Metadata tagging is a fundamental element of information management. It is the key to the proper classification of assets. Proper classification helps all the stakeholders—employees, clients, and prospects—access relevant assets quickly. Until recently, metadata tagging was an onerous task. The advent of artificial intelligence (AI) has brought in a welcome change to the process of metadata tagging. To begin with, it has reduced the time taken for performing the task from hours to minutes. This TechTarget article describes how AI aids metadata tagging.
Leveraging AI for metadata tagging involves two steps—analysis and action. For instance, in a video stream, speech-to-text algorithms transcribe the spoken words and image recognition algorithms, identify the words in a presentation and render them as metadata tags and values. These metadata tags and values are then synchronized with the video time codes. Now, when an action in the form of query is generated, the relevant snippet within the video stream is located automatically.
Similarly, in contract repositories, the meanings from the terms and conditions phrases are extracted using text analytics approaches and subsequently categorized with the help of relevant metadata. In fact, text analysis becomes a step in a workflow process, assembles the terms, and conditions for a new contract, based on the capabilities of the product being shipped automatically.
Smart algorithms, which constitute the basis for AI, generate metadata tags for digitizing routine tasks and augmenting human processes. In addition, the algorithms use the metadata as a computational mechanism for describing what an asset is about. Thereby, making aboutness the secret sauce for translating digital bits into intelligence and then action.
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