetize Your Data?

Future Data

Keep an Eye Out for Nascent Technologies and Trends in Applications of Analytics

GIS and Geo Analytics

Graph Databases

Time Series Databases

Today Is the Time to Start Collecting Data for the Future

Data Strategy and Data Paradigms

What Is DataOps?

SECTION III: DESIGNING FOR ANALYTICS SUCCESS

10 Technology to Create Analytics

Analytics Maturity.

Architectural Considerations for the Data Scientist

Data Discovery and Acquisition

Exploratory Data Analysis

Data Preparation

Feature Engineering

Model Build and Selection

Model Evaluation and Testing

Model Deployment

Model Monitoring

Legality and Ethical Use of Data

Automation and ML

The Real World is Different than University

Do You Know How to Bake Bread?

Analytical Capabilities and Architectural Considerations

Data Management as a Prerequisite

Starting with the Data

Starting with the Analytics

Data Sources

Analytics

Model Building

Reporting and Dashboards

Data Science

AI, ML, Deep Learning - Oh My!

Model Training

Model Inference

Model Management

Streaming Analytics

IoT and Edge Analytics

Cloud Ecosystems and Frameworks

A Few Example Architectures

Uber

Facebook

An Evolution of CRISP-DM

Feature Stores

Cost Considerations

Other Open Source Considerations

Technical Debt in Data Science and ML

Model Dependencies

Data Dependencies

Feedback

Anti-patterns or Poor Coding Habits

11 Technology to Communicate and Act Upon Analytics

An Analytics Confluence

Data Storytelling

Building an Analytics Culture

Model Ops

How Is Analytics Different?

Why Does an Organization Need Model Ops?

Model Ops Capabilities

Model Visibility

Model Repository

Model Performance Metrics

Contextualized Collaboration Framework

Keywords

12 To Build, Buy, or Outsource Analytics Platform

Analytics Infrastructure Components

What Really Matters (In Your Business)?

Build vs. Buy Considerations.

Strategy and Competitive Advantage.

Notes:

"A Productivity Press book."

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9780429343957

0429343957

9781000433999

1000433994

OCLC:

1262640656