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Author: Sandeep Rangineni
Publisher: Xoffencerpublication
ISBN: 8119534654
Category : Computers
Languages : en
Pages : 252
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Book Description
Lacking coherence and ambiguity Product information drives up the cost of compliance, slows down the time it takes to bring a product to market, creates inefficiencies in the supply chain, and results in market penetration that is lower than anticipated. Lacking coherence and ambiguity in addition to obscuring revenue recognition, posing dangers, causing sales inefficiencies, leading to ill-advised marketing campaigns, and causing consumers to lose loyalty, consumer information. Due to the fact that the data from suppliers is inconsistent and fragmented, there is a greater likelihood of exceptions from suppliers, there is less efficiency in the supply chain, and there is a negative impact on the attempts to manage spending. "Product," "Customer," and "Supplier" are only few of the significant business entities that are included in Master Data. There are many more important business entities as well. Master data is the queen when it comes to the analytical and transactional operations that are necessary for the operation of a business. The purpose of Master Data Management (MDM), which is a collection of applications and technology that consolidates, cleans, and augments this data, is to achieve the aim of synchronizing this corporate master data with all of the applications, business processes, and analytical tools. As a direct result of this, operational efficiency, effective reporting, and decision-making that is founded on facts are all significantly improved. Over the course of the last several decades, the landscapes of information technology have seen the proliferation of a multitude of new systems, applications, and technologies. A significant number of data problems have surfaced as a consequence of this disconnected environment.
Author: Sandeep Rangineni
Publisher: Xoffencerpublication
ISBN: 8119534654
Category : Computers
Languages : en
Pages : 252
Get Book Here
Book Description
Lacking coherence and ambiguity Product information drives up the cost of compliance, slows down the time it takes to bring a product to market, creates inefficiencies in the supply chain, and results in market penetration that is lower than anticipated. Lacking coherence and ambiguity in addition to obscuring revenue recognition, posing dangers, causing sales inefficiencies, leading to ill-advised marketing campaigns, and causing consumers to lose loyalty, consumer information. Due to the fact that the data from suppliers is inconsistent and fragmented, there is a greater likelihood of exceptions from suppliers, there is less efficiency in the supply chain, and there is a negative impact on the attempts to manage spending. "Product," "Customer," and "Supplier" are only few of the significant business entities that are included in Master Data. There are many more important business entities as well. Master data is the queen when it comes to the analytical and transactional operations that are necessary for the operation of a business. The purpose of Master Data Management (MDM), which is a collection of applications and technology that consolidates, cleans, and augments this data, is to achieve the aim of synchronizing this corporate master data with all of the applications, business processes, and analytical tools. As a direct result of this, operational efficiency, effective reporting, and decision-making that is founded on facts are all significantly improved. Over the course of the last several decades, the landscapes of information technology have seen the proliferation of a multitude of new systems, applications, and technologies. A significant number of data problems have surfaced as a consequence of this disconnected environment.
Author: David Loshin
Publisher: Morgan Kaufmann
ISBN: 0080921213
Category : Computers
Languages : en
Pages : 301
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Book Description
The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to "master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure
Author: Dalton Cervo
Publisher: John Wiley & Sons
ISBN: 0470910550
Category : Business & Economics
Languages : en
Pages : 278
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Book Description
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.
Author: Allen Dreibelbis
Publisher: Pearson Education
ISBN: 0132704277
Category : Business & Economics
Languages : en
Pages : 833
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Book Description
The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration
Author: Dalton Cervo
Publisher: John Wiley & Sons
ISBN: 111808568X
Category : Business & Economics
Languages : en
Pages : 272
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Book Description
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.
Author: Mark Allen
Publisher: Morgan Kaufmann
ISBN: 0128011475
Category : Computers
Languages : en
Pages : 244
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Book Description
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.
Author: D. Hayn
Publisher: IOS Press
ISBN: 1614997594
Category : Health & Fitness
Languages : en
Pages : 412
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Book Description
Ineffective discharge management can jeopardize the successful completion of hospital treatment; but a well managed transition from hospital care to care at home depends on the efficient exchange of information with out-patient healthcare providers and professionals. This is just one way in which ICT can support healthcare and provide tools which help health professions to identify and communicate relevant data. Such tools will be increasingly important in future healthcare systems, and indeed a Europe-wide ICT infrastructure for information and data exchange may do much to revolutionize the quality of healthcare. It is therefore essential that infrastructures build on well-established standards such as Integrating the Healthcare Enterprise (IHE), even if this initially takes longer to implement. This book presents the proceedings of the annual Health Informatics meets eHealth conference, held in Vienna, Austria, in May 2017. The special topic chosen for eHealth2017 is Digital Insight – Information-Driven Health & Care, and the conference addressed the increasingly international focus of eHealth and the importance of cross-border health ICT. The papers presented here cover many eHealth topics, from maternity records to rehabilitation and from staff training to information exchange. Future ICT systems will inevitably involve machine learning and predictive analytics in order to provide actionable information to health professionals and support preventive healthcare concepts, and this book provides an insight into current research in health informatics and eHealth, addressing many issues central to the future of health and care. The book will be of interest to all healthcare researchers and practitioners.
Author: David Loshin
Publisher: Elsevier
ISBN: 0080920349
Category : Computers
Languages : en
Pages : 423
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Book Description
The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. - Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. - Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. - Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.
Author: Christiansen, Bryan
Publisher: IGI Global
ISBN: 1466696400
Category : Business & Economics
Languages : en
Pages : 718
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Book Description
Supply Chain Management (SCM) has always been an important aspect of an enterprise’s business model and an effective supply chain network is essential to remaining competitive in a global environment. By properly managing the flow of goods and services, businesses can operate more efficiently while managing most of the workload behind-the-scenes. The Handbook of Research on Global Supply Chain Management is an in-depth reference source that covers emerging issues and relevant applications of information pertaining to supply chain management from an international perspective. Featuring coverage on topics such as the global importance of SCMs to strategies for producing an effective supply chain, this comprehensive publication is an essential resource for academics and business professionals alike interested in uncovering managerial insight and logistics solutions.
Author: Soumendra Mohanty
Publisher: Apress
ISBN: 1430248734
Category : Computers
Languages : en
Pages : 311
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Book Description
Big Data Imperatives, focuses on resolving the key questions on everyone’s mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. This book addresses the following big data characteristics: Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible Petabytes/Exabytes of data Millions/billions of people providing/contributing to the context behind the data Flat schema's with few complex interrelationships Involves time-stamped events Made up of incomplete data Includes connections between data elements that must be probabilistically inferred Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability. Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.
