Gartner: Master Data Management Functionality

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MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.

Technology

MDM Functionality: You'll Need a Tightly Integrated Set of Capabilities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],MDM ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Reference matter data management: Two categories of structured data : Master data: is data associated with core business entities such as customer, product, asset, etc. Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail. Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .

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Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.

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Master data management (MDM) involves managing core business entities that are used across many business processes and systems. These entities include customers, products, suppliers, and more. MDM provides a single source of truth for key business data and ensures consistency. There are different domains of MDM, including customer data integration which manages party data, and product information management which manages product definitions. MDM systems can be used collaboratively to achieve agreement on topics, operationally as transaction systems, or for analytics on the managed data. Common implementation styles include registry, consolidation, transactional hub, and coexistence. MDM systems include repositories to store master data, services to manage it, and integration with other systems and applications.

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1) MDM is the process of creating a single point of reference for highly shared types of data like customers, products, and suppliers. It links multiple data sources to ensure consistent policies for accessing, updating, and routing exceptions for master data. 2) Successful MDM requires defining business needs, setting up governance roles, designing flexible platforms, and engaging lines of business in incremental programs. Common challenges include lack of clear business cases and roadmaps. 3) Key aspects of MDM include modeling shared data, managing data quality, enabling stewardship of data, and integrating/propagating master data to operational systems in real-time or batch processes.

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This document provides an overview of best practices in metadata management. It discusses what metadata is, why it is important, and how it adds context and definition to data. Metadata management is part of an overall data strategy. The document outlines different types of metadata and how it is used by various roles like developers, business people, auditors, and data architects. It discusses challenges like inconsistent metadata that can lead to issues. It also provides examples of metadata sources, architectural options, and how metadata enables capabilities like data lineage, impact analysis, and semantic relationships.

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Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information. While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.

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Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself. Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar. Bob will discuss the following subjects in this webinar: - Successful Data Governance relies on value from very important tools - What it means to govern your data catalog, business glossary, and data dictionary - Why governing the metadata in these tools is important - The roles necessary to govern these tools - Governance expected from metadata in catalogs, glossaries, and dictionaries

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This document discusses keys to successful master data management including process, governance, and architecture. It summarizes a survey finding that while many companies see data as an asset, only around 20% have implemented master data management. Successful MDM requires alignment with business objectives, clear governance models, and comprehensive solution architectures. The document advocates establishing policies, procedures, standards, governance, and tools to create and maintain high-quality shared reference data.

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This document discusses Digital Realty's implementation of a master data management (MDM) system. It provides an overview of MDM and why most projects fail. Digital Realty is succeeding by taking an agile approach with flexible multi-domain solutions. They leverage data virtualization and have identified data champions to manage master data domains like customers, products, facilities and people. The MDM implementation has provided benefits like improved data quality monitoring, faster integration of acquired companies, and ensuring compliance with data governance policies. Digital Realty is working to expand their MDM to additional transactional and dimensional master data entities.

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Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions: its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities. Learning objectives: - Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK) - Understand why these are an important component of your Data Architecture - Gain awareness of Reference and MDM Frameworks and building blocks - Know what MDM guiding principles consist of and best practices - Know how to utilize reference and MDM in support of business strategy

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Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data. Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.

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Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf

leebarnesutopia

So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation. In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.

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Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf