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Data dictionary creator9/26/2023 ![]() the audience - data analysts, data modelers, and data scientists (high technical skill).the end goal - to ensure accurate, reliable, and trustworthy analysis of the Consumers Price Index to inform nationally significant decisions.In the context of this nationally significant dataset, the answers to the planning questions mentioned above might be: The data dictionary, published by Stats NZ, on the Consumers Price Index is a good example. In these situations, you may need to publish comprehensive data dictionaries that describe every detail of the data. when there are many datasets, about related and complex topics, that are hard to find.if there are regular updates, with new columns, and changes to old mathematical methods and sampling techniques.when there is a high risk and consequence of other's misunderstanding the dataset.complex datasets that include variable transformation. ![]() There are datasets or situations in which basic data dictionaries just aren't enough, for instance: Motor vehicle registry open data dictionary Comprehensive data dictionaryīasic data dictionaries are good in many situations. the user need - a description of the columns, the units related to data in columns, the codes used and their meaning.the audience - data analysts (medium to high technical skill).the end goal - analysts confidently and reliably use the motor vehicle registry data.Their answers to the planning questions mentioned above might be: The motor vehicle registry open data dictionary, published by Waka Kotahi - NZTA, is a good example of a basic data dictionary. In that dictionary, you might include a description of the data, a definition of the column headers, and the codes used as values in the columns. In these situations, you may only need a basic data dictionary. The columns or values in your data could be hard to understand, but the data could be easy for your audience to find. the user need - no extra information other than that already provided in the dataset.the audience - the wider public (low technical skill) to software developers (higher technical skill).the end goal - data about DOC hut locations are used by others.The data about DOC huts published by the Department of Conservation is a good example. For instance, columns or content may obvious to those that want to use the data. In these cases, there is no need for a data dictionary. Some data doesn’t need detailed information to make it findable and useable. These levels have been made up by us for the purpose of showing you how different aims, audience needs, and data complexities can require different levels of detail in your data dictionary. We have divided our examples into three levels: no data dictionary, basic, and comprehensive. The answers to those questions will help you decide on the level of detail that you will need to include in your data dictionary. the user need - what do they need to know about your data to use it appropriately?.the audience - who is going to use your data?.the end goal - what are you trying to achieve?.Explore examples of data dictionaries published by other government organisations.īefore you go about making a data dictionary for each specific dataset, you have a few things to think about: ![]() Learn about the decisions you need to make before creating a data dictionary and the tools that might help.
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