Automating something is always better than having to do it manually, if the automating process is fully functional without any kind of error.
By using an automated process, you also have the benefit of uniformity, since tags will follow strict rules set by you and enforced by the system that processes everything. When done by humans, each person might look at the data in separate ways and tag it differently, or even just spell each tag in an unusual way.
Why is it important to collect and organize Metadata?
Metadata is essential for keeping track of crucial details about your data. This includes the who, what, where, when, and why. So, without efficient metadata management, it can be akin to having a library without a card catalog, leading to time-consuming searches even for known data and challenging quests for vaguely defined information. By prioritizing metadata, you gain the ability to leverage it for data catalogs, data lineage, data governance, and data search and discovery. The initial stage involves metadata collection, where you identify and extract metadata from your data systems.
Why “Automate” is better than “Manual”?!
In the past, when data creation and manipulation were limited, the number of metadata generated was relatively low, making manual metadata collection and management feasible.
However, in today’s era, enterprises accumulate vast amounts of data, measured in terabytes or even petabytes, with continuous data creation. As a result, manual metadata collection and management has become an overwhelming and never-ending endeavour.
How to automate metadata collection?
Automating metadata collection offers several advantages over manual methods. It saves time and effort, but it also ensures consistency and accuracy in tagging and organizing data. To automate metadata collection effectively there are some steps that need to be followed:
- Determine the types of information to be collected, such as: categories, keywords, authors, etc…).
- Finding a tool or software that automates the process.
- Use natural language processing to understand and analyse content.
- Train machine learning models to extract metadata patterns.
- Integrate the automated system with your existing content management system.
- Establish rules for tagging and metadata creation.
- Regularly review and update the system as content evolves.
- Evaluate the automated process with sample data and make improvements if needed.
By following these steps, time can be saved, and the accuracy of your metadata collection can be improved.
TML: Texter Machine Learning – Supercharge your content with AI!
Your content and data are the foundation upon which your business operates, and critical decisions are made. Recent advancements in AI in areas such as image and natural language processing have enabled a whole new level of automatic extraction of information and data analysis that power the automation of key business processes not possible until now.
Download here our TML – Texter Machine Learning – Datasheet:
By submitting you confirm that you have read and agreed with our Privacy Policy.
If you’re struggling with your digital transformation, remember… you are not alone in this… Texter Blue is here to help you providing the best results! Make sure you read our news and articles and contact us.