Data Governance Handbook : A Practical Approach to Building Trust in Data / Wendy S. Batchelder.

Format:

Book

Author/Creator:

Batchelder, Wendy, author.

Language:

English

Subjects (All):

Data protection.

Physical Description:

1 online resource (394 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2024]

Biography/History:

Batchelder Wendy S. : Wendy S. Batchelder is a three-time chief data officer with a wide understanding of how to take highly technical aspects of data and analytics and translate them into simple, concise business-valued solutions that are practical and easy to understand. Her background has led her to lead global data and analytics organizations at four Fortune 500 companies, including Wells Fargo, VMware, Salesforce, and now, Centene. She approaches situations with curiosity and humility, which has led to applying innovative data solutions to challenges with increased complexity to deliver value that companies can measure. A lifelong learner, Wendy graduated from Miami University with a BS in accounting with a minor in information systems, from Drake University with a master's in accountancy, and from the University of Iowa with an executive MBA, and she has completed ongoing professional education at Harvard Business School. Wendy resides in West Des Moines, Iowa, with her husband and six children.

Summary:

2.5 quintillion bytes! This is the amount of data being generated every single day across the globe. As this number continues to grow, understanding and managing data becomes more complex. Data professionals know that it’s their responsibility to navigate this complexity and ensure effective governance, empowering businesses with the right data, at the right time, and with the right controls. If you are a data professional, this book will equip you with valuable guidance to conquer data governance complexities with ease. Written by a three-time chief data officer in global Fortune 500 companies, the Data Governance Handbook is an exhaustive guide to understanding data governance, its key components, and how to successfully position solutions in a way that translates into tangible business outcomes. By the end, you’ll be able to successfully pitch and gain support for your data governance program, demonstrating tangible outcomes that resonate with key stakeholders.

Contents:

Cover

Title Page

Copyright

Dedication

Editorial Reviews

Contributors

Table of Contents

Preface

Part 1: Designing the Path to Trusted Data

Chapter 1: What Is Data Governance?

What you can expect to learn

What's driving the increasing need for data governance?

What is data governance?

What data governance is not

The objective of data governance - create business value

A brief overview of the data governance components

Policy and standards

Roles and responsibilities

Governance forums

Reporting on governance progress

Related teams and capabilities needed for success

Defining value

Who to meet with

Crafting a powerful why statement

Customizing the message

Data governance as a strategic enabler

The mission of the chief data and analytics office

The mission of the data governance program

Building a business case for your company

When and why to launch a data governance program

Why you should launch now

Why you might want to wait

How to build your delivery timeline

Conclusion

References

Chapter 2: How to Build a Coalition of Advocates

Building relationships with impact

Building trust one relationship at a time

Identifying stakeholders

Building a stakeholder map

The case for building trust in data

Landing an executive sponsor

Identifying and assessing sponsors

Building a business case to land a sponsor

A note on translating to business outcomes

Establishing feedback loops

Key roles to support you

How to gain the support of the masses

Chapter 3: Building a High-Performing Team

Optimizing for outcomes

Common outcomes

Defining core functions

Incorporating product management in organizational design

Three common data organization models

Establishing the office of the CDO.

Maturing and empowering through the hub and spoke model

Driving consistency through the centralized model

How to select the right model for your organization

What roles are needed

CDO versus CDAO

Data management roles

Data solutions leader

AI considerations

How to structure the team for results (and why)

Building the rhythm of the business of data

Enterprise data committee

Enterprise data council

Functional roles

Executive data domain leader

Business data steward

Technical data steward

Talent development

Recruiting talent

Growing the pipeline of talent

Upskilling and reskilling

Chapter 4: Baseline Your Organization

What is a data management maturity model?

Overview of process

Why you should baseline data management maturity

Foundational reasons to baseline

Executing a data management maturity assessment

[#1] Defining the scope

[#2] Identifying stakeholders

[#3] Selecting a data management maturity model

[#4] Execute the assessment and collect data

[#5] Analyzing the data

Alignment and agreement

[#6] Communicate the results

Communicating disaggregated results

Communicating aggregated results

Program baseline

[#7] Develop a plan

[#8] Implement the plan

[#9] Monitor progress

[#10] Reassess your maturity

Measuring success

Chapter 5: Defining Success and Aligning on Outcomes

Capabilities versus outcomes

Capabilities

Outcomes

Business outcomes and data capabilities

You need both

What is success?

What is the definition of value?

Defining success

Aligning on outcomes

Step 1 - Aligning on the business outcome

Step 2 - Defining data capabilities

Step 3 - Defining data capability deliverables

Step 4 - Aligning on value measurement.

Step 5 - Delivering iteratively

Step 6 - Reporting on progress iteratively

Step 7 - Measuring success in data outcomes

Step 8 - Measuring success in business outcomes

Summary

Barriers to achieving business value

Building value measures into your stakeholder map

Part 2: Data Governance Capabilities Deep Dive

Chapter 6: Metadata Management

Metadata management defined

What is metadata management?

The value of metadata management

Why does metadata matter?

Core metadata capabilities

Metadata standards

Business glossary

Data catalog

Building optimal metadata management capability

What is a data marketplace?

What's in a data marketplace?

Why does a data marketplace matter?

Measuring outcomes and return on investment

Setting up metadata management for success

Chapter 7: Technical Metadata and Data Lineage

Technical Metadata

Why does it matter? What matters?

How do you measure the value?

Which metrics should be used to measure maturity?

Who manages it?

What does maturity look like?

How should you use it?

Data Lineage

What metrics should be used to measure maturity?

How should you use Data Lineage?

Building an optimal Data Lineage capability

Chapter 8: Data Quality

Data quality defined

Data Quality Strategy

Data quality enablement

The value of measuring data quality

Core capabilities

Data profiling

Data cleansing

Data validation and standardization

Data enrichment

Feedback loops, exception handling, and issue remediation

Building an optimal data quality capability

Certified data

Transparency

Setting up data quality for success.

The real-time request

Integrations with other systems

Chapter 9: Data Architecture

Data architecture defined

Simple wins

The value of data architecture

Why data architecture is often overlooked

Measures of success

Establishing a data architecture program

As-is and to-be modeling

Building an optimal data architecture capability

Establishing design principles

Developing architectural standards

Tight integration with business architecture and IT architecture

Building data architecture into the systems development life-cycle process

Setting up data architecture for success

Chapter 10: Primary Data Management

Defining Primary Data Management

Reference Data

Primary Data versus Reference Data

Types of Primary Data

Customer

Product

Vendor [or Supplier]

Contact

Building an Optimal Primary Data Management Capability: Core Capabilities for Success

De-duping or Deduplication

Common Definitions

Golden Source Attribution

Hierarchies

Trust Logic

Integration

Quality Third-Party Enrichment

Consumption Model

CRM vs. PDM

What is CRM?

Key Differences

The Value of Primary Data Management

Building the Business Case

A Note on Scope of Program

Capability Statements

Conceptual Architecture

Directional Objectives &amp

Specific Measures of Success

Business Benefits of PDM

Chapter 11: Data Operations

Defining data operations

Data operations versus IT operations

IT and data operations partnerships

Data operations capabilities

The value of data operations

The unsung hero of data governance

Making data operations more visible

Building an optimal data operations capability and setting up for success

Conclusion.

Part 3: Building Trust through Value-Based Delivery

Chapter 12: Launch Powerfully

Assessing readiness for launch

Performing the assessment

Common baseline

Simple and strong core messaging

Crafting a compelling vision

As Is versus To Be (aka current versus future state)

Getting crisp with your messaging

Writing a narrative memo

Design based on outcomes

Creating a repeatable process

Designing feedback loops

Setting and meeting expectations in the program launch

Chapter 13: Delivering Quick Wins with Impact

Finding quick wins

Identifying areas of need

Rationalizing the list

Prioritizing the list

Short-term versus long-term wins

Organizational readiness considerations

Investment/funding models

Follow through

Communicate effectively for support

Why policies, standards, and procedures can generate buzz

Data ownership

Applying a product mindset to data capabilities

Product management for data

Products versus non-product solutions

Building momentum through a continuous delivery model

Continuous delivery model

Further reading

Chapter 14: Data Automation for Impact and More Powerful Results

What is automation?

What is data automation?

Types of data automation

Advanced data automation capabilities

Benefits of data automation

Measuring the benefits

How to determine which type of automation to use

Step 1 - Identify your goals

Step 2 - Identify the existing process and pain points

Step 3 - Agree on the problem statement(s)

Step 4 - Align on the approach and ROI calculation

Step 5 - Execute

Step 6 - Measure and report

Third-party enrichment

Data solution examples powered by data automation

Customer domain

Operations domain

Chapter 15: Adoption That Drives Business Success.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781803242477

1803242477

OCLC:

1435803132