Let’s examine four data tagging best practices. Help flag and filter ethically dubious or otherwise questionable data before any of it is used in decision making or artificial intelligence solutions.Help identify sensitive personal data so access can be properly managed and governed.Improve big data quality, especially by making unstructured and semi-structured big data more usable.Enable efficient data discoverability – so when data is needed later for specific business purposes, it's quick and easy to locate the most applicable data.Help determine how much data preparation should be performed on new data sources.Within the context of enterprise data management, data tagging provides many benefits. We’ve all experienced disappointment when tagging was intentionally wrong and led us to click to supposedly related content – only to discover it was click-bait. Tags also play a big role in keyword searches and search engine optimization. Blog posts (like this one), online articles, videos, photos, podcasts and social media are all examples of unstructured or semi-structured data that rely heavily on tagging to connect them to related material.
Many people are familiar with tagging outside the context of enterprise data management. Data tagging is only one aspect of that – but it's a very important one.
The ability of disparate data to connect and combine (even when it’s co-located in the same data lake or cloud repository) is largely dependent on the metadata the data shares. This is why you should never overlook the important role metadata plays in the data ecosystem. While there’s no shortage of data available to your enterprise today, it’s often difficult to know what data you have and how it can be used. The more data you can apply to a business problem, the better its potential solutions.
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