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IBM Industry Models and IBM Master Data Management Positioning And IBM Analytics White paper IBM Industry Models and IBM Master Data Management Positioning and Deployment Patterns 2 IBM Industry Models and IBM Master Data Management Introduction Although the value of implementing Master Data Management (MDM) • The Joint Value Proposition summarizes the scenarios in which solutions is widely acknowledged, it is challenging for organizations IBM Industry Models and MDM accelerate projects and bring to realize the promised value. While there are many reasons for this, value. ranging from organizational alignment to siloed system architecture, • Positioning of IBM MDM and IBM Industry Models explains, by certain patterns have been proven to dramatically increase the success using the IBM reference architecture, where each product brings rates of successful projects. value, and how they work together. • IBM Industry Models and MDM deployment options describes The first of these patterns, which is not unique to MDM projects, is the different reasons for which IBM Industry Models are that the most successful projects are tightly aligned with a clear, concise implemented, and how IBM Industry Models are used together business value. Beyond this, MDM projects create challenges to an with IBM MDM in implementing a solution. organization along several lines. • A comparison of IBM Industry Models and MDM discusses the similarities and differences in structure and content of IBM • MDM is a business application that acts like infrastructure. Industry Models and IBM MDM. Organizations need to learn what this means to them and how they are going to adapt to it. This document is intended for any staff involved in planning for or • Although MDM projects might not start with data governance, implementing a joint IBM Industry Models and MDM initiative within they quickly encounter it. MDM projects provide focus on their organization, including IT Architects, Enterprise Architects and what aspects of governance are required for the project’s value Business Analysts. proposition. • Mastering data implies that there are business processes concerned with establishing and maintaining the quality of that data. • For MDM programs to be truly successful, they need to use a service-oriented architecture. IBM’s experience is that industry-specific models can help organizations with the last three points, once they have identified the specific value proposition to be delivered by the project. This white paper explains what MDM is, what IBM® Industry Models are, and how the combination of the two can significantly improve the targeted value realization and can reduce project durations and overall costs. This document contains the following: Figure 1. MDM strategic components IBM Analytics 3 IBM Master Data Management Overview By providing a single view of people, products, services and more, IBM InfoSphere® Master Data Management is the most complete, MDM enhances strategic decision making across an organization. The proven and powerful MDM solution with collaborative and operational quality of that data shapes the decisions that are made and ultimately capabilities. affects everything from customer relationships and regulatory compliance to business agility and competitiveness. Master data is the information about customers, products, materials, accounts and other entities that is critical to the operation of the • Service-oriented architecture delivers functionality through business. But companies hold pieces of master data in many different intelligent, pre-packaged web services that can be used to applications, such as enterprise resource planning (ERP) and customer seamlessly integrate MDM into existing business processes and relationship management (CRM) systems. Each of those source systems technical architectures. creates and holds the data in its own unique way. As a result, information • Pre-built and extensible data models for any domain are does not match from one system to the next. Critical data elements optimized for MDM; an organization can import existing data might be missing, duplicated or inconsistent. Furthermore, each models or build data models from scratch. department can operate only from within its own compartmentalized • Collaborative tasks allow workflows to be set up that reflect view. existing and new business processes, delivering a system that closely aligns with business practices. IBM InfoSphere MDM software manages the creation, maintenance, • Business process management capabilities enable the delivery and use of master data, both to ensure that it is consistent and implementation of policies and coordinate multi-step / multi-role trustworthy, and to make it possible to see the data in an organization- workflows for data stewardship and data governance on-premises, wide context. in the cloud, and bridging between on-premises and cloud. • Policy management ensures high quality master data using a quantitative, probabilistic approach to monitoring and enforcing policies. • InfoSphere Governance Center allows business users, data stewards, and IT teams to collaboratively improve master data quality by resolving data quality tasks and creating master data in compliance with corporate governance policies. • InfoSphere MDM Application Toolkit delivers business value rapidly with governance applications through pre-built blueprints and widgets for embedding within existing applications. • Common matching and search engine employs advanced statistical techniques to automatically resolve and manage data quality issues using probabilistic or deterministic options. Figure 2. Logical view of the MDM Hub 4 IBM Industry Models and IBM Master Data Management IBM InfoSphere MDM Standard and Advanced Editions (MDM IBM Industry Models Overview Operational Server) deliver single, trusted and complete versions of IBM Industry Models provide predefined data model industry content critical data assets and their relationships to applications and users to for banking, financial markets, telecommunications, healthcare, support efficient operational business processes and strategic decision insurance, retail and energy & utilities. The main components of the making. MDM helps clients in many industries share information data model are: across multiple systems to improve the services they provide to patients, citizens and customers. Business Terms Business terms define industry concepts in plain business language, with IBM InfoSphere MDM Collaborative Edition (MDM Collaboration no modeling or abstraction involved. Business terms have a standard set Server) manages master product data to maintain a single view of of properties and are organized by business category. Clearly defined product information for use throughout an organization and as part of a business terms help standardization and communication within an comprehensive MDM strategy. organization. IBM InfoSphere MDM Reference Data Management Hub (RDM) is a Supportive Content robust MDM solution for centralized management and distribution of Supportive Content represents data elements in the language of a given reference data across the enterprise. (Reference data is static data, such source requirement. For example, requirements such as Health Level as code tables, used to classify business entities – for example, salutation, 7 (HL7), which is the standard series of predefined data formats for gender, country code). It provides the governance, process, security and packaging and exchanging healthcare data in the form of messages that audit control for managing reference data as an enterprise standard, are transmitted between disparate IT systems, or BASEL, which defines resulting in fewer errors, reduced business risk and cost savings. the capital requirements for banks. IBM InfoSphere Big Match for Hadoop helps analyze large volumes of Analytical Requirements structured and unstructured data to derive deeper customer insight on a Analytical Requirements reflect the most common queries and analyses Big Data platform. for business performance measurement and reporting. They help with the rapid scoping and prototyping of data marts, which provide a subject-specific analytical layer in a data warehouse solution. IBM Analytics 5 Business Data Model (BDM) Dimensional Warehouse Model (DWM) The Business Data Model is the first point at which the various The Dimensional Warehouse Model is a logical model that is derived business requirements are brought together and modeled in an entity from the BDM and is an optimized data repository for supporting relationship format. It helps organizations to perform the initial analytical queries. This repository holds data to meet the needs of modeling of their business requirements and understand the various business-user-required analyses. constraints, relationships and structures that can be implied in their business requirements. Atomic Warehouse Model (AWM) The Atomic Warehouse Model is a logical, specialized model that is derived from the BDM. It is optimized as a data repository, which can hold long-term history, usually across the entire enterprise in a flexible manner. Figure 3. IBM Industry Model components 6 IBM Industry Models and IBM Master Data Management In the case of Banking, Financial Markets and Insurance, IBM Industry Analysis Process Model (APM) Process and Services Models are also available. This extended landscape The Analysis Process Model provides the initial Business specification consists of the following model components: of processes, which is used to underpin the initial analysis activities
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