Enterprise IT teams everywhere struggle to manage the massive amounts of unstructured data stored across multiple platforms. This presents IT teams with a data management problem: the value of unstructured data may not be accessible or even known.
The focus historically has been on finding the lowest-cost data storage solution and not on unlocking value. To make this shift from managing volumes to delivering value, it’s important to identify, inspect, scrub, and sort the file objects before sending them to the destination analytics environment.
In sum, companies need a way to corral the data, so they can manage it appropriately. That’s where data management tagging comes in.
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What is Unstructured Data Management Tagging?
Tagging is the process of adding labels to categorize unstructured data, so users can easily search and find the data they need when they need it. Put simply, it’s adding and enriching the metadata on your data.
Industries such as Life Sciences have been early adopters of data tagging. For example, laboratory equipment such as microscopes will apply tags to images that identify the microscope that took the image, the project ID, and information about the subject. This allows the images to be discoverable and associated with a clinical study.
Tagging is similar to adding a hashtag to a social media post. For instance, a user writing about data management on LinkedIn would add the hashtag #DataManagement to help others searching for information about this topic.
When it comes to unstructured data management, tagging offers a number of benefits, including:
- Allows users to quickly and easily locate data with the precise characteristics they need.
- Improves the quality of unstructured data by making it more usable.
- Helps flag and filter questionable data before business leaders use that information in their decision-making.
- Helps identify personal identifiable information (PII), so businesses can properly manage, secure, and govern that data.
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Tagging can be done manually by employees as part of a workflow when content is created or ingested, or with machine learning tools that analyze the data based on specific parameters. For tagging to be effective, it must be applied consistently and accurately, a time-consuming process when done manually.
Fortunately, the process can be automated using machine learning. One of the advantages of using machine learning to automatically tag data is that it can be done 24/7, not just when employees are working. In addition, auto-tagging reduces the errors inherent in manual tagging.
Modern unstructured data management platforms provide a framework that allows users to identify datasets based on file attributes and metadata and then apply tags.
Examples of tags include project, owner, data type, cost center, business unit, and security classification as well as custom tags that fit more industry or customer-specific use cases. Think of it like an index or catalog across your storage silos, bringing structure to your unstructured data.
Applications such as artificial intelligence (AI), machine learning, and analytics tools can process data and apply tags based on the results. For example, an ML application may inspect images and then automatically apply a tag that categorizes the data. With tags in place, users and applications can now easily identify precise datasets.
Bringing Structure to Unstructured Data
While there’s no shortage of data in the enterprise today, IT organizations often don’t know what data they have and how they can use it to their advantage. That’s why with the massive growth of unstructured data, it’s critical to accurately tag and organize that data.
Tagging enables enterprises to operate more efficiently, minimize error, support unstructured data analytics projects, and enhance data governance, compliance, and security.
About the Author:
Steve Pruchniewski, director of product marketing at Komprise