Data Governance Handbook: A practical approach to building trust in data

Data Governance Handbook

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To my husband, for supporting my weekly writing sessions, encouraging me to keep going and pour my heart and experience into this book, being my life partner, and sharing this beautiful, messy, and incredible life with me.

To my children, who inspire me to stay curious, ask questions, and help others see the beauty in simplicity, love, and inclusion, every day.

To my father, who told me I belonged in tech, even when I was asked if I was in the wrong room on the first day of my first IT class, and who encouraged me to keep showing up and taking up space, no matter what others thought. Your encouragement inspires me every day, which in turn, impacts others, long after your passing.

To my teammates, mentors, mentees, and sponsors – thank you. You have taught me more than I can ever put into words. Thank you for inspiring me.

– Wendy S. Batchelder

Editorial Reviews

Much more than an innovative reference work for data governance professionals alone, this text is a beacon for anyone leading data-driven initiatives. Wendy is an exceptional guide through the complexities of data governance while maintaining a rigorous perspective on the business impact of data. A must-read for those aspiring to transform an enterprise through the power of information.

Dave Mayer, Vice President | Program Director, Gartner Data and Analytics Research Board

As a sales leader, there is nothing more critical than understanding and interpreting data. One of the biggest challenges is assessing integrity, and data governance has a direct impact on integrity. What I love about Wendy’s work is it’s not about data in a vacuum; she provides a level of business acumen and outcome orientation that far exceeds many practitioners in this field.

Melissa Steffen, General Manager Sales and Customer Success, Thomson Reuters

Wendy’s most recent Data Governance Handbook, A practical guide to building Trust in Data, for me, proves to be an industry-agnostic roadmap for success; with two of its primary on-ramps being 1) Trust and 2) Data! Wendy offers a pragmatic and reusable framework that can be adapted and adopted by all enterprises and organizations, unlocking meaning for every reader and organizational role from the Boardroom to the Back Office. Wendy’s transparent approach in unveiling the journey to building Trust in Data is refreshing, and her methodical principle-based approach, using practical industry-proven techniques, hints, and use cases, undoubtedly equips and empowers data and non-data practitioners, business leaders, and executive sponsors with a roadmap to success!

Stephen Harris, Former Corporate Vice President, Cloud + AI, Microsoft

Wendy Batchelder’s guide to data governance is an essential resource for anyone looking to harness the vast potential of data in a structured and effective manner. With her extensive experience as a Chief Data Officer and her clear, insightful approach, Wendy not only demystifies complex data governance concepts but also connects them directly to tangible business outcomes. This book is a must-have for leaders who are serious about making informed decisions that drive company success. Her practical frameworks and real-world examples equip professionals to launch and sustain impactful data governance initiatives with confidence. A compelling read, highly recommended for those committed to transforming their organizations through data.

Sadie St. Lawrence, Founder and CEO, Women in Data

The relevance of this book expands well beyond just data governance professionals and applies to GTM operations professionals as well, especially as many look to leverage AI capabilities to make every customer engagement more intentional. Wendy simplifies the complexity of data governance, making it easy for multiple functions within a global organization to apply her best practices accordingly. I recommend this book to any GTM or revenue operations leaders.

Ryan Mac Ban, President of Americas, UiPath

Contributors

About the author

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.

About the reviewer

Ankur Roy is a solutions architect at Online Partner AB in Stockholm, Sweden. Prior to this, he worked as a software engineer at Genese Solution in Kathmandu, Nepal. His areas of expertise include cloud-based solutions and workloads in a diverse range of fields such as development, DevOps, and security. Ankur is an avid blogger, podcaster, content creator, and contributing member of the Python, DevOps, and cloud computing community. He has completed all the available certifications in Google Cloud and several others in AWS and Azure as well. Moreover, he is an AWS Community Builder.

Table of Contents

Preface

Part 1: Designing the Path to Trusted Data

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

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

Conclusion

References

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

Conclusion

References

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

Conclusion

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

Conclusion

Part 2: Data Governance Capabilities Deep Dive

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

Conclusion

References

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

Why does it matter? What matters?

How do you measure the value?

What metrics should be used to measure maturity?

Who manages it?

What does maturity look like?

How should you use Data Lineage?

Building an optimal Data Lineage capability

Conclusion

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

Conclusion

9

Data Architecture

Data architecture defined

Simple wins

The value of data architecture

Why data architecture is often overlooked

Measures of success

Core capabilities

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

Conclusion

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 & Specific Measures of Success

Business Benefits of PDM

Conclusion

References

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

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

Conclusion

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

Follow through

Conclusion

Further reading

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

Conclusion

15

Adoption That Drives Business Success

Why adoption matters – getting started

Start with the why

Adjust the solution (if needed) and make it easy to use

Don’t forget about culture

Address barriers to adoption

Low adoption is costly

Quantitative costs of low adoption

Qualitative costs of low adoption

Why does adoption fail?

The solution is the problem

Your company is the problem

You are the problem

How to succeed at driving exceptional adoption

Recovering from failed launches

Uncover the root problem

Collaboration (almost always) wins

Post-deployment

Adoption roadmap

Monitoring activities

Baking adoption into SDLC practices

Conclusion

16

Delivering Trusted Results with Outcomes That Matter

How to message stakeholders

Focus on value and impact

Speak their language

Address concerns and build trust

Use clear and compelling communication

How to communicate unexpected results and variances from commitments

Offer clarity and context

Focus on solutions and next steps

Maintain transparency and open communication

How to deliver results to build trust

Prioritize collaboration and communication

Demonstrate expertise and competence

Foster a culture of openness and accountability

Capability review

Data governance

Metadata (business and technical)

Data quality

Data architecture

Data operations

Conclusion

Part 4: Case Study

17

Case Study – Financial Institution

Scenario - highly regulated entity – banking institution

Identifying quick wins

Initial discovery

Key themes

Quick wins

Messaging long-term solutions to the executive team

Messaging to the regulators

How to design for iterative delivery with impact

Results

Conclusion

Index

Other Books You May Enjoy

Preface

The world generates ~2.5 quintillion bytes of data every single day (and growing!). As a result, understanding and managing the data created becomes more complex every single day. It’s our job to drive simplicity, understanding, and ease of use to make accessing and using data as easy and understandable as possible.

As a data professional, our role is to ensure we can govern data and empower our businesses with the right data, at the right time, with the right controls. This book is a comprehensive guide on how to better understand what data governance is, its key components, and how to successfully position solutions in a way that translates into real, understandable business results. After reading this book, you will be able to successfully pitch and gain support for a data governance program, with measured outcomes in terms the business will understand and deeply value.

We will move from establishing a Chief Data and Analytics Office and building a business case to successfully implementing the more technical capabilities that any CDAO will need to deliver to drive successful data management. You will notice in this book that I emphasize the why behind these capabilities. In my experience, simply explaining what the capabilities are without being able to see how they can impact a business is a recipe for failure.

There are many more technical books available on each of these topics, and where I hope this book will provide a variance from what is already available is that: business value. I will spend time explaining the technical capabilities in terms your business stakeholders can understand, with the aim of creating a business-led program.

Ultimately, if you want to get into details about a specific tool or technical implementation, there are loads of other great resources (including books) you can head to drive your implementation, including a wonderful inventory from the publisher of this book, Packt.

Who this book is for

This book is for chief data officers, data governance leaders, data stewards, engineers who want to understand the business value of their work. It is also for IT professionals seeking further understanding of data management. Any business leader who wants to better understand data governance would also benefit from learning the basics, as well as any executive finding themselves managing a chief data and analytics officer who wants to better understand the discipline at a higher level. You should have a basic understanding of working with data and understand the basic needs of a business and how to meet those needs with data solutions. You do not need to have the knowledge or skills needed to sell solutions to executives, nor coding experience.

What you will learn:

Exactly how to position data governance to obtain executive buy-in

How to launch a governance program at scale with measured impact

Real-world use cases that enable you to take action quickly

How to obtain support for data governance-led digital transformations

How to launch strong, with confidence

A detailed step-by-step guide from ideation to delivery and beyond

What this book covers

Chapter 1

, What Is Data Governance?, introduces you to data governance. At face value, data governance may seem like a cost center, if not approached with value generation in mind. Many companies start a data governance program without the right support, structure, or funding model. First, you will learn the basics of what data governance is and how it relates to adjacent capabilities. Then, you will learn the components of data governance programs, why each component matters, and finally, why to treat data governance as an enabler for business value.

Chapter 2

, How to Build a Coalition of Advocates, explores gaining support for your program, which is arguably the most important part of launching a data governance program that drives impact. First, you will learn why and how to identify and secure the right executive sponsor for the data program, and then how to bring in additional leadership support. Lastly, you will learn how to engage and energize the entire company to collaborate toward value-based outcomes that matter to them.

Chapter 3

, Building a High-Performing Team, focuses on establishing a high-performing data governance team, which is a critical and long-term investment in the success of a company’s use of data. First, you will be introduced to the key roles in a successful data governance function, how they should optimally structure for results, and finally, how to establish routines and rhythms to support the operations of the team.

Chapter 4

, Baseline Your Organization, teaches you the importance of defining a baseline, not only for the organization as a whole but also for individual projects. A key component of measuring success is measuring where you start. You will learn how to capture a baseline and who to communicate it to. Finally, we will discuss how to ensure agreement on the baseline before beginning work.

Chapter 5

, Define Success and Align on Outcomes, focuses on the area where many data transformations fall flat – aligning on outcomes that matter to a business. Most data transformations stop with data outcomes and fail to reach the final mile – where the business uses the delivered data capabilities to drive operational efficiency, increased revenues, and better insights. In this chapter, you will learn why defining success beyond data and with the business matters, how to successfully map all relevant stakeholders (including secondary and tertiary stakeholders), and how to translate results into business terms.

Chapter 6

, Metadata Management, delves into establishing a high-value, high-return metadata management capability, which is required for any data governance program. The success or failure of a chief data and analytics officer hinges on being able to answer a few fundamental, core questions. Where is my data? Who owns it? How is it classified? Is it safe and secure? Can I leverage it for value? Do I know how to reduce risk? You will learn the answers to these questions and be guided through how to tactically set up a metadata management capability for success.

Chapter 7

, Technical Metadata and Data Lineage, explores establishing a high-value, high-return data lineage capability, which is a core capability for any data governance program. Following on from Chapter 6

, this chapter focuses on the data supply chain. You will learn the answers to the questions in Chapter 6

, with a focus on data lineage, and will be guided through how to set up data lineage for success.

Chapter 8

, Data Quality, examines understanding the quality of data and being able to have a defendable stance when it comes to Can I trust it?, which is key for any user of data or information that is used to make decisions. Establishing a data quality capability enables the CDO/CDAO and their teams to stand behind their data, being able to defend the quality of the information. This solution can also, when coupled with metadata management and data lineage, lead to a data certification process. You will learn the answers to the questions and be guided through how to tactically set up a data quality capability.

Chapter 9

, Data Architecture, delves into data architecture. Designing the patterns and optimal flow of information throughout an organization is sometimes more art than science. With data architecture, you will learn just that. First, you will be grounded in what good data architecture is, when and how it should be applied to an organization, why perfection is not the goal, and when not to involve data architects in a program.

Chapter 10

, Primary Data Management, focuses on primary data. One of the core capabilities of any organization is the ability to standardize and conform its most critical information – customer, product, and reference data. By nature, rationalized data provides a solution, whereas data used by multiple divisions for many uses is standardized and cleansed for the benefit of the organization as a whole. First, you will understand what primary data is and is not, clarifying misconceptions. Then, you will be guided through the various types of primary data, how to prioritize, and how to implement a strong and centralized primary data solution that will impact and elevate the power of data into a strategic asset. All the capabilities introduced so far will be woven into this powerful capability to tie them together.

Chapter 11

, Data Operations, explores how to run the operations of a data organization, including support for the running of primary data management, data warehouses, data lakes, and other authorized provisioning points managed by the data organization. First, you will learn what data operations are, how to scale effectively, and when to pull in engineering. Lastly, you will learn how to optimize DataOps as a core capability and what opportunities there are to automate.

Chapter 12

, Launch Powerfully, examines how to launch a good data governance program, which can quickly lose impact if not launched properly. The importance of the launch cannot be underscored enough. You will learn how to create simple and strong core messaging to engage and clearly articulate to the stakeholder community what and how delivery will be accomplished. Then, you will be led through the creation of a launch plan, a design of feedback loops to ensure continuous improvement, and finally, how to report on an ongoing basis for impact.

Chapter 13

, Delivering Quick Wins with Impact, delves into the post-launch period, when the data governance team must quickly begin to deliver results. The time to first value metric should be as short as possible while producing impacts that matter to the business. You will learn how to create momentum through the delivery of quick wins, how to communicate the wins to the business, and how to ensure that the business not only understands the results but also becomes an advocate for the future success of the data governance program.

Chapter 14

, Data Automation for Impact and More Powerful Results, focuses on automation, which is a lever that can be pulled to expedite data product deliveries. First, you will be introduced to what automation techniques can be applied to data governance. Second, you will learn how to select the right automation solutions for their transformation. Finally, you will learn how to power their transformation with automation across all solutions.

Chapter 15

, Adoption That Drives Business Results, explores business adoption. Now that you have learned what data governance is, how to gather support, design a program, baseline the organization, launch, and deliver against the plan, you need to be able to ensure that your solutions are used by the business. Building an adoption roadmap, you will be able to articulate to your stakeholders how to use the solutions, ensuring a lasting impact of the solutions provided to the organization. Lastly, you will double the impact by ensuring ongoing supports are in place.

Chapter 16

, Delivering Trusted Results with Outcomes That Matter, teaches you how to ensure consistency in what was promised to stakeholders versus what was actually delivered. As the implementation of data governance occurs, the chief data/analytics officer and their leadership team must keep all messaging focused on results. You will be guided through how to explain variances in expected delivery versus real results, and how that builds trust. Finally, you will learn how to message back to stakeholders powerfully, during delivery for impact.

Chapter 17

, Case Study – Financial Institution, walks you through how to apply the topics covered in this book to an organization with a high degree of regulation (i.e., a financial institution). First, you will find use cases that are unique to this type of entity. Second, you will learn how to pull out the unique requirements and how to adjust messaging, sequencing, and results to accommodate the special needs of a highly regulated organization.

To get the most out of this book

You will learn how to design trust in data governance, starting with a fundamental understanding of what data governance is and the path to align an organization around the need for data governance. You will learn about the subcomponents of data governance, how to implement them, and how to drive adoption within the organization that creates value and ease of use for the business. To get the most out of this book, you should embrace a beginner’s mind, allowing you to relearn your approach to data concepts with fresh perspectives.

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Part 1:Designing the Path to Trusted Data

In this part, you will get an overview of how to design a data governance program, starting with the basic definitions, how to design a successful and scalable team, how to gain support, and how to define what success for your team and your company will be when it comes to data governance.

This part contains the following chapters:

Chapter 1

, What Is Data Governance?

Chapter 2

, How to Build a Coalition of Advocates

Chapter 3

, Building a High-Performing Team

Chapter 4

, Baseline Your Organization

Chapter 5

, Define Success and Align on Outcomes

1

What Is Data Governance?

As a data professional, some of the most frustrating conversations you will have about data governance will be about data programs feeling like a series of constraints versus a strategic enabler and that you are slowing business down vs. enabling excellence. Having led data transformations in three Fortune 500 companies, I have heard my fair share of these same messages. In my humble opinion, this is feedback; feedback that we are speaking in data speak and have not created a business case that is centered on value generation from the lens of our stakeholders. Rather, we have delivered a business case that is focused on data needs vs. business needs.

From a stakeholder’s perspective, there are a plethora of forces at stake in driving business: generating revenue through the sales teams, marketing to existing and potential customers, economic factors, and supply chain challenges. Data is a part of all of these critical business components, but it is not the first thing that comes to mind for our stakeholders. It is embedded in how business runs. It is a part of the day-to-day. It does not and should not feel like a standalone function.

Therefore, it’s our job to serve the business and to make it feel seamless to the business stakeholders we enable. When things feel like friction, it’s not necessarily because we’re not supported; it’s because we are one of many problems leaders are facing. Often, this comes in the form of a lack of buy-in or pushback, a seemingly endless number of questions, or simply a lack of engagement. For data professionals, conversations like this often end in frustration and the underfunding of the data governance program. I have seen this scenario over and over again in organizations firsthand and have heard it from data executives in every single industry. Far too often, it ultimately ends in the failure of a chief data & analytics officer to survive in the organization.

The question is, why?

Over the course of the next 17 chapters, I will explain why Chief Data and Analytics Officers fail to establish themselves as strategic business partners in their organizations and how you can overcome these common pitfalls and succeed. I will cover everything you need to know to build a case for data governance, rally your organization to support you, deploy a strong data governance program, leverage core data governance solutions, and apply all of this in a case study for a fictitious financial institution. Let’s dive in.

What you can expect to learn

Throughout this book, I promise to be transparent and direct about my experiences, and we’re going to start strong: governance programs fail because we have failed. We have failed to explain data governance in a way that makes sense to our business stakeholders. We have failed to deeply and intimately understand how our solutions will drive business success. In short, we have failed to explain in terms of business value. Conversely, the most successful data executives I have had the opportunity to work with have been successful because they deeply understand their company. They have spent the time to intimately understand the business, have crafted data solutions that enable business success and have successfully explained the benefits in terms of business results vs. data results.

As we go deep into these topics, I will not make assumptions about your experience implementing a successful data governance program. I will start with the basics by grounding you in definitions and the foundational capabilities and will build on how to launch a successful and impactful program, complete with the measures for success that will resonate with executive management and, ultimately, the board of directors for your organization. In the end, we will complete a case study to bring it all together. By the end of this book, you will have all you need to launch a program and deliver with excellence in your own organization. No longer will your organization be overwhelmed by data and underwhelmed by insight. We will change the narrative together.

In this chapter, we will ground ourselves in the basics of data governance and how it relates to adjacent capabilities. Then, we will define the components of a data governance program, why each component matters, and why we treat data governance as an enabler for business value. Subsequent chapters will dive deeper into the fundamental capabilities of a data governance program and how to implement them.

We will cover the following main topics:

What is data governance?

What’s driving the increasing need for data governance?

A brief overview of the data governance components

Data governance as a strategic enabler

Building a business case for your company

When and why to launch a data governance program

What’s driving the increasing need for data governance?

As I meet with data professionals across industries, it is abundantly clear that data governance is more important than ever. Executives are expecting more from data, but without the proper investment, it is harder than ever to respond at the speed of business.

So why is it increasingly difficult to respond to our executives at the pace of the business? There are a number of key factors, including the continuous rise in the following:

Data volume: We have more data today than yesterday (everyday!). In fact, the amount of data doubles every two years. Yet, we cannot expect to double our efforts or double our staffing or technology spend.

Regulation: The regulatory landscape is evolving, increasing expectations for how data is handled. In the United States, at the time of this writing, six states had signed privacy and data protection legislation into law. This increases the complexity of compliance for data handling.

Expectations: Executives’ expectations are rising, but our use of data is not. In a recent Tableau survey, >80% of CEOs wanted their organizations to be data driven, but less than 35% percent of employees felt their data was used in decision making.

User base: More individuals than ever are engaging in data, wanting it for their own use but needing to trust it. It puts our governance professionals in a position to add tremendous value by providing trusted, well-governed data to our organizations.

We have to become more innovative and more embedded, leveraging more technologies (e.g., automation and AI) than ever before. We talk about what that means for our customers. But what does it mean for us? If it’s difficult to answer key, basic business questions today, how do we expect to do it in two–three years with more data than ever? We must take this sense of urgency and build capabilities that will scale and last as our volume, complexity, expectations, and user base continue to grow at an unprecedented rate.

What is data governance?

Before we dive in, it’s important that we ground ourselves in basic definitions. During my first role in data management, we made the mistake of assuming that our stakeholders around the organization were aligned on what data we were referring to when we were discussing a particular domain of data. After several months of having difficult conversations on scope (if a particular data element, report, or system were in scope), we realized that we needed to go back and ground all stakeholders in a few very simple questions.

Data governance is the formal orchestration of people, processes, and technology by which an organization brings together the right data at the right time with the right controls to enable the company to drive efficient and effective business results. This formal orchestration should control, protect, deliver, and further enhance the value of data and create equity for an organization. Data governance is active and is delivered through capabilities, including the following:

Metadata management

Data lineage

Data quality

Data architecture

Mastering data

Data operations

We will explore these core capabilities, among other methods, in detail in subsequent chapters. The capabilities that make up a successful data governance program are defined slightly differently in just about every organization. Therefore, it is important that we define them here for the purposes of this book. Feel free to use the vocabulary in this text within your organization or the common language of your business.