Multi-Domain Master Data Management

Advanced MDM and Data Governance in Practice

Mark Allen, Dalton Cervo

• Editorial: Morgan Kaufmann
• Año: 2015
• ISBN:
  9780128011478
• Categoría: Informatica
• Categoría: Database

Multi-Domain Master Data Management delivers practical guidance and specific instruction to help guide planners and practitioners through the challenges of a multi-domain master data management (MDM) implementation. Authors Mark Allen and Dalton Cervo bring their expertise to you in the only reference you need to help your organization take master data management to the next level by incorporating it across multiple domains. Written in a business friendly style with sufficient program planning guidance, this book covers a comprehensive set of topics and advanced strategies centered on the key MDM disciplines of Data Governance, Data Stewardship, Data Quality Management, Metadata Management, and Data Integration.

• Provides a logical order toward planning, implementation, and ongoing management of multi-domain MDM from a program manager and data steward perspective.
• Provides detailed guidance, examples and illustrations for MDM practitioners to apply these insights to their strategies, plans, and processes.
• Covers advanced MDM strategy and instruction aimed at improving data quality management, lowering data maintenance costs, and reducing corporate risks by applying consistent enterprise-wide practices for the management and control of master data.

Part I

Planning Your Multi-Domain Initiative

Chapter 1

Strategy, Scope, and Approach

Abstract

This chapter discusses strategy, scope, and approach associated to planning an enterprisewide, multi-domain Master Data Management (MDM) program. It focuses on defining the guiding principles of data governance, data stewardship, data integration, data quality management, and metadata management to create scalable approaches that will drive a successful multi-domain initiative. Discuss the importance of establishing collaboration and alignment across data governance, project management, and information technology (IT).

Keywords

Master Data Management (MDM)

Program

Strategy

Scope

Approach

Top-down

Middle-out

Bottom-up

Planning

Discipline

This chapter covers the strategy, scope, and approach associated with planning an enterprisewide, multi-domain Master Data Management (MDM) program. Although companies are increasingly recognizing the value and need for MDM, they still often struggle with being able to fully or consistently implement MDM strategies and objectives for multiple domains or subject areas. They find this so difficult because they fail to recognize the many components, functions, and services that are either required to make MDM work correctly or are created as a consequence of an integrated set of disciplines and shared data. Many companies see MDM only as a data integration discipline, which is a mistake.

This chapter sets the foundation for MDM services, components, and disciplines by defining the guiding principles of data governance, data stewardship, data integration, data quality management, and metadata management to create scalable approaches that will drive a successful multi-domain initiative. It discusses where common approaches should be applied across the data domains, while recognizing and supporting flexibility where unique requirements, priorities, and deliverables can exist within each domain. It also covers the importance of establishing collaboration and alignment across program management, data governance, business units, and information technology (IT), all supported by a strong, high-level executive sponsorship.

Defining Multi-domain MDM

A data domain reflects the organization of key business entity areas such as customers, products, vendors, partners, service providers, financial accounts, patients, employees, and sites. Master data is data most critical to a company’s operations and analytics because of how it is shared and how it interacts with and provides context to transactional data. Master data is nontransactional in nature. Multi-domain MDM is concerned with managing master data across multiple domains. While some MDM functions and disciplines can and should be leveraged across multiple domains, some of those functions and disciplines within each domain are still specific enough or have distinct business requirements, so they need very specific management and implementation. Even technology use and maturity can vary widely across domains, as will be described in more detail in Chapter 3. This fact alone directly influences how companies have to adapt their MDM practices for different domains.

Imagine a small software company, with 10–20 employees, selling a single, very specialized application to a handful of customers using prepackaged software components from certain vendors. This company is unlikely to need MDM software or MDM automation to manage its master data. Why? It certainly knows its customers very well, and its managers have a clear understanding about what vendors they use and where to get what they need from them. They have their contacts clearly established. The multiple versions of their application can be managed by a good software configuration system. More important, chances are that they utilize a minimum set of internal software applications to maintain their operations, or even use a single cloud computing service such as Salesforce.com to support most of their IT functions. The bottom line is that the company’s volume, redundancy, and fragmentation of information are very low. Furthermore, business intelligence is simplistic. MDM automation would not add much value to this type of company.

Now imagine a large automotive finance company. This company does business with millions of prospects and customers, finances and leases millions of cars from multiple manufacturers, manages millions of financial accounts and a multitude of financial products, handles negotiations with thousands of dealerships and related contacts, maintains relationships with thousands of vendors and other firms (e.g., auction houses, appraisal companies, bankruptcy courts and trustees, and collection agencies), uses services of thousands of insurance companies, deals with thousands of attorneys representing either them or their customers when disputes occur, and hires thousands of employees. Just from these specifications, it is possible to identify a multitude of domains that such a company would have: prospects, customers, vehicles, manufacturers, financial accounts, financial products, dealers, contacts, vendors, attorneys, and employees. It is clear that the volume of information is large, and it is easy to figure out that the number of attributes and data systems maintaining all the information would probably be quite large. This company very likely can benefit from MDM because chances are that there is data redundancy and inconsistency across many data sources. But do all those domains need MDM?

There are many factors to consider when determining where to prioritize an MDM focus. Chapter 2 will detail how to identify and prioritize data domains, but let’s take a quick look at the key drivers that influence MDM decisions and priorities.

Figure 1.1 indicates the key factors that should drive the priorities and business case decisions for where MDM should be focused in a multi-domain model. However, regardless of the business case or the domain, there are key data management practices and disciplines that need to be scoped, planned for, and implemented in any MDM program. Each of the five major practices—data governance, data stewardship, data integration, data quality management, and metadata management—is important enough to be addressed by its own chapters in Part 2 of this book (Chapters 6 through 10, respectively). In addition, entity resolution, create-read-update-delete (CRUD) management, reference data management, data security, and data architecture are major topics and vital functions that also need to be covered within the overall MDM program strategy and scope. Let’s understand all these key areas before going any further.

Figure 1.1 Factors that determine what domains need MDM

Multi-domain MDM Strategy and Approach

Data is considered a strategic asset to a company, and it needs to be managed accordingly. Data supplies companies with the information and knowledge it needs to compete and thrive amid increasing pressure to succeed. Consequently, there is no dispute how valuable data is to a business. But the challenge is that the IT department has predominantly owned data management, and business organizations have not taken sufficient responsibility for data quality. Data is maintained within technology-enabled applications but created and changed by business processes, so any data issue requires as much of a business emphasis as it does a technical emphasis. Multi-domain MDM programs need an effective engagement and collaboration between business and IT. Collaboration is cultural, but it can be stimulated with proper organizational structure, robust communication channels, and an effective change management process.

Large companies experience huge data management challenges that emerge over the years as companies grow, constrict, acquire other companies, face new competitive challenges, transition from old system infrastructures to new platforms, and are subject to increasing requirements regarding security, information privacy, government regulations, and legal compliance. Because any of these conditions can be very disruptive, companies that can maintain a flexible and fluid dynamic between the business and IT roles will be most able to adapt quickly to address these challenges. The flexibility and adaptability needed here has to be an existing dynamic within specific roles and responsibilities and doesn’t just happen with initiating a new project or a consulting engagement.

This dynamic needs to be demonstrated by dedicated managers, data stewards, and data analysts working closely together across business and IT lines under data governance authority to address these data management challenges while also minimizing disruption to the normal operational practices. An MDM program will struggle to gain a successful foothold, where traditional business and IT dynamics create a very rigid engagement model, has a mostly reactive approach, and generally is only focused on back-end-oriented data management practices. A multi-domain MDM program needs to act as a bridging dynamic to create collaborative data management roles and responsibilities across business and IT functions. But this won’t happen overnight. MDM maturity and value grows over time.

MDM is not a project. Venturing into multi-domain MDM becomes a large exercise in Business Process Management (BPM) and Change Management. The planning and adoption of cross-functional disciplines and processes necessary for the support of MDM needs to be well orchestrated. This process can be greatly aided by leveraging a consulting partner well versed in MDM, particularly during the discovery and planning phases where capability needs, gap analysis, stakeholder assessment, and project planning deliverables need to be addressed and clearly articulated to a steering committee for review and approval.

With that said, Figure 1.2 is an example of the type of cross-domain model that must be carefully considered when creating your own model. It is also used to exemplify typical scenarios, but they must be adapted to each particular situation.

Figure 1.2 Multi-domain MDM—A cross-domain model

In Figure 1.2, the four boxes at the top represent the management organization functions required to support a successful multi-domain MDM model. Domains are represented by round boxes labeled “Domain 1,” “Domain 2,” and “Domain n.” Those constitute the data domains within the MDM program scope. The square boxes completely inside the rounded boxes are functions that are likely to require high specialization for a particular domain. In data synchronization, for example, one domain might require real-time synchronization among all sources, while another domain might meet business needs with nightly batch synchronization.

The vertical boxes crossing the domains represent functions that are likely to be generic and can be used by various types of master data. For example, a data governance program and charter can include scope and authority broader than just the master data focus. Similarly, data security is likely to be a cross-functional component focused on policies and management related to data access and data protection. The platform where the domain data exists is irrelevant, and data security disciplines are very reusable.

Every component will benefit from economies of scale. As MDM is applied across more domains, these functions and their associated tools and processes become more reusable or adaptable. Certain functions, such as data quality, are extremely broad. Most of the disciplines inside data quality are indeed reusable across multiple domains, but they still have domain-specific requirements for activities such as data profiling, data ma...