A data taxonomy provides a framework to help end-users make informed decisions on the data within your organization. Having a well-defined data taxonomy can also improve search, discovery, and data categorization. It can mitigate risk by helping to know what data the is about before consuming it. Furthermore, a data taxonomy can offer consistent and reduced data management and increase insights to data types spread across multiple platforms. Despite its importance, many organizations do not have formalized guidelines for data classification.
It is never too early or late to create a data taxonomy that fits your organization. However, it would be ideal to follow some of the best practices while creating one. The first and most crucial idea to understand about creating a data taxonomy is that it is a company-wide strategy that will impact all users. Therefore, building a data taxonomy needs to be a collaborative effort amongst IT, Legal, HR, Executives, etc.
The second crucial task is knowing your data. You will need to discover where all data sources exist - from on-premises file shares, cloud repositories, or endpoints. After knowing what data exists within your environment, you can start to scope your taxonomy framework. Start broad and straightforward. Define data classifications that apply to the largest group of users possible.
While defining a formal data taxonomy framework is necessary for proper data classification, you cannot expect users to review the documentation each time they need to decide what to label a file or email. With daily use of classification labels, users will become more familiar with each type and their usage requirements. However, to help with adoption, you will want to provide easy to read clear and concise descriptions that are no more than 1-2 sentences. In this regard, an ideal framework provides classification categories that will apply to the 90th percentile of your user base.
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