Benefits of artificial intelligence (AI) in records management
Outdated approaches are no match for AI and intelligent information management tools
Cloud-based information management tools are meant to help highly regulated organizations like yours improve productivity and streamline collaboration. But when it comes to accurately classifying records and content from these modern tools, traditional governance approaches often fall short.
At RecordPoint we are making significant investments in Research and Development to enhance our products with greater AI capabilities. Records management automation is the best way to address these challenges.
But what is automation, really? There are two main categories of automation to consider:
- Fingerprinting technology: Sample documents are provided to an application that represents the types of content in an organization. These are analyzed by the application to find common characteristics, which could include things like phrasing or formatting. These common characteristics are referred to as the document’s “fingerprints”.
- Linguistic analysis: When provided with sample documents the application extracts data and metadata from the samples. It then uses linguistic analytics to determine what records series should be applied to what content.
Seven types of automation
Within these two main categories, there are seven types of automation we typically deal with in the records management world. They can use fingerprinting, linguistic analysis, or both as methods of automation.
All of them help us to classify content correctly against the file plan, and in some cases, we can build relationships between content for event better classification. This also helps us to enhance search and retrieval of information. Collectively, these automation techniques are referred to as Artificial Intelligence (AI).
At RecordPoint we are making significant investments in Research and Development to enhance our products with greater AI capabilities. We have focused on the concepts below and how they apply to records management, information management, and information and data governance. This article explains the key approaches we are focusing on.
Automated classification is the application of categories, labels, tags, or metadata to content. This can be done using fingerprinting and/or linguistic analysis.
We can understand a lot about content by looking at fingerprints, such as who uploaded it, where they put it, and the document title. From this, we can often infer a classification.
Additionally, we can also look at the content inside the document using linguistic analysis techniques to classify it appropriately.
Machine Learning uses statistical techniques to give computers the ability to learn. In plain English, this means if you are editing a document with a colleague, the computer can infer that you have a stronger relationship with that person than someone who has never authored a document with you.
Once you track these relationships across multiple platforms and content, the computer can know a lot about your work preferences and behavior, creating a fingerprint that can be used in future cases.
This fingerprint can help us build relationships between documents for records management purposes and help reduce the number of classification errors by recognizing what content should be classified as a record.
It can also help us to group together like information, such as all content related to a certain customer across all content sources, which improves productivity and the collaboration experience, in addition to helping us to be more compliant.
Natural language processing
Natural language processing (NLP) is artificial intelligence concerned with the interactions between computers and human (natural) languages. It also looks at how to program computers to process enormous amounts of natural language data using linguistic analysis.
NLP includes a large group of automation tasks, but a few directly apply to records management. First, NLP can be used to identify terms and metadata that are relevant to the document, as if a person had manually read and chosen terms, rather than the terms that appear most frequency.
Second, optical character recognition (OCR) can recognize text in PDFs and classify them appropriately.
Third, given a chunk of text, NLP can identify the relationships among named entities. For example, it could pull the name of a person from the document and automatically look up what department they work in, even if the department is not mentioned in the document directly.
There are many more examples, but these are just a few.
Automated rules can perform repetitive actions on your behalf. They are triggered when certain criteria are met. For example, when a document is classified as a contract over $500,000 a retention schedule can be automatically applied.
Using fingerprinting and/or linguistic analysis we can automatically identify when the triggers occur and what rules should be used.
Black box automation is related to NLP and classification. It is another type of automation the identifies relationships between data and predicts the next data in a sequence.
For example, we can count how many times a word appears in a document (top-ranked words) or find relevant terms using linguistic analysis. We then would compare it to other similar documents to develop a fingerprint. When a future document matches that fingerprint, we can start to infer what metadata might apply to that document.
This is applied to records management to be able to identify the relationships between content and data, to ensure they are classified correctly, and the appropriate retention policy has been applied.
Neural networks improve performance on classification by looking at other examples where a category has been applied, like in fingerprinting.
For example, in image recognition, they might learn to identify images that contain a dog by analyzing example images that have been manually labeled as “dog” or “no dog” and using the results to identify dogs in other images.
Neural networks are another tool that helps us better classify content for records management purposes, so we are more confident in the classification and that the correct retention policy has been applied.
Deep learning is a type of machine learning. In this case, deep learning can use a hierarchy of concepts, such as a hierarchical file plan, to classify content.
For example, say your file plan hierarchy is Legal -> Contracts. In deep learning the document would first be identified as a legal document using fingerprinting and/or linguistic analysis, then it would only look at categories under legal to identify it is a contract. This can be repeated over hundreds of layers. In each case, the previous layers inform the next layer of classification.
Learn more about artificial intelligence automation
You no longer must choose between efficiency and classification accuracy, which is crucial for making informed business decisions and ensuring compliance. With a more sophisticated information management solution, you can manage structured and unstructured content and data in a scalable and sustainable way.
There are certainly a lot of artificial intelligence (AI) automation concepts that can apply to records management. The great news is that at RecordPoint, we’re doing the hard work for you so you automatically can benefit from these technologies by applying Records365’s layers of intelligence. Our goal is to make it easier for you to automatically identify records and classify content using classification intelligence.
Bringing consistency to your data management, no matter where the data is
Connectors provide the same high-value inventory and sensitive data identification to more and more data sources without the ongoing headache of integration maintenance and code-based customization
ML 101: How machine learning powers RecordPoint’s Classification Intelligence
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