Predictive Metadata in Digital Asset Management

Predictive metadata with artificial intelligence (AI) is a game changer in digital asset management. By automatically generating metadata for digital assets, such as images, videos, and documents, predictive metadata can help organisations to improve their efficiency, save time and money, and gain valuable insights into their digital assets.

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Key Benefits of Predictive Metadata

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One of the key benefits of predictive metadata is its ability to automate the process of tagging digital assets. Tagging assets manually can be a time-consuming and error-prone task, especially when dealing with large volumes of digital assets. Predictive metadata can help to alleviate this problem by using AI algorithms to analyse the content of an asset and automatically generate relevant tags.

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This tagging can include information identified from technologies such as object recognition, facial recognition, text extraction, and natural language processing and can be especially beneficial for businesses in retail, media and entertainment, healthcare, and more where a large amount of assets are created and used on a daily basis.

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Standardisation of Metadata Tags

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Additionally, the use of predictive metadata can help to standardise the way assets are tagged and make the process more consistent across an organisation. Without a standardised process, different teams and departments within an organisation may use different terminology and tagging conventions, making it difficult to search for and locate assets. This can lead to confusion, inefficiency, and wasted time. Predictive metadata with AI can help to standardise the way assets are tagged by using algorithms that are trained to recognize and classify assets using a consistent set of terms and conventions. This can help to ensure that all assets are tagged in a uniform way, making it easier for users to find the assets they need, regardless of which team or department created them.

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Improved Searchability

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Another benefit of predictive metadata is its ability to improve searchability. With the growing volume of digital assets, it can be difficult to find the specific asset you need when you need it. Predictive metadata can help to improve the accuracy and speed of search results by providing more detailed information about the assets. For example, if you search for images of "beaches", an image recognition algorithm can scan all the images in the system and return only the images that match the beach criteria. This can save time and increase efficiency by making it easy to find the assets that are needed.

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Predictive Metadata with Artificial Intelligence

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Predictive metadata with AI can also provide valuable insights into the usage and performance of digital assets. By analysing the metadata associated with an asset, organisations can gain a better understanding of which assets are being used most frequently, which assets are underperforming, and how assets are being used across different teams and departments. This information can help organisations to optimise their asset management strategies and make more informed decisions about which assets to invest in, which assets to retire, and how to improve the performance of their digital assets.

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Enhanced Security of Digital Assets

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In addition to these benefits, predictive metadata with AI can also enhance the security of digital assets by detecting and flagging potential risks. For example, an image recognition algorithm can scan images for sensitive information such as personal identification numbers, credit card numbers, and other sensitive data. This can help organisations to prevent data breaches and protect sensitive information.

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Conclusion

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Overall, predictive metadata with AI is a powerful tool for digital asset management. It can help organisations to improve their efficiency, save time and money, gain valuable insights, and enhance the security of their digital assets. With the growing volume of digital assets, predictive metadata is becoming an essential part of any organisation's digital asset management strategy.

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If you would like to learn more about how we use predictive metadata in the HIVO platform, please get in touch and our team would be happy to provide you with a demo.