Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program
264
Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program
264eBook
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Overview
- Provides a complete overview of the data governance lifecycle, that can help you discern technology and staff needs
- Specifically aimed at managers who need to implement a data governance program at their company
- Includes case studies to detail ‘do’s’ and ‘don’ts’ in real-world situations
Product Details
| ISBN-13: | 9780123978486 |
|---|---|
| Publisher: | Elsevier Science |
| Publication date: | 11/07/2012 |
| Series: | The Morgan Kaufmann Series on Business Intelligence |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 264 |
| Sales rank: | 828,787 |
| File size: | 21 MB |
| Note: | This product may take a few minutes to download. |
About the Author
John is widely published. John frequently writes and speaks on a variety of technology and data topics. His information management experience is balanced between strategic technology planning, project management and practical application of technology to business problems.
Read an Excerpt
Data Governance
How to Design, Deploy, and Sustain an Effective Data Governance Program
By John Ladley
Elsevier Science
Copyright © 2012 Elsevier Inc.All rights reserved.
ISBN: 978-0-12-397848-6
Excerpt
CHAPTER 1
Our opinions do not really blossom into fruition until we have expressed them to someone else. —Mark Twain
INTRODUCTION
While the main purpose of this book is to give the reader a solid head start on the deployment, implementation, or "standing up" of a data (or information) governance program, it is also intended to supplement all other literature written about data governance. If you have a data governance program in place, but it is faltering, there is still plenty of advice in the following pages. In the following chapters, every attempt was made to keep the positions and processes disclosed as neutral as possible. In addition to a large amount of background, definitions, and preferred practices, this book will present a generic version of the steps and activities required to deploy data governance. Some case study examples and a few artifacts will help tie the process together. There are templates included in the appendices as well that serve as starting points for the various deliverables and artifacts that you may need to create, or as supplements for existing programs that may not have addressed all of the necessary factors required for success.
The content in this book represents what we have been doing in our practice over the years. That is why the pronoun "we" is used by the author. A lot of experience and refinement has gone into the material you are about to read. These processes are not the ramblings of one person as to what should be done. This material is battle-tested. Some of the material may vary from other published methods. Where this is the case, we try and point it out.
For example, Gwen Thomas of the Data Governance Institute has a defined data governance life cycle. It is focused on the entire life cycle, from learning about DG to selling the concept to implementing. We focus on implementation.
There are two intended audiences, and for this reason the book is assembled into two layers. The next three chapters (2 through 4) can be considered an executive overview, suitable for CIOs and other organizational leadership. The remainder of the book provides the details to move forward. In this way, a project manager can read the book from start to finish, but a senior leader will also find value by reading Chapters 1–4.
There is a secondary purpose to this book, which is to absolutely convince you, the reader, that data governance (DG or IG) is not a new kind of IT or technology project. In addition, DG is not an accumulative program—that is, if done correctly, you do not need to add an eternally funded requirement for manpower and capital. In fact, the perfect deployment of DG will result in nearly or absolutely no visible separate DG area. Therefore, while this book may seem to be a simple "how to," it is also unabashedly a treatise to convince organizations to think differently about how to manage their information and data universe. To be clear, real data governance requires that organizations act differently in regard to their use and management of content, meaning data, information, documents, media, et al. You implement data governance by overseeing the management of these instances of content, as well as projects and processes that create, use, and dispose of content.
This book does not distinguish between data governance and information governance, although some authors do. From a practical viewpoint, there is no real difference. We could conjure up some philosophical argument that there is a difference, but experience has shown these discussions only serve to confuse and reduce the effectiveness of the program.
Data governance is absolutely a mandatory requirement for success if an organization wants to achieve master data management, build business intelligence, improve data quality, or manage documents. However, DG is not an eternally lasting add-on process. This may seem contrary to much of the literature flying about the information industry at the time of this book's writing. There are many articles, for example, on how to design the DG "department," when you are really designing a framework to govern.
At the end of the day, we are modifying people's behaviors and business processes to think more clearly about the care and feeding of data. If we do this correctly, there is no need for large incremental groups of people implementing something brand new. Organizations love to jump on bandwagons and then bang on the "next big thing" until it surrenders. Frankly, this book is determined to prevent that. When it comes to data governance, the devil is in the mindset (as well as in the details).
As stated earlier, the next three chapters form an executive-oriented section. The purpose is to provide background, value proposition, and business relevance.
Chapter 2 will first establish a common vocabulary. The author's practice in this area has determined that the slightest variations in semantics can become huge obstacles. Therefore, we will present a set of terms and definitions as well as context. We will always provide the context of the term as well as refer to the definition. That way, if you read another version of a term like "policy," you at least have a frame of reference.
We will also stick to business terminology. If there is a technical aspect of a topic, it will be presented in business terms. If there is a business metaphor to lock in a point, it will be used in place of a technology metaphor.
Once we establish the terminology, we will cover the basic elements of the DG or IG program. We will present the core managerial and business concepts required for building and operating a DG program. Since DG is a business program, you may feel quite at home reviewing the various pieces and intersections of people, processes, and information technology.
Please thoughtfully read the text that addresses the scope of DG. One of the most critical errors that can be made while designing a DG program occurs when an organization has the initial conversation on scope and priorities. This examination also segues into a discussion on the business role of DG. The value proposition of DG needs to be clearly understood by executives if DG is to be successful. Finally, this part of the book is important because if data governance is misunderstood, it leads to a tendency to jam it into another box on the organization chart of the IT department, and this is often a fatal mistake.
The elements, scope, and business role sections are part of an overall segment that provides an overview of the entire DG program. It continues with a detailed examination of who should do the governing, what activities they need to perform, what is actually governed, and how DG looks when it occurs.
The first three chapters present an effective executive-level overview of deploying data governance so a CEO would have enough confidence to hand the book to a subordinate with instructions to develop a plan of attack. In essence, the first section of the book covers the higher levels of business thinking. If we were to view the realm of an enterprise's information architecture as a matrix representing the conceptual view through the physical, we might say the first few chapters address the top two levels of the matrix, or framework. In other words, we cover DG deployment from a conceptual and logical view. Figure 1-1 shows this.
The next few chapters address the middle layers from a level of orientation and understanding. Layer two starts with two chapters suitable for management as well. Chapter 4 talks about the value proposition of DG and Chapter 5 presents an overview of the process to deploy the DG program.
The start of the second layer (Chapter 4) starts with a topic that merits its own chapter, and that is the business case for DG. Very often clients will ask for assistance in developing a return on investment (ROI) for a DG program. In most organizations, the largest obstacle to starting DG is the selling—or a business case. This chapter will cover tangible and intangible business drivers for DG. Frankly, developing an ROI for a program like data governance is usually done to accommodate a lack of understanding, political posturing, or plain old resistance to anything perceived as "new." DG is not a "project" that will grant a traditional return. DG does add value, and stating this as part of a business case is about the best way there is to frame its value proposition. We will also leverage the chapter on the business case to learn how to identify the metrics we will use to sustain the DG program.
It is important to understand Chapter 5 and the context of the concepts from Chapter 2. If you want to dive into the list of tasks to get you from point A to point B (Chapters 6–13) go right ahead, but you will end up returning to Chapters 2 and 5 to figure out why you are being asked to do certain things at certain times.
Chapters 6–13 review the details of each phase of the process we use to deploy data governance. The activities, tasks, work products, and artifacts are reviewed. To the extent space permits, we present examples and ideas for how to actually execute the activities. Please understand at this point that a book like this can easily swell to 500 or more pages, so we need to strike a balance between education and writing a cookbook.
Please note that Chapters 12 and 13 focus heavily on managing the behavioral and organization changes required of DG. This is not a culture change management textbook, although reading one of those is advisable. We do delve heavily into those types of activities in the context of DG. Do not take them lightly. If you do not manage the changes associated with DG, you will fail.
Chapter 14 concludes our material with an overview of the technology for data governance, where we will cover what kind of technologiesdsuch as workflow, enterprise architecture, modeling, collaboration, content management, and othersdcan provide.
We summarize everything in Chapter 15. Under the mantra of "tell them what you are going to tell them, tell them, then tell them what you told them," we will cover a handful of mandatory takeaway concepts. In addition to the usual list of CSF-type bromides, you will find a lot of bullet points you can use for marketing and sustaining your data governance program.
It is our fervent hope you find value on starting and sustaining your data governance effort within these pages. If you already have one, we hope you find some good tidbits in here to give you some ideas and make your success sustainable. If you have any ideas or feedback, please visit the Wiki we have at www.makingeimworkforbusiness.com. Thank you for taking the time and energy to read this book.
CHAPTER 2
Definitions and concepts
Metaphors are hard to implement. —John Ladley
While this chapter is titled Definitions and concepts, it is much more than a glossary or repeat of DG bromides. We need to spend some time on the deeper concepts behind the terms that influence the processes to be presented. Also, rather than present a definition and just let it sit there, we will talk about how the term or concept fits into practical data governance practice. In addition, wherever a term or concept is being used in different ways in the real world, we will point out the differences. Either way, we will determine one definition of all the terms you will need to know to get through the remainder of the book. Data governance (DG) is part of a larger discipline that has traditionally been called enterprise information management (EIM). In fact, most confusion about the meaning of data governance stems from there being slightly differing views as to how it fits into information management.
Information management is commonly defined and understood as stated in the Data Management Body of Knowledge, or DMBOK, for short. The DMBOK labels data management as synonymous with information management. This is fine since we have taken the position that data, information, and content (documents, media, et al.) are all the same fodder for data governance. For the remainder of this book, information management, data management, and content management, as well as data governance, information governance, and content governance, all point to the same concepts and activities.
(Continues...)
Excerpted from Data Governance by John Ladley. Copyright © 2012 by Elsevier Inc.. Excerpted by permission of Elsevier Science.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
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Table of Contents
Preface 1. Introduction 2. Definition 3. Data Governance Program Overview 4. The Business Case 5. Components of DG 6. Scope 7. Assess 8. Align and business case 9. Functional design 10. Organization design 11. Deploy 12. Sustain 13. Executing DG – Practical Guide 14. Summary AppendicesWhat People are Saying About This
Everything you need to know to design, deploy, and sustain an effective data governance program!