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Overview: Based on a combination of original research and practical use of data models in the field, usually under significant time pressure, the presenter has amassed a number of techniques for developing and checking the quality of data models. Models have been developed over the past 25 years in a wide range of industries including CPG, Manufacturing, Insurance, Media, Energy, Telecommunications, Service and Healthcare.
This Webinar introduces a toolkit of 5 particularly strong and practical techniques to help uncover errors and omissions in conceptual and logical data models. This helps you to improve the quality of those models, and reduce costly and embarrassing errors in the systems you build from them. It can dramatically improve communications between IT professionals and business professionals too helping to forge a common understanding in a project team, department or at an enterprise level. More recently, data models have found uses in domains such as business intelligence, data quality, and master data management where the definition of the data is absolutely critical to a successful outcome. The benefits of a common, shared understanding of data in these domains include shorter project times, reduced rework from errors and omissions and improved customer service and satisfaction. Compatible with technical approaches such as normalization, and SBVR, the techniques introduced in this webinar are non-technical and easy to apply focusing on how to highlight and correct errors and omissions, allowing you to use a data model as a communication vehicle for describing (and checking accuracy against) the real world.
Whether you have inherited a data model, developed one yourself, or are about to develop one and want to do a high quality job, these techniques will provide you with a set of "acid tests" that highlight errors and omissions. By applying the techniques you can assess whether your model is fit for purpose or not, and provide you with a checklist of modifications that you can make to improve it.
Why should you attend: Developed or inherited a data model? How do you know whether it is "right" or not? Do all your group have a common understanding of what the model means? Is the model in error, or missing key components? What will happen if you build a database from a poor quality model?
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Speaker Profile: Cliff Longman
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