BEGIN:VCALENDAR VERSION:2.0 PRODID:-//hacksw/handcal//NONSGML v1.0//EN METHOD:PUBLISH BEGIN:VEVENT DTSTAMP:20260519T063840Z DESCRIPTION:Click for Latest Location Information: http://edwbootcamps.data versity.net/sessionPop.cfm?confid=145&proposalid=12571\nToday's present ations will take you through the specific components and considerations in creating your Data Architecture. \nData Modeling \nData Modeling is a primary means of achieving a better understanding of specific Data Arc hitecture components. Data Architecture is the sum of the various organizat ional data models. Both are made more useful by the other. Data models are literally the pages, intersecting Data Architecture and Data Modeling. Any time you are talking about architecture, it is important to include the com plementary role of engineering.\n\n Understanding the role played by models\n Incorporating the interrelated concepts of architecture/engineering\n What is taught: forward engineering with a goal of building\n What is also needed: reverse engineering with a goal of understanding\n How increasing coordination requirements increase design simplicity\n\nMeta data\nThe first step towards understanding data assets' impact on your organization is understanding what those assets mean for each other. Metada ta is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practic e. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques, supporting larger and more complex business initiatives. Program learning objectives include:\n\n Understanding how to leverage metadata practices in support of business str ategy\n Discussing foundational metadata concepts\n Exploring guiding principles for and lessons previously learned from metada ta and its practical uses applied strategy\n\nData Quality\nGood data is li ke good water: best served fresh, and ideally well-filtered. Data Managemen t strategies can produce tremendous procedural improvements and increased p rofit margins across the board, but only if the data being managed is &ldqu o;of sufficient quality.” This program provides a useful framework fo r guiding those approaching Data Quality challenges. Specifically, Data Qua lity must be approached as an engineering discipline. Data Quality engineer ing must be approached as a specific ROI-based discipline or it cannot effe ctively support business strategy. A better understanding of how to “ do Data Quality right” allows for speedy identification of business p roblems, the delineation between structural and practice-oriented defects i n Data Management, and proactive prevention of future issues. Program learn ing objectives include:\n\n Vivid demonstrations of how chronic business challenges for organizations a re often rooted in broader kinds of Data Quality that suggested treatments can address\n Helping you to understand foundational Data Quality concepts, guiding princ iples, best practices, and an improved approach to Data Quality at your org anization\n The basis of a number of specific case studies illustrating the hallmarks a nd benefits of Data Quality success\n\nReference and Master Data\nData tend s to pile up and can be rendered unusable or obsolete without careful maint enance processes. Reference and Master Data Management (MDM) has been a pop ular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it from a master/trans action perspective. This webinar presents MDM as a strategic approach to im proving and formalizing practices around those data items that provide cont ext for organizational transactions — its master data. Too often , MDM has been implemented technology-first and achieved the same very poor track record (1/3 succeeding on-time, within budget, achieving planned fun ctionality). MDM success depends on a coordinated approach involving typica lly Data Governance and Data Quality activities. Learning objectives includ e:\n\n Understanding foundational reference and MDM concepts\n Why they are an important component of your Data Architecture\n Awareness of Reference and MDM Frameworks and building blocks\n What consists of MDM guiding principles and best practices\n How to utilize Reference and MDM in support of business strategy\n\nLeverag ing Technologies\nOur architecturally solid stool requires three legs: peop le, process, and technologies. This section of the course looks at the most misunderstood of these three components: technology. While most organizati ons begin with technologies, it turns out that technologies are the last co mponent that should be considered. This discussion will survey a range of t echnologies that can be used to increase the productivity of Data Managemen t efforts. The goal is to invest in as little infrastructure as possible wh ile still achieving business/program objectives. The learning objectiv es include:\n\n Understanding technology considerations\n Appreciating the value of technologies, but keeping technology&nb sp;in perspective\n Automation technologies\n Glossaries, Catalogs, Repositories\n Profiling/discovery tools\n Cloud architecture\n\n DTSTART:20201022T083000 SUMMARY:Data Architecture Bootcamp (Day 2 of 2) DTEND:20201022T162959 LOCATION: See Description END:VEVENT END:VCALENDAR