Pdf master data management

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. In an increasingly digital economy, mastering the quality of data is an increasingly vital yet still, in most organizations, a considerable task.

All the stakeholders in a company have a role to play and great benefit to derive from the overall goals here, but will invariably turn towards their IT department in search of the answers. However, the majority of IT systems that have been developed within businesses are overly complex, badly adapted, and in many cases obsolete; these systems have often become a source of data or process fragility for the business.

This book shows how company executives and IT managers can take these new challenges, as well as the advantages of using reference and master data management, into account in answering questions such as: Which data governance functions are available? How can IT be better aligned with business regulations?

What is the return on investment? How can we assess intangible IT assets and data? What are the principles of semantic modeling? What is the MDM technical architecture? In these ways they will be better able to deliver on their responsibilities to their organizations, and position them for growth and robust data management and integrity in the future.

The latest techniques for building a customer-focused enterprise environment "The authors have appreciated that MDM is a complex multidimensional area, and have set out to cover each of these dimensions in sufficient detail to provide adequate practical guidance to anyone implementing MDM.

While this necessarily makes the book rather long, it means that the authors achieve a comprehensive treatment of MDM that is lacking in previous works. Regain control of your master data and maintain a master-entity-centric enterprise data framework using the detailed information in this authoritative guide. Master Data Management and Data Governance, Second Edition provides up-to-date coverage of the most current architecture and technology views and system development and management methods.

Discover how to construct an MDM business case and roadmap, build accurate models, deploy data hubs, and implement layered security policies. Legacy system integration, cross-industry challenges, and regulatory compliance are also covered in this comprehensive volume. Plan and implement enterprise-scale MDM and Data Governance solutions Develop master data model Identify, match, and link master records for various domains through entity resolution Improve efficiency and maximize integration using SOA and Web services Ensure compliance with local, state, federal, and international regulations Handle security using authentication, authorization, roles, entitlements, and encryption Defend against identity theft, data compromise, spyware attack, and worm infection Synchronize components and test data quality and system performance.

Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals.

DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment.

Enterprises today understand the value of employing a master data management MDM solution for managing and governing mission critical information assets. Cloud computing introduces new considerations where enterprise IT architectures are extended beyond the corporate networks into the cloud. Many enterprises are now adopting turnkey business applications offered as software as a service SaaS solutions, such as customer relationship management CRM , payroll processing, human resource management, and many more.

However, in the context of MDM solutions, many organizations perceive risks in having these solutions deployed on the cloud. In some cases, organization are concerned with the legal restrictions of deploying solutions on the cloud, whereas in other cases organizations have policies and strategies in force that limit solution deployment on the cloud.

Immaterial of what all the cases might be, industry trends point to a prediction that many "extended enterprises" will keep MDM solutions on premises and will want its integrations with SaaS applications, specifically customer and asset domains. This trend puts a key focus on an important component in the solution construct, that is, the cloud integration middleware and how it fits with hybrid cloud architectures that span on premises and cloud services.

As this trend pans out, the on-premises MDM solution integration with SaaS applications will be the key pain point for the "extended enterprise. This book lays the background on how mastering and governance needs for SaaS applications is quite similar to what on-premises business applications would need. It draws the perspective for serving the on-premises application and the SaaS application with the same MDM hub. After reading this book, you will have a good understanding about the considerations for on-premises InfoSphere Master Data Management integration with SaaS applications in general and Salesforce.

The MDM practitioners and integration architects will understand the deployable integrations patterns and, in general, will be able to effectively contribute to delivering strategies that involve building solutions in this area. Additionally, SaaS vendors and customers looking to build or implement SaaS solutions that might require trusted master information will be able to use this compilation to ensure that the right architecture is put together and adhered to as a set of standard integrations patterns with all the core building blocks is essential for the longevity of a solution in this space.

An enterprise can gain differentiating value by aligning its master data management MDM and business process management BPM projects. This way, organizations can optimize their business performance through agile processes that empower decision makers with the trusted, single version of information. Many companies deploy MDM strategies as assurances that enterprise master data can be trusted and used in the business processes.

It examines how you can align them to enable trusted and accurate information to be used by business processes to optimize business performance and bring more agility to data stewardship. It also provides beginning guidance on these patterns and where cross-training efforts might focus. By reading this book, MDM or BPM architects can understand how to scope joint projects or to provide reasonable estimates of the effort.

They can also learn how to import data governance samples and extend them to enable collaborative stewardship of master data. It is intended to provide a overview of the subject with chapters covering key topics such as: the business case, data privacy, the challenges of global MDM, golden source and authoritative source explanations, the different MDM styles and the record matching process.

The back cover text says the following: " Master Data Management MDM for short has become a whole industry, within an industry. There are many companies now claiming to be MDM software or services providers. Everyone wants a master data project on their CV, and in general it has become hip and trendy to talk about and do.

The reality is that MDM is in fact the reincarnation of the problem of how to manage the consistency and integrity of the myriads of data assets that exist across the enterprise. This book provides an understanding of MDM, the business drivers behind it, the various techniques that are critical to its success and gives a good architectural grounding in the subject. Transform your business into a customer-centric enterprise Gain a complete and timely understanding of your customers using MDM-CDI and the real-world information contained in this comprehensive volume.

Master Data Management and Customer Data Integration for a Global Enterprise explains how to grow revenue, reduce administrative costs, and improve client retention by adopting a customer-focused business framework. Learn to build and use customer hubs and associated technologies, secure and protect confidential corporate and customer information, provide personalized services, and set up an effective data governance team.

Design and implement a dynamic MDM-CDI architecture that fits the needs of your business Implement MDM-CDI holistically as an integrated multi-disciplinary set of technologies, services, and processes Improve solution agility and flexibility using SOA and Web services Recognize customers and their relationships with the enterprise across channels and lines of business Ensure compliance with local, state, federal, and international regulations Deploy network, perimeter, platform, application, data, and user-level security Protect against identity and data theft, worm infection, and phishing and pharming scams Create an Enterprise Information Governance Group Perform development, QA, and business acceptance testing and data verification.

Master data management combines a set of processes and tools that defines and manages the non-transactional data entities of an organization. Master data management can provide processes for collecting, consolidating, persisting, and distributing this data throughout an organization. You can use it to gain control over business information by managing and maintaining a complete and accurate view of master data. You also can use InfoSphere MDM Server to extract maximum value from master data by centralizing multiple data domains.

InfoSphere MDM Server provides a comprehensive set of prebuilt business services that support a full range of master data management functionality.

It looks at the basics of a BI program, from the value of information and the mechanics of planning for success to data model infrastructure, data preparation, data analysis, integration, knowledge discovery, and the actual use of discovered knowledge. Organized into 21 chapters, this book begins with an overview of the kind of knowledge that can be exposed and exploited through the use of BI. It then proceeds with a discussion of information use in the context of how value is created within an organization, how BI can improve the ways of doing business, and organizational preparedness for exploiting the results of a BI program.

It also looks at some of the critical factors to be taken into account in the planning and execution of a successful BI program. In addition, the reader is introduced to considerations for developing the BI roadmap, the platforms for analysis such as data warehouses, and the concepts of business metadata. Other chapters focus on data preparation and data discovery, the business rules approach, and data mining techniques and predictive analytics.

Finally, emerging technologies such as text analytics and sentiment analysis are considered. This book will be valuable to data management and BI professionals, including senior and middle-level managers, Chief Information Officers and Chief Data Officers, senior business executives and business staff members, database or software engineers, and business analysts.

It explains the end-to-end process of an InfoSphere MDM Ref DM Hub implementation including the considerations of planning a reference data management project, requirements gathering and analysis, model design in detail, and integration considerations and scenarios. It then shows implementation examples and the ongoing administration tasks. But we can't succeed if we develop only one talk addressed to the 'average customer.

This is the best book that I have seen on the subject. Johnson Distinguished Professor of International Marketing Kellogg School of Management, Northwestern University "In this world of killer competition, hanging on to existing customers is critical to survival. This book offers sound advice to business people in search of innovative ways to bring data together about customers-their most important asset-while at the same time giving IT some practical tips for implementing CDI and MDM the right way.

No matter what your product, customers pay the bills. But the strategic importance of customer relationships hasn't brought companies much closer to a single, authoritative view of their customers. Written from both business and technicalperspectives, Customer Data Integration shows companies how to deliver an accurate, holistic, and long-term understanding of their customers through CDI.

Hierarchical Data: Data yang menyimpan hubungan antara Gambar 2. Global Architecture Master Data Management data lainnya. Data hierarkis kadang-kadang dianggap sebagai domain MDM super karena sangat penting untuk 2. Reference Data yaitu suatu tipe khusus data master yang Pada suatu rencana pelaksanaan project MDM senantiasa dikaitkan digunakan untuk mengkategorikan data lain, atau digunakan dengan beberapa kebutuhan, prioritas, ketersediaan sumber daya, untuk menghubungkan data dengan informasi di luar batas waktu, dan batasan masalah.

Menurut Roger Wolter, Haselden, dan perusahaan. Data referensi dapat dibagi di seluruh objek Kirk sebagian besar MDM project mecakup fase — fase berikut data master atau transaksional misal: negara, mata uang, : zona waktu, ketentuan pembayaran. Identifikasi data source dari data master. Master Data yang menggambarkan objek di sekitar bisnis Melakukan identifikasi terhadap referensi dari master data yang dilakukan.

Ini biasanya jarang berubah dan dapat yang berasal dari suatu aplikasi atau system tertentu. Pencarian data customer dari data master. Data Management harus memiliki karakteristik berikut : 3. Mengumpulkan dan menganalisis metadata untuk data 1. Model data yang fleksibel, dapat diperluas dan terbuka master business strategy. Melakukan analisis tentang metadata — metadata mana saja 2. Model data harus bersifat netral aplikasi apapun dan support yang akan menjadi kategori dimensi bisnis master data OLTP sources.

Kemampuan melakukan pengelolaan metadata. Penunjukan petugas data 4. Menerapkan program tata kelola data dan dewan data Kelola 5. Kemampuan menerapkan system data quality agar dapat Menetukan proses tata kelola bisa berbentuk workflow menemukan dan mengatasi data redundan.

Mendukung interface antar muka data quality untuk data master organization and roles. Mengembangkan model data master 7. Kemampuan melakukan trigger system untuk 7. Menentukan tools yang akan digunakan mensinkronisasikan perubahan data ke semua system yang Menentukan tools — tools yang akan digunakan untuk terhubung. Sistem keamanan data yang komprehensif untuk melakukan tools ETL, Tools HDFS storage environment, dan lain pengelolaan dan pemantauan data serta pembaharuan.

Antar muka yang user friendly terhadap user umum dan user 8. Mendesain infrastruktur system khusus. Mendesain system infrastruktur yang akan digunakan untuk Kemampuan melakukan migrasi data untuk memastikan mengakomodir seluruh aplikasi — aplikasi yang ada kedalam konsistensi data pada saat data bergerak melintasi aplikasi- Master Data Management Enabling Infrastructure.

Memproses dan melakukan uji test terhadap data master Mendukung platform intelejen bisnis untuk membuat profil, Melakukan uji test kevalidan , keakuratan, dan performa kepatuhan, audit, dan bisnis indicator kerja. Platform tunggaluntu mengelola semua objek data master Modifikasi untuk pembentukan dan penggunaan data master Kemampuan untuk menganalisis data master secara pada system. Dapat melakukan modifikasi pada saat pembentukan dan penggunaan data master Life Cycle.

Mengimplementasikan proses maintenance Menerapkan prose maintenance termasuk backup, restore , 3. Solusi 1. Group Keuangan yang besar dan memiliki banyak diantaranya: lokasi.

Mereka membantu klient secara personal dan komersial. Operasional MDM b. Dukungan penjualan yang tidak efisien, Berhubungan dengan pengolahan bisnis data operasional dikarenakan : perusahaan.

Kontak management yang tidak efektif, suatu 2. Karakteristik Solusi Penggunaan MDM penawaran tidak dapat dikomunikasikan kepada klien potensial Berdasarkan David Butler, agar suatu perusahaan berhasil mengelola d.

Manajemen promosi yang hilang, kesulitan dalam melacak pola pembelian pelanggan, retensi. Organisasi ingin beralih dari bisnis yang berpusat pada produk kepada bisnis yang berpusat pada Adapun solusi yang ditawarkan adalah pelanggan. Alat dan praktik MDM dipilih sebagai metodologi untuk menerapkan system management yang baru 2.

Gambar 3. Solution Architecture Master Data Management c. Proses bisnis berubah menjadi customer-centric. Mengaktifkan klien yang ditargetkan dan kampanye yang digerakkan oleh peristiwa untuk jenis klien tertentu. Arsitektur yang akan digunakan untuk menjawab permasalahan d.

Menyederhanakan operasi yang dihadapi klien. Platform data pelanggan dikelola dan terintegrasi dengan antarmuka data pelanggan terpadu. Peningkatan kualitas data dan alur kerja resolusi masalah data. Keterlibatan bisnis yang meningkat secara signifikan.

Kita teleh mengetahui Bersama bahwa MDM telah banyak dipelajari oleh banyak elemen tentunya dengan berbagai literatur juga. Kemudian studi kasus akan MDM di dunia nyata juga telah banyak 3. Pada makalah ini Sementara itu solusi proses bisnis yang ditawarkan adalah sebagai metode yang digunakan untuk mengukur teknologi MDM dapat berikut : meningkatkan keuntungan bisnis perbankan adalah dengan melakukan wawancara dan dengan menebar quisioner.

Kekwaletswe and Tshegofatso Lesole. Data Management.