Data science strategy for dummies / by Ulrika Jagare.

Format:

Book

Author/Creator:

Jägare, Ulrika, author.

Series:

--For dummies.

For dummies

Language:

English

Subjects (All):

Big data.

Data mining.

Physical Description:

1 online resource (355 pages).

Edition:

1st edition

Place of Publication:

Hoboken, NJ : John Wiley & Sons, Inc., [2019]

Summary:

All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. Learn exactly what data science is and why it’s important Adopt a data-driven mindset as the foundation to success Understand the processes and common roadblocks behind data science Keep your data science program focused on generating business value Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.

Contents:

Intro

Title Page

Copyright Page

Table of Contents

Foreword

Introduction

About This Book

Foolish Assumptions

How This Book Is Organized

Icons Used In This Book

Beyond The Book

Where To Go From Here

Part 1: Optimizing Your Data Science Investment

Chapter 1: Framing Data Science Strategy

Establishing the Data Science Narrative

Capture

Maintain

Process

Analyze

Communicate

Actuate

Sorting Out the Concept of a Data-driven Organization

Approaching data-driven

Being data obsessed

Sorting Out the Concept of Machine Learning

Defining and Scoping a Data Science Strategy

Objectives

Approach

Choices

Data

Legal

Ethics

Competence

Infrastructure

Governance and security

Commercial/business models

Measurements

Chapter 2: Considering the Inherent Complexity in Data Science

Diagnosing Complexity in Data Science

Recognizing Complexity as a Potential

Enrolling in Data Science Pitfalls 101

Believing that all data is needed

Thinking that investing in a data lake will solve all your problems

Focusing on AI when analytics is enough

Believing in the 1-tool approach

Investing only in certain areas

Leveraging the infrastructure for reporting rather than exploration

Underestimating the need for skilled data scientists

`Navigating the Complexity

Chapter 3: Dealing with Difficult Challenges

Getting Data from There to Here

Handling dependencies on data owned by others

Managing data transfer and computation across-country borders

Managing Data Consistency Across the Data Science Environment

Securing Explainability in AI

Dealing with the Difference between Machine Learning and Traditional Software Programming

Managing the Rapid AI Technology Evolution and Lack of Standardization.

Chapter 4: Managing Change in Data Science

Understanding Change Management in Data Science

Approaching Change in Data Science

Recognizing what to avoid when driving change in data science

Using Data Science Techniques to Drive Successful Change

Using digital engagement tools

Applying social media analytics to identify stakeholder sentiment

Capturing reference data in change projects

Using data to select people for change roles

Automating change metrics

Getting Started

Part 2: Making Strategic Choices for Your Data

Chapter 5: Understanding the Past, Present, and Future of Data

Sorting Out the Basics of Data

Explaining traditional data versus big data

Knowing the value of data

Exploring Current Trends in Data

Data monetization

Responsible AI

Cloud-based data architectures

Computation and intelligence in the edge

Digital twins

Blockchain

Conversational platforms

Elaborating on Some Future Scenarios

Standardization for data science productivity

From data monetization scenarios to a data economy

An explosion of human/machine hybrid systems

Quantum computing will solve the unsolvable problems

Chapter 6: Knowing Your Data

Selecting Your Data

Describing Data

Exploring Data

Assessing Data Quality

Improving Data Quality

Chapter 7: Considering the Ethical Aspects of Data Science

Explaining AI Ethics

Addressing trustworthy artificial intelligence

Introducing Ethics by Design

Chapter 8: Becoming Data-driven

Understanding Why Data-Driven Is a Must

Transitioning to a Data-Driven Model

Securing management buy-in and assigning a chief data officer (CDO)

Identifying the key business value aligned with the business maturity

Developing a Data Strategy

Caring for your data

Democratizing the data

Driving data standardization.

Structuring the data strategy

Establishing a Data-Driven Culture and Mindset

Chapter 9: Evolving from Data-driven to Machine-driven

Digitizing the Data

Applying a Data-driven Approach

Automating Workflows

Introducing AI/ML capabilities

Part 3: Building a Successful Data Science Organization

Chapter 10: Building Successful Data Science Teams

Starting with the Data Science Team Leader

Adopting different leadership approaches

Approaching data science leadership

Finding the right data science leader or manager

Defining the Prerequisites for a Successful Team

Developing a team structure

Establishing an infrastructure

Ensuring data availability

Insisting on interesting projects

Promoting continuous learning

Encouraging research studies

Building the Team

Developing smart hiring processes

Letting your teams evolve organically

Connecting the Team to the Business Purpose

Chapter 11: Approaching a Data Science Organizational Setup

Finding the Right Organizational Design

Designing the data science function

Evaluating the benefits of a center of excellence for data science

Identifying success factors for a data science center of excellence

Applying a Common Data Science Function

Selecting a location

Approaching ways of working

Managing expectations

Selecting an execution approach

Chapter 12: Positioning the Role of the Chief Data Officer (CDO)

Scoping the Role of the Chief Data Officer (CDO)

Explaining Why a Chief Data Officer Is Needed

Establishing the CDO Role

The Future of the CDO Role

Chapter 13: Acquiring Resources and Competencies

Identifying the Roles in a Data Science Team

Data scientist

Data engineer

Machine learning engineer

Data architect

Business analyst

Software engineer

Domain expert.

Seeing What Makes a Great Data Scientist

Structuring a Data Science Team

Hiring and evaluating the data science talent you need

Retaining Competence in Data Science

Understanding what makes a data scientist leave

Part 4: Investing in the Right Infrastructure

Chapter 14: Developing a Data Architecture

Defining What Makes Up a Data Architecture

Describing traditional architectural approaches

Elements of a data architecture

Exploring the Characteristics of a Modern Data Architecture

Explaining Data Architecture Layers

Listing the Essential Technologies for a Modern Data Architecture

NoSQL databases

Real-time streaming platforms

Docker and containers

Container repositories

Container orchestration

Microservices

Function as a service

Creating a Modern Data Architecture

Chapter 15: Focusing Data Governance on the Right Aspects

Sorting Out Data Governance

Data governance for defense or offense

Objectives for data governance

Explaining Why Data Governance is Needed

Data governance saves money

Bad data governance is dangerous

Good data governance provides clarity

Establishing Data Stewardship to Enforce Data Governance Rules

Implementing a Structured Approach to Data Governance

Chapter 16: Managing Models During Development and Production

Unfolding the Fundamentals of Model Management

Working with many models

Making the case for efficient model management

Implementing Model Management

Pinpointing implementation challenges

Managing model risk

Measuring the risk level

Identifying suitable control mechanisms

Chapter 17: Exploring the Importance of Open Source

Exploring the Role of Open Source

Understanding the importance of open source in smaller companies

Understanding the trend.

Describing the Context of Data Science Programming Languages

Unfolding Open Source Frameworks for AI/ML Models

TensorFlow

Theano

Torch

Caffe and Caffe2

The Microsoft Cognitive Toolkit (previously known as Microsoft CNTK)

Keras

Scikit-learn

Spark MLlib

Azure ML Studio

Amazon Machine Learning

Choosing Open Source or Not?

Chapter 18: Realizing the Infrastructure

Approaching Infrastructure Realization

Listing Key Infrastructure Considerations for AI and ML Support

Location

Capacity

Data center setup

End-to-end management

Network infrastructure

Security and ethics

Advisory and supporting services

Ecosystem fit

Automating Workflows in Your Data Infrastructure

Enabling an Efficient Workspace for Data Engineers and Data Scientists

Part 5: Data as a Business

Chapter 19: Investing in Data as a Business

Exploring How to Monetize Data

Approaching data monetization is about treating data as an asset

Data monetization in a data economy

Looking to the Future of the Data Economy

Chapter 20: Using Data for Insights or Commercial Opportunities

Focusing Your Data Science Investment

Determining the Drivers for Internal Business Insights

Recognizing data science categories for practical implementation

Applying data-science-driven internal business insights

Using Data for Commercial Opportunities

Defining a data product

Distinguishing between categories of data products

Balancing Strategic Objectives

Chapter 21: Engaging Differently with Your Customers

Understanding Your Customers

Step 1: Engage your customers

Step 2: Identify what drives your customers

Step 3: Apply analytics and machine learning to customer actions

Step 4: Predict and prepare for the next step

Step 5: Imagine your customer's future

Keeping Your Customers Happy.

Serving Customers More Efficiently.

Notes:

Includes index

Description based on print version record.

ISBN:

1-119-56627-4

1-119-56626-6

OCLC:

1104533382