Master Data Management in Practice

Achieving True Customer MDM

Dalton Cervo, Mark Allen

• Publisher: Wiley
• Year: 2011
• Print ISBN: 9780470910559
• eBook ISBN: 9781118085684
• Edition: 1
• Topic: Business
• Subtopic: Decision Making
• Index: Business

In this book, authors Dalton Cervo and Mark Allen show you how to implement Master Data Management (MDM) within your business model to create a more quality controlled approach. Focusing on techniques that can improve data quality management, lower data maintenance costs, reduce corporate and compliance risks, and drive increased efficiency in customer data management practices, the book will guide you in successfully managing and maintaining your customer master data. You'll find the expert guidance you need, complete with tables, graphs, and charts, in planning, implementing, and managing MDM.

Part I
Planning Your Customer MDM Initiative

Chapter 1
Defining Your MDM Scope and Approach¹

Success is not final, failure is not fatal: It is the courage to continue that counts.

—Winston Churchill

MDM Approaches and Architectures

Master Data Management (MDM) is about bringing master data together to facilitate the employment of master data management services—such as data governance and stewardship; data quality, metadata, hierarchy, and overall data lifecycle management—and ultimately, to serve as the single source of truth for the business. Customer MDM focuses on the customer data domain in particular and its associated properties, such as company name, tax ID, addresses, contacts, accounts, and company hierarchy.

In addition to data domains, such as customers, products, partners, and suppliers, data inside a company can also be classified as operational or nonoperational. Operational data is the real-time collection of data in support of a company's needs in their daily activities. Nonoperational data is normally captured in a data warehouse on a less frequent basis and used for business intelligence (BI). This particular classification of data is relevant in this context because it can be used to distinguish most common MDM initiatives.

Although the very essence of implementing MDM is in the appliance and fine tuning of MDM practices to fit the enterprise architecture and business model, MDM implementations as a whole can generally be categorized into three major types of initiatives based on its primary focus being operational or nonoperational data:

1. Analytical MDM: address BI
2. Operational MDM: address business operations
3. Enterprise MDM: address both BI and operations

Each has a somewhat different objective and carries distinct levels of complexity, risk, and impact. Companies should perform detailed analysis to decide which approach is required. At a minimum, an MDM program must take into consideration business and IT requirements, time frame, resource availability, priority, and the size of the problem to be addressed.

Deciding which approach to implement is dependent on the business case, which is explained in more detail later in this chapter. Because each of the previous approaches targets a different category of information, they ultimately impact a company at varying degrees. Figure 1.1 depicts the level of intrusiveness of each MDM approach.

Figure 1.1 MDM Approaches

Operational data is inherently more critical to a company than nonoperational data due to its usability and timeliness. Therefore, analytical MDM is the least intrusive approach, followed by operational MDM and obviously the all-encompassing enterprise MDM, which is a combination of both analytical and operational MDM.

Naturally, more intrusive MDM projects involve both higher risks and higher likelihoods of disrupting companies' daily operations. It is important to notice that the figure does not suggest a sequence or phases to be adopted when implementing an MDM solution. As a matter of fact, phased deployments need to be observed from two different perspectives. One is concerned with progressing from one approach into another, such as starting with an operational MDM, then an analytical one to complete the enterprise solution. Another way to look at phased deployments is within a particular approach. It is not uncommon to start an operational MDM integrating just a few legacy systems, and slowly incorporate others. More about phased deployments will be discussed in Chapter 3.

Next, each of the approaches is explored further with the most common architectures employed for each of them. Keep in mind these are generic frameworks. MDM can be so encompassing and pervasive that the number of potential combinations can be many. Hybrid solutions are also very common. Finally, many subjects in the MDM arena don't have a universal terminology. What is called approaches and architectures in this book may be called styles, framework, or implementation in other books along with other varying definitions. What is important is to understand how the master data is integrated, used, maintained, improved, and governed.

Analytical MDM

Historically, analytical MDM has been the most commonly adopted MDM approach. This stems mostly from the relative simplicity of leveraging data warehouse projects. It is beyond the scope of this book to describe data warehouses in detail, but the following summary of the three primary data warehouse architectures might help you understand how MDM projects can benefit from this already existing integration:

1. Top-down. Major proponent is Bill Inmon. Primarily characterized by a data warehouse as a centralized and normalized repository for the entire enterprise, with dimensional data marts containing data needed for specific business processes. Up-front costs are normally higher and it takes longer initially until common structure is designed, built, and sources are integrated, but it is more adaptable afterward.
2. Bottom-up. Major proponent is Ralph Kimball. Data marts are first created to provide reporting and analytical capabilities for specific business processes, and can eventually be integrated to create a comprehensive data warehouse. Provides results quickly to each independent business unit, but overall data integration is potentially harder to achieve.
3. Hybrid. A combination of top-down and bottom-up approaches, characterized by a high-level normalized enterprise model, more quickly integrated with business specific data marts for faster results.

One may ask: If there is already a data warehouse integrating the data from across the enterprise, isn't that MDM? The answer is: not necessarily. It actually depends what is being done with that data. Bringing the data together is just one piece of MDM. The other piece is applying MDM practices, such as identity resolution; data cleansing, standardization, clustering, consolidation, enrichment, categorization, synchronization, and lineage; metadata management; governance; and data stewardship.

Bottom line is a data warehouse, and data mart infrastructure can work as the conduit to a much larger and encompassing MDM program. Conversely, the business intelligence, analytics, reports, and other outputs relying on the data warehouse and data marts will greatly benefit from the additional practices imposed by MDM—above all, data quality and hierarchy management improvements. Keep in mind that in this context, a strategic or a tactical BI implementation is implied instead of an operational BI since the underlying data is nonoperational.

Figure 1.2 depicts a common architecture adopted by companies implementing an analytical MDM approach.

Figure 1.2 Analytical MDM

Figure 1.2 shows that an extract, transform, load (ETL) process gathers data from disparate operational systems. Ultimately, the data is stored on an enterprise data warehouse (EDW). EDW and associated data marts become the source of master data for BI and analytics. Since EDW is now a single source from an analytical perspective, it is also the centerpiece for what can be called MDM services.

Analytical MDM is the quick-hit approach. While companies can quickly make a tremendous impact with respect to reporting and BI, with the analytical MDM approach relatively minimal inputs yield corresponding outputs. Specifically, companies fail to harvest the benefits of the MDM services back to their operational data. Remember, the data improvements are happening in the data warehouse, which is downstream from the operational systems. What's more, the analytical MDM approach does not enforce any regulatory or audit requirements since those are mandatory at the operational level.

Another drawback with this implementation is the possibility of adding one more fragmented and incomplete data system to the company. Obviously, the quality of the results will be directly related to the quality of the MDM services applied to the data. But a less obvious conclusion is the quality of the results is also directly related to the amount of data sources integrated. Certain lines of business (LOBs) are very sensitive about feeding data warehouses with their operational and strategic information, making it hard to achieve comprehensive integration.

On the other hand, it is possible for companies implementing an analytical MDM to influence the operational world. Analytical teams have access to an integrated view of the data and its underlying quality. They can recognize bad data and potential root-cause offending practices relatively quickly, as well as correlate discrepancies across LOBs. This is powerful knowledge that can be used by a strong data governance team to influence and improve data quality and business practices at the source. Be aware, however, that operational LOBs tend to be very resistant to this approach and to succeed with this practice, strong sponsorship from high-level executives is necessary.

Operational MDM

Operational MDM targets operational systems and data. It provides the opportunity to consolidate many, and ideally all, disparate operational data systems across the company, and become a true system of reference. This is obviously an enormous task. From a data integration perspective, the difficulty increases with the volume of data to be integrated along with the level of disparity among the systems to be combined. But it is much more than simply data integration. It is about business process integration and massive technological infrastructure change, which can impact virtually everyone in the compan...

Table of contents

• Cover
• Title Page
• Copyright
• Dedication
• Foreword
• Preface
• Acknowledgments
• Introduction
• Part I: Planning Your Customer MDM Initiative
• Part II: The Implementation Fundamentals
• Part III: Achieving A Steady State
• Part IV: Advanced Practices
• Recommended Reading
• About the Authors
• Index
• End User License Agreement