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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
How to build and maintain strong data organizations--the Dummies way Data Governance For Dummies offers an accessible first step for decision makers into understanding how data governance works and how to apply it to an organization in a way that improves results and doesn't disrupt. Prep your organization to handle the data explosion (if you know, you know) and learn how to manage this valuable asset. Take full control of your organization's data with all the info and how-tos you need. This book walks you through making accurate data readily available and maintaining it in a secure environment. It serves as your step-by-step guide to extracting every ounce of value from your data. * Identify the impact and value of data in your business * Design governance programs that fit your organization * Discover and adopt tools that measure performance and need * Address data needs and build a more data-centric business culture This is the perfect handbook for professionals in the world of data analysis and business intelligence, plus the people who interact with data on a daily basis. And, as always, Dummies explains things in terms anyone can understand, making it easy to learn everything you need to know.
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Seitenzahl: 433
Veröffentlichungsjahr: 2022
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Data Governance For Dummies®
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ISBN: 978-1-119-90677-3 (pbk); 978-1-119-90678-0 (ebk); 978-1-119-90679-7 (ebk)
Data Governance For Dummies®
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Table of Contents
Cover
Title Page
Copyright
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Beyond the Book
Where to Go from Here
Part 1: Data Everywhere
Chapter 1: Defining Data Governance
Understanding Data Governance
Developing a Data Governance Framework
Preparing for Data Governance
Chapter 2: Exploring a World Awash in Data
Defining Data
The Role of Data in the 21st Century
Understanding the Impact of Big Data
Chapter 3: Driving Value through Data
Identifying the Roles of Data
Improving Outcomes with Data
Chapter 4: Transforming through Data
Examining the Broader Value of Data
Developing a Data Strategy for Improved Results
Part 2: Delivering Data Governance
Chapter 5: Building the Business Case for Data Governance
Identifying the Business Case for Data Governance
Creating a Data Governance Strategy Proposal
Chapter 6: Focusing on the Fundamentals of Data Governance
Establishing Basic Approaches to Data Governance
Examining Where Data Governance Brings Value
Part 3: Developing Data Governance
Chapter 7: Establishing Data Governance Objectives
Defining an Organization’s Priorities
Determining the Desired Outcomes
Chapter 8: Identifying Data Governance Roles and Responsibilities
Exploring Roles and Responsibilities in Data Governance
Creating Data Governance Leadership Groups
Chapter 9: Designing a Data Governance Program
Analyzing Stakeholder Needs
Developing Policies and Procedures
Chapter 10: Deploying a Data Governance Program
Implementing a Data Governance Program
Using Data Governance Across the Enterprise
Part 4: Democratizing Data
Chapter 11: Running a Successful Data Governance Program
Managing the Day-to-Day of a Data Governance Program
Automating a Data Governance Program
Chapter 12: Measuring and Monitoring a Data Governance Program
Identifying Ways to Measure Program Success
Deploying Monitoring Processes
Chapter 13: Responding to Data Governance Challenges and Risks
Exploring the Complexities of Data Governance
Anticipating Ongoing Program Risks and Challenges
Part 5: The Part of Tens
Chapter 14: Ten Data Governance Best Practices
Start Small and Progressively Build Your Program
Ensure the Program Is Aligned with the Interests of the Organization
Get Your Leaders to Advocate for the Program’s Success
Begin the Change Management Process Early
Establish Meaningful Metrics
Create Abundant Learning Opportunities for Team Members
Communicate Early and Often
Remind Stakeholders This Is a Program, Not a Project
Focus on People and Behaviors
Understand What Data Matters to the Organization
Chapter 15: Ten Essential Data Governance Stakeholders
Data Owner
Data Steward
Data Custodian
Data User
Data Governance Manager
Chief Data Officer (CDO)
Chief Information Officer (CIO)
Chief Executive Officer (CEO)
Data Governance Council (DGC)
Data Governance Program Office (DGPO)
Index
About the Author
Connect with Dummies
End User License Agreement
List of Tables
Chapter 2
TABLE 2-1 Quantification of Data Storage
TABLE 2-2 The Differences Between Data and Information
Chapter 9
TABLE 9-1 Differences between Policies and Procedures
Chapter 11
TABLE 11-1 Ongoing Data Governance Communication Plan Template
List of Illustrations
Chapter 1
FIGURE 1-1: The most common elements of a data governance program.
FIGURE 1-2: Common components of a data governance framework.
Chapter 2
FIGURE 2-1: The qualitative and quantitative nature of data types.
FIGURE 2-2: Data leads to insight.
FIGURE 2-3: The lifecycle of data.
FIGURE 2-4: Data growth in zettabytes from 2010 to 2025.
Chapter 3
FIGURE 3-1: Basics steps in data analysis.
FIGURE 3-2: The relative complexity and business value of four types of analyti...
Chapter 4
FIGURE 4-1: A basic orientation of the components of a data catalog.
FIGURE 4-2: The four components of a data strategy.
Chapter 7
FIGURE 7-1: The building blocks of data governance success.
FIGURE 7-2: A data governance maturity model.
Chapter 8
FIGURE 8-1: Comprehensive view of a data governance organization.
FIGURE 8-2: How each of these data roles differs in their areas of focus.
Chapter 9
FIGURE 9-1: Simple entity-relationship diagram (ERD) of college data and relati...
FIGURE 9-2: Example of knowledge graph visualization.
FIGURE 9-3: Seven-step process to create data governance policies and procedure...
FIGURE 9-4: An example data governance policy and procedure document.
Chapter 10
FIGURE 10-1: The three phases of change management.
FIGURE 10-2: Typical ongoing change management request and approval process.
FIGURE 10-3: The basic components of a data governance communications strategy....
FIGURE 10-4: The basic building blocks of a training plan.
Chapter 11
FIGURE 11-1: Ongoing communications serves four main objectives.
FIGURE 11-2: The PDCA continuous improvement framework.
FIGURE 11-3: Basic MDM architecture.
FIGURE 11-4: Software tools serve all these data governance areas.
FIGURE 11-5: More than half of data management operations can be automated (sha...
Chapter 12
FIGURE 12-1: An example of a data governance performance indicator.
FIGURE 12-2: An example of a data governance balance scorecard measurement.
Guide
Cover
Title Page
Copyright
Table of Contents
Begin Reading
Index
About the Author
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Introduction
In the 21st century, data really matters. Some even claim it’s the most important asset organizations possess today. Reviewing the evidence, I think they might be right.
Although all organizations use and manage data, far too many don’t do it well. As a consequence, they are missing out on opportunities to grow their businesses, increase revenue, and leverage valuable insights. In addition, they’re putting their organization at greater risk in a world of complex regulatory requirements and punishing cyberattacks.
Today, an increasing number of leaders recognize that managing data well and increasing its quality can deliver remarkable results for their organizations. They’re discovering the value behind data governance.
Unfortunately, implementing data governance is no walk in the park. Research from Gartner suggests that up to 90 percent of organizations fail at their first attempt. This book can help fix that. Proven, high-quality guidance is required and that’s what this book is all about.
I wrote this book to help you succeed at managing and optimizing your data in better ways than you do today. Understanding data governance will empower you to increase the value and quality of your organization’s data and manage the risks and obligations associated with it.
About This Book
Despite the title, this isn’t a book for dummies. It’s for those smart people who recognize that managing data well is the right thing to do. But you already knew that.
Data governance may not be the most exciting topic of our times, but in terms of importance and positive organizational impact, it’s certainly hard to beat. The increasing demand for data governance is a direct result of the rise in the value and volume of data and the attendant opportunities and risks this presents.
Since you’re reading this book, my assumption is that you generally get this. Ahead of many, you recognize the value of data governance and that attaining the skills and methods to implement a successful program will benefit you and your organization.
That said, I wrote this book for those with no data governance knowledge and for those with existing skills but with a desire for more insight and detail. In other words, whether you know a little or a lot on the topic, this book is designed to help you. In practical terms, like all Dummies books, you can read it from cover to cover, or you can just jump to a certain section.
Data governance is often a confusing and complex topic. It also has a lot of unfamiliar terminology associated with it, particularly if you don’t have a technical or data background. As an educator, I like to explain things simply. In fact, I like to explain things the way I would like them explained to me. This means I’ve gone to great efforts to eliminate the confusion and complexity of the topic while also providing easy-to-understand explanations. You may also find some repetition in chapters, and this is deliberate. Repeating some concepts, in a variety of contexts, reinforces the core ideas.
If you decide to read the book from cover to cover, you’ll notice it has five parts that are designed to take you from concepts to planning and right through to implementation and support:
Part 1
, Data Everywhere:
The chapters in this part provide a detailed background of data governance and explain why it’s important in a world of increasing volume, variety, and velocity of data.
Part 2
, Discovering Data Governance:
The chapters in this part explain how to build the business case and get approval for your data governance program. It also explains the value data governance can bring to different functions in your organization.
Part 3
, Developing Data Governance:
The chapters in this part detail the steps to planning, designing, and developing your data governance program.
Part 4
, Democratizing Data:
The chapters in this part cover how to support and maintain your data governance program once it is implemented, including monitoring it and measuring results.
Part 5
, The Part of Tens:
The chapters in this part provide two lists — one that identifies best practices and the other that covers the essential stakeholders involved in data governance.
Foolish Assumptions
I made the following assumptions about you, dear reader, when writing this book. You:
Have little patience for unnecessary jargon and deeper explanations and just want what’s necessary to get the work done and be successful.
Want a comprehensive guide to data governance that can be read cover to cover or used to provide the answers you seek.
Know that this book doesn’t provide information and details about specific technology vendors.
Understand that data governance is focused on people and their behaviors. You won’t be learning how to write a database query.
Recognize that data governance is not the same as data management.
Appreciate that data governance can appear easier to implement than in reality. The tips and best practices in the book will help.
Acknowledge that some repetition is deliberate in order to reinforce important concepts and to describe them in different contexts.
Understand that data governance is evolving, so you’re best to supplement these topics by exploring current best practices and research online.
Understand that you cannot implement data governance alone. It requires collaboration across the enterprise. Your colleagues may need to read the book too!
Icons Used in This Book
You’ll see a few icons scattered around the book. These icons highlight bits of information that are of particular importance to you.
The Tip icon shares an insight or lesson that I’ve learned the hard way — so you don’t need to — or it’s been gleaned from extensive research and suggests a good way to approach an issue.
The Remember icon highlights information that’s especially important to know. This is key information that you’ll want to reference later.
The Warning icon tells you to watch out! It highlights information that may save you headaches. Don’t skip over these.
Beyond the Book
You can complement everything in this book with additional research online, including some excellent written and video content. You might also enjoy watching my “Learning Data Governance” video series on LinkedIn Learning. It’s an hour and a half and is a light summary of some of the key areas in this book. You can also check out this book’s online cheat sheet by searching for Data Governance for Dummies Cheat Sheet at dummies.com.
Where to Go from Here
You don’t need to read this book from cover to cover. You can, if that strategy appeals to you, but it’s set up as a reference guide, so you can jump in wherever you need to. Looking for something in particular? Take a peek at the table of contents or index, find the section you need, and then flip to the page to get your answer.
Part 1
Data Everywhere
IN THIS PART …
Understand what data governance is and the value it can bring to your organization
Discover why data is now considered your organization’s most valuable asset
Explore the many valuable roles that data plays in every business
Learn the importance of creating and implementing a data strategy
Chapter 1
Defining Data Governance
IN THIS CHAPTER
Unpacking the definition of data governance
Discovering the elements of a data governance program
Understanding the role of data culture
Determining data governance readiness
Today, the topic of data governance suffers from a public relations problem. In the pages ahead, I explain how data governance is one of the most valuable programs that an organization can implement right now. The trouble is that many business leaders have an entirely different perception, assuming they even know what data governance is.
Although the momentum toward adoption is picking up pace, far too many organizations don’t understand what data governance is. Many executives admit it’s not showing up on their list of top priorities. They perceive data governance to be bureaucratic, complex, expensive, and largely discretionary.
Leaders soon change their views when they understand that data governance, done right, can help unleash the remarkable power of data, drive business growth, and enable successful digital transformations, all while reducing significant business risk.
What’s not to like?
Solving this public relations problem begins with growing the number of people — in every role and level — who understand what data governance is and the value it brings to all organizations. That’s why, in this chapter, I’m starting at the beginning, by defining governance and explaining what it means relative to the growing volume and complexity of data that confronts every business.
This book is dedicated to changing perceptions and helping more organizations succeed. When data governance is fully understood, your organization can enjoy its powerful results. Managing data well is a big deal and it must be a priority for every business leader.
In the second half of this chapter, I delve into the importance of determining whether your data culture — the level of commitment to data-driven decisions and actions — is ready for data governance.
Understanding Data Governance
The topic of data governance seems abstract to far too many people without a full appreciation of its definition, role, and value. You may have experienced puzzled looks from friends, family, and colleagues when you told them that your work involves data governance. They want to be happy for you, so they smile and congratulate you, but there’s a reasonable chance they don’t know what you’re talking about.
I want to help fix that.
If you’re going to communicate the importance of data governance to your organization so you can, for example, build a business case and get approval to design and deploy a program, you need to explain the topic clearly. Your senior leaders will appreciate it. So will your colleagues. I start by answering the most fundamental question.
What is meant by governance?
When first presented with the phrase data governance, most people immediately understand the data part, but can be quickly confused by the use and context of the word “governance.”
Governance is not a word that most of us use on a regular basis. Sure, you create data. You use data. You store data. These concepts make sense. But governing data? That’s not something that comes up too often. It sounds abstract, a little exotic, and frankly, complicated.
Fortunately, it’s not nearly as complex as it appears. Understanding what it means right now, in the context of data, will put you at ease as you immerse yourself in the world of data governance.
Governance is the manner in which an entity chooses to oversee the control and direction of an area of interest. It typically takes the form of how decisions are made, regulated, and enforced. When entities grow and increase in complexity, formal governance becomes important. Left ungoverned, the possibility of devolving into chaos is all too probable. I’m reminded of what used to happen when the teacher briefly left the classroom in my elementary school. Anarchy!
Governance is a relatively straight-forward concept, but in so many contexts, it’s extremely important and impactful.
To some degree, everything in life is governed. It’s just a question of its degree of formality. Parents may have a loose set of rules that govern how they raise their children, whereas our national government has a more rigorous governance system to enable, support, and enforce our democracy and its laws.
The formality and structure that governance takes depends on context and intent. For example, given their goals as organizations, governance in a public agency such as a city will differ greatly from that of a private enterprise. Each of these entities has different purposes and responsibilities.
Governance is the system that formalizes control, processes, and accountabilities, so that specific results such as meeting goals or sustaining standards can be attained.
The many domains that have adopted the term governance apply it relative to intent. Project governance, for example, is focused on a process for how project decisions are made and how communications are managed between stakeholders. Another area, land governance, concerns itself with issues relative to land ownership and the rules under which decisions are made around land use and control.
This book is concerned with exploring techniques and approaches for deriving as much value from your data as possible while also managing any associated risks. The priority of data value and risk management has escalated in recent years, as data continues to grow rapidly and flow with velocity into the organization from a large number of sources. Today, the average data volume in an organization is growing at over 30 percent a year, and many are growing at an even faster rate.
These factors create urgency for many organizations to build a formal system for data control and oversight, and that includes structured processes and accountabilities.
Organizations want to reap the benefits of data abundance while managing its growing risks. In other words, organizations are now demanding data governance.
What is data governance?
To be effective at their jobs, staff want to find the data they need quickly, and they want it to be high-quality data. This means the data needs to be accurate and current. Leaders want data to provide the basis for rich insights that enable timely and informed data-driven decision-making. The legal department requires data to be handled by everyone in a manner consistent with laws and regulations. Product designers want data to inform creative decisions that align with marketplace demands and customer trends. Security professionals are tasked with ensuring that the data is appropriately protected.
Undoubtedly, a wide range of stakeholders want to harness the remarkable power of data.
To achieve these and other increasingly common business demands, you need some form of data control and accountability in your enterprise. Quality results require the diligent management of your organization’s data.
Data governance is all about managing data well.
Today, when data is managed well, it can drive innovation and growth and can be an enterprise’s most abundant and important lever for success.
Well managed data can be transformational, and it can support the desirable qualities of a data-driven culture. This is when decisions at all levels of the organization are made using data in an informed and structured manner such that they deliver better outcomes internally and to customers. Research confirms that most business leaders today want their organizations to be data-driven, but, according to a survey by NewVantage Partners, only around 32 percent are achieving that goal.
Successful data governance also means that data risks can be minimized, and data compliance and regulatory requirements can be met with ease. This can bring important comfort to business leaders who, in some jurisdictions, can now be personally liable for issues arising from poor data management.
Every organization manages data at some level. All businesses generate, process, use, and store data as a result of their daily operations. But there’s a huge difference between businesses that casually manage data and those that consider data to be a valuable asset and treat it accordingly. This difference is characterized by the degree in which there are formalities in managing data.
Broadly, the discipline in which an organization acts in recognition of the value of its information assets (a fancy term for data with specific value to an organization, such as a customer or product record) is called enterprise information management (EIM). Governing and managing data well is a central enabler of EIM, which also includes using technologies and processes to elevate data to be a shared enterprise asset.
Data governance versus data management
Within the EIM space there are many terms that sound like they might mean the same thing. There is often confusion about the difference between data governance and data management. Data governance is focused on roles and responsibilities, policies, definitions, metrics, and the lifecycle of data. Data management is the technical implementation of data governance. For example, databases, data warehouses and lakes, application programming interfaces (APIs), analytics software, encryption, data crunching, and architectural design and implementation are all data management features and functions.
Data governance versus information governance
Similarly, in EIM, you may want clarity on the difference between data governance and information governance. Data governance generally focuses on data, independent of its meaning. For example, you may want to govern the security of patient data and staff data from a policy and process perspective, despite their differences. The interest here is on the data, not as much on the business context. Information governance is entirely concerned with the meaning of the data and its relationship in terms of outcomes and value to the organization, customers, and other stakeholders.
You might experience obvious overlap between the two terms. For sure, as a data governance practitioner, to some extent you’ll be operating in both the data and information governance worlds each day. This shouldn’t present an issue as long as the strategy for data governance is well understood. My view is that understanding the context of data, a concept known as data intelligence, and the desired business outcomes, complement data governance efforts in a valuable manner.
The value of data governance
If an organization considers data to be a priority — and an increasing number of businesses believe just that (in fact, according to Anmut, a data consultancy, 91 percent of business leaders say that data is a critical part of their organization’s success) — and it puts in place processes and policies to leverage the data’s value and reduce data risks, that organization is demonstrating a strong commitment to data controls and accountabilities. In other words, that organization values data governance.
Fundamentally, data governance is driven by a desire to increase the value of data and reduce the risks associated with it. It forces a leap from an ad hoc approach to data to one that is strategic in nature.
Some of the main advantages achieved by good data governance include:
Improved data quality
Expanded data value
Increased data compliance
Improved data-driven decision-making
Enhanced business performance
Greater sharing and use of data across the enterprise and externally
Increased data availability and accessibility
Improved data search
Reduced risks from data-related issues
Reduced data management costs
Established rules for handling data
Any one of these alone is desirable, but a well-executed and maintained data governance program will deliver many of these and more.
In the absence of formalized data governance, organizations will continue to struggle in achieving these advantages and may, in fact, suffer negative consequences. For example, poor quality data that is not current, inaccurate, and incomplete can lead to operating inefficiencies and poor decision-making.
Data governance does not emerge by chance. It’s a choice and requires organizational commitment and investment.
Creating a data governance program
The basic steps for creating a data governance program consist of the following (these steps also form the basic outline of this book):
Defining the vision, goals, and benefits
Analyzing the current state of data governance and management
Developing a proposal based on the first two steps, including a draft plan
Achieving leadership approval
Designing and developing the program
Implementing the program
Monitoring and measuring performance
Maintaining the program
Depending on the level of sophistication and the nature of the business, the design and implementation of a data governance program can vary greatly. Unfortunately, there’s no one-size-fits-all approach. One business may implement data governance with an emphasis on realizing greater revenue growth, while another may be more concerned with the regulatory requirements of their industry. Each organization will approach data governance in a manner that best reflects their desired outcomes.
As a discipline that has matured over a number of years, data governance is achieved through a set of common elements. Figure 1-1 illustrates many of the most common areas. You can think of these as a good representation of data governance scope. Right now, several of the terms in the illustration may not be familiar to you. Don’t worry, because this book explores each one of these and suggests approaches that may work for you.
In summary, data governance is about managing data well and helping to deliver its optimum value to your organization. It includes ensuring your data is available, usable, and secure. It’s the actions that team members take, the policies and processes they must follow, and the use of technologies that support them throughout the data lifecycle in their organization.
It’s safe to say that for a growing number of organizations, data governance is becoming a very big deal.
(c) John Wiley & Sons
FIGURE 1-1: The most common elements of a data governance program.
Developing a Data Governance Framework
You can’t buy a data governance program off-the-shelf. That’s actually good news. Organizations must implement a program relative to its level of interest, as well as its needs, budget, and capabilities. Even a modest effort can produce meaningful results. Glancing at all the areas in Figure 1-1 may seem overwhelming, but not all these elements need to be addressed (certainly not at first), and there are different degrees in which each can be pursued. As you read and learn about them in this book, you can decide what makes most sense for your organization.
Regardless of how and to what degree you implement the elements of a data governance program, you’ll need a basic set of guiding concepts and a structure in which to apply them. This is called the data governance framework.
While there are many framework variations to choose from, including ISACA’s Control Objectives for Information and Related Technologies (COBIT) IT governance framework, they share some common components that address people, process, and technology.
I’ve done the hard work of distilling down the most important qualities of a data governance framework and captured them in Figure 1-2. In addition, these components are explored in detail throughout this book. You’ll learn everything you need to know about how to implement a basic data governance framework. This is a foundation that will serve you and your organization well and enable you build upon it over time.
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FIGURE 1-2: Common components of a data governance framework.
The data governance framework in Figure 1-2 is not in a specific order, with the exception of leadership and strategy, which is a prerequisite for the rest of the framework.
Leadership and strategy
Your data governance program must be aligned with the strategy of the organization. For example, how can data governance support the role that data plays in enabling growth in specific markets? Data plays a role in many aspects of organizational strategy, including risk management, innovation, and operational efficiencies, so you must ensure there’s a clear alignment between these aspects and the goals of data governance.
The disconnect between business goals and data governance is the number one reason that data governance programs fail. When mapping organizational strategy to data governance, you need the support, agreement, and sponsorship of senior leadership. I’ll be blunt about this: Without full support from your organization’s leaders, your data governance efforts won’t succeed.
Roles and responsibilities
Your data governance program will only be possible with the right people doing the right things at the right time. Every data governance framework includes the identification and assignment of specific roles and responsibilities, which range from the information technology (IT) team to data stewards.
While specific roles do exist, your organization must understand that data governance requires responsibilities from nearly everyone.
Policies, processes, and standards
At the heart of every data governance program are the policies, processes, and standards that guide responsibilities and support uniformity across the organization. Each of these must be designed, developed, and deployed. Depending on the size and complexity of the organization, this can take significant effort.
Policies, processes, and standards must include accountability and enforcement components; otherwise it’s possible they will be dead on arrival.
Metrics
The data governance program must have a mechanism to measure whether it is delivering the expected results. Capturing metrics and delivering them to a variety of stakeholders is important for maintaining support, which includes funding. You’ll want to know if your efforts are delivering on the promise of the program. Based on the metrics, you and your team can make continuous improvements (or make radical changes) to ensure that the program is producing value.
Tools
Fortunately, a large marketplace now exists for tools in support of data governance and management. These include tools for master data management, data catalogs, search, security, integration, analytics, and compliance. In recent years, many data science-related tools have made leaps in terms of incorporating ease-of-use and automation. What used to be complex has been democratized and empowered more team members to better manage and derive value from data.
Communications and collaboration
With the introduction of data governance and the ongoing, sometimes evolving, requirements, high-quality communications are key. This takes many forms, including in-person meetings, emails, newsletters, and workshops. Change management, in particular, requires careful attention to ensure that impacted team members understand how the changes brought about by the data governance program affect them and their obligations.
A large number of disparate stakeholders need to work together in order to effectively govern data. Collaboration is essential and can be the difference between success and failure. Good collaboration requires a positive culture that rewards teamwork. It also requires clear channels between teams, such as regular meetings. Online collaboration platforms are increasingly being used too.
Preparing for Data Governance
It might seem a good idea just to form a team, create a plan, buy some tools, and then implement data governance. That would be a mistake. Data governance requires careful treatment, beginning with understanding whether an organization is ready to accept it. As the following sections make clear, there are some traps that you can avoid if you and your team are diligent.
Being ready as an organization involves determining the extent to which a data culture exists. Intuitively you can conclude that an immature, reactive data culture, where data is simply handled in an ad hoc manner, is an entirely different experience than a sophisticated data-driven culture.
There are other prerequisites for data governance success. These include ensuring that the organization’s strategy is fully aligned with the proposed program. As mentioned, any misalignment here is the number one reason data governance program deployments fail.
At the end of the chapter, I provide a basic checklist that will help you evaluate your organization’s overall readiness for a data governance program.
What is data culture?
In my over 30 years as a business and technology leader, I’ve had the chance to observe at close range hundreds of projects and initiatives, some that have succeeded and some that have failed. I’ve been deeply interested in why so many efforts miss the mark. After all, most teams work thoughtfully and diligently to deliver a quality result. Of course, much has been researched and written about this topic, but there’s one area in particular that’s worth exploring relative to your mission to design and deploy a successful data governance program.
I’ve seen well-designed projects and initiatives fall flat and fail even though their teams seem to have done everything right. Too often, the work gets deployed into an environment that is either not ready for change or doesn’t have the optimum conditions for success. A study by IDC noted that organizations are spending trillions of dollars on technological upgrades — digitally transforming their businesses — and 70 percent are failing because they don’t have the prerequisite data culture to support these efforts.
Yes, data culture. In a boxing match, culture defeats strategy almost every time.
Imagine for a moment designing and deploying a data governance program for an organization that has little or no data culture. Intuitively this sounds like a disaster in the making. To be fair, every organization has some form of a data culture; it just might not be pretty.
Assessing the data culture
If you want to increase your chances of success — and I think you do — you need to understand the data culture of your organization and determine how to broaden and mature it if necessary. You need an environment for success.
On a basic level, data culture is how your organization values data and how it manages and uses it. There’s a wide chasm between companies that simply manage data as a consequence of doing business versus those that consider data central to how their organization operates and makes decisions (the latter being the qualities exhibited by a mature data culture).
Effective data cultures support and empower all employees, from the newest intern to the CEO, to access and use meaningful and timely data for their work. Such cultures ensure that employees have attained the skills they need to use data analytics and can make good data-driven decisions. It’s not an overstatement to say that these types of organizations are often defined by their enlightened and competitive use of data.
In a data culture, decisions don’t rely on gut feelings, guesses, or the opinion of the highest paid person in the room (admit it, this is all too common, right?). Rather, decisions are based on data and the insights they can produce.
It’s been said that data culture is decision culture.
In Chapter 2, I go to great lengths to describe why data is an organization’s most valuable asset today. In a world undergoing rapid digital transformation, data is the metaphorical oil that is powering and enabling it all. To be competitive, a progressive data-driven strategy is no longer optional. It’s a central concern. Data culture can be now considered a new way of doing business in the digital age.
Leaders in all types of organizations are recognizing that to succeed in the third decade of the 21st century and beyond, they must leverage the enormous power and value of data. This acknowledgement, and the actions that senior leaders take to foster the use of data, is the primary success factor in the development and maturity of an effective data culture.
Trust comes in a close second. This means that team members will only make data-driven decisions if they trust the data they’re using. Trust is built when data is high-quality, its origin and value is understood, and team members know how it can contribute to the goals of the business.
To start, you need to assess the maturity of your organization’s data culture. You and your team can interview leaders and team members. You can also observe how people make decisions, how decisions are communicated, and the degree to which data is currently governed and managed. It won’t be just one thing that provides a score for your data culture, but a mix of inputs. If the conclusion is that your data culture is sufficient for the introduction of a data governance program, you’re in good shape.
What you shouldn’t lose sight of here is that implementing data governance will be a positive and important contributing factor to building a data culture.
Maturing the data culture
If you decide that you need to better prepare the organization for data governance by maturing the data culture, consider these items to start (good news! many of these items are covered in detail in this book):
Help leaders communicate the value of data and model the type of behavior that demonstrates that data is a priority. This must include communicating the positive results of using data.
Provide basic tools and education for data use that includes manipulating data, analytics, data cleansing, basic query commands, and visualization. Don’t overlook the remarkable capabilities of common applications such as spreadsheets.
Do something, even if it’s small, to show progress. A successful data culture doesn’t begin with the deployment of complex, far-reaching solutions. Rather, it can be eased into the organization via basic data-management skills offered in a classroom setting or online.
Recognize that resistance and frustration is part of the journey. Rather than fighting it, find ways to bring comfort and rewards to team members. At a minimum, provide a channel for feedback and positive discussion.
Assessing data governance readiness
So, you’ve either determined your organization has a good data culture, or you’ve put into action some steps to help move it forward, and now you’ve decided it’s time to roll up your sleeves and begin designing a data governance program. Right?
Wait. Not so fast. There are a few other items you should consider in order to determine if your organization is ready for a data governance program. If the right conditions don’t exist, you may be walking into a minefield, only one step away from disaster.
Unfortunately, the success rate of data governance programs on the first try remains low. That’s me being nice. It’s important to maximize the conditions for success. You may not even get a second chance.
This book walks you through all the steps for designing and creating a data governance program, but you also have to consider the readiness of your organization prior to beginning the journey.
The following basic checklist of items will help you determine the data governance readiness of your organization:
The basis of a data culture exists.
The program is 100 percent aligned with business strategy.
Senior leadership is 100 percent committed to the program and its goals.
Senior leadership understands this is a strategic, enterprise program and not the sole responsibility of the IT department.
One or more sponsors have been identified at an executive level.
The program has a commitment to fund its creation and to maintain it in the long term.
The organization understands this is an ongoing program and not a one-off project.
You have documented the return-on-investment (ROI).
Legal and compliance teams (internally or externally) understand and support the goals of the program.
Fundamental data skills exist for the data governance journey.
The IT organization is capable and resourced to support the program.
Of course, this list is not exhaustive and there may be other items you consider relevant to your organization.
As you read through this book and begin planning your data governance program, return to this list often and assess the status of each item.
Chapter 2
Exploring a World Awash in Data
IN THIS CHAPTER
Defining data and its relationship to information
Exploring the role of data in the 21st century
Moving from data to insights
Discovering the impact of big data
I’ve had the fortunate opportunity to work in private industry, government, and academia. Regardless of the sector, I observed that almost all actions were taken in the context of constraints. There are always limitations around budget, skills, time, and more. Of course, that’s no big revelation. Scarcity is the central basis of economics. How resources are used and allocated is of great concern to everyone and largely drives societal behavior.
My observation about constraints was elevated — and to me particularly noteworthy — because it contrasted so greatly with the abundance of data that was available. Each organization had resource limitations that dictated the terms of their decision-making and actions, but also had such quantity of data, that abundance, rather than constraints, was a source of concern.
Recognizing the issue of data abundance, many questions arose in these organizations, such as could they take advantage of it? Could they protect it? Could they simply manage it?
Today, most organizations exist in an environment awash in data. The volume of data amassing may be the result of their day-to-day activities, their products and services, the data they have access to, or the data created and delivered by their customers and partners. Whatever the reason and source, this is the age of big data.
Governed and managed correctly, data has the potential to improve an organization’s operations and performance and increase profits and market share. To fully understand the potential of data abundance and learn how you can leverage its value, you need to understand what data is and how it fits into the larger business landscape.
Defining Data
You and I create and use data all the time. We usually take it for granted. It’s part of our daily personal and business vernacular. Like so many things, if I were to ask you to define data, you’d give me your definition and it may not be the same as mine. In fact, it may not even be entirely accurate. It’s not so much a criticism as a statement of how much we take data for granted, and perhaps don’t stop and pause to ensure each of us is on the same page.
For example, your colleague may ask you to gather data on a topic. Seems straightforward. But might they actually be asking you to gather information instead? They’re different things. If you gather data and then produce it for them, they’re going to be disappointed when their expectation was information.
We have to be on the same page. Data has a specific meaning and it’s really important to be clear on the definition, particularly as we start talking about information, knowledge, and insights. But it’s even more important as we consider data in the context of governance and management. After all, as I will soon make clear, data governance is not the same as data management.
A solid definition of data and its role today gets us on the same page and sets the stage for delivering on the promise of data governance.
What’s all the fuss about data?
Data refers to collections of digitally stored units, in other words, stuff that is kept on a computing device. These units represent something meaningful when processed for a human or a computer. Single units of data are traditionally referred to as datum and multiple units as data. However, the term data is often used in singular and plural contexts and, in this book, I’m going to simply refer to both as data. I’m happy to get that out of the way.
Prior to processing, data doesn’t need to make sense individually or even in combination with other data. For example, data could be the word orange or the number 42. In the abstract and most basic form, something we call raw data, we can agree that these are both meaningless.
Data is also defined based on its captured format. Specifically, at a high level, it falls into one of the following categories:
Structured:
Data that has been formatted to a set structure; each data unit fits nicely into a table in a database. It’s ready for analysis. Examples include first name, last name, and phone number.
Unstructured:
Data that's stored in a native format that must be processed to be used. Further work is required to enable analysis. Examples include email content and social media posts.
Semi-structured:
Data that contains additional information to enable the native format to be searched and analyzed.
Units of data are largely worthless until they are processed and applied. It’s only then that data begins a journey that, when coupled with good governance, can be very useful. The value that data can bring to so many functions, from product development to sales, makes it an important asset.
To begin to have value, data requires effort, a theme I will keep returning to throughout this book. If we place the word orange in a sentence, such as “An orange is a delicious fruit,” suddenly the data has meaning. Similarly, if we say, “The t-shirt I purchased cost me $42,” then the number 42 now has meaning. What we did here was process the data by means of structure and context to give it value. Put another way, we converted the data into information.
