Established companies struggle to extract value from unstructured data like emails, presentations, videos, images, etc. Managing unstructured data isn’t really about storage and organization. It’s about making that content functional, or in more simple words, deriving value from it.
To make this data useful and meaningful, organizations have been relying on human interaction. Unfortunately, this type of handling is slow, expensive, and most importantly it is often a subject of errors.
Fortunately, the constant evolution of technology has brought technologies, which based on mimicking cognitive functions, could derive value from your massive amounts of unstructured content.
I’m talking about AI technologies.
Why Rely On AI Technologies?
The capability of machines to mimic cognitive functions can be a tremendous help for organizations that want to take the ECM mess and derive value from it.
Based on algorithms, AI technologies are capable of analyzing data, finding solutions to the problems analyzed, and making automated decisions about concrete outcomes. Technologies like these can easily make content more useful, workflows more efficient and interaction more productive.
To achieve the best possible results, AI technologies need clear data. Unfiltered data is the only reason why AI technologies may fail to derive value from large amounts of data.
AI technologies need large data volumes to learn effectively. These large data volumes are in fact, learning materials for machine-learning. Mode data for the learning stage means better conclusions at the end of the process. This is because AI technologies learn from examples and patterns that correspond to classifications provided by humans.
Let’s take a look at some ways in which AI technologies can help your organization derive value from your unstructured data.
6 Ways AI Can Help Your Organization Derive Value From Unstructured Data
1. Automatic Classification & Content Processing
Machines recognizing text is nothing new. We’ve used OCR for a very long time. But AI technologies are capable of much more than text recognition.
AI technologies are capable not only of recognizing the text, but also of “understanding” the text, classifying it accurately, and based on that classification, creating automated workflows.
And all of this without human intervention.
Learning from repeated exposure to documents and workflows used by people, AI technologies improve their identification and processing capabilities daily.
2. Data Extraction
AI technologies can be trained to extract data from documents much more efficiently than any other system. Over time, AI technologies even learn how to estimate the relevance of a new text in a given data extraction task.
I have heard in many conversations that companies find it easier and faster to do data extraction with AI, compared to the traditional way of people sifting through documents.
3. Document Clustering
These new technologies can easily group documents using the process known as ‘document clustering’. AI technologies can group documents together based solely on content without any previous classification.
This ability allows organizations to understand the similarities between documents and how they are related.
Many news aggregation services, like Google News, use this approach to group related articles into topics and categories.
Imagine what this could do for you in organizing your corporate emails and documents.
4. Advanced Security
AI technologies emphasize the importance of advanced security of enterprise content. These new technologies employ various ways of doing so. For instance, they are capable of detecting sensitive and personally identifiable information (PII) and mark these documents for special handling.
It’s not much of a quantum leap to then take this PII identified documents and move them to a more digitally secured location while each document waits its turn to be processed by a human.
Also, these technologies have developed so much so that they can completely secure data access. They use highly accurate and secure biometric techniques to identify employees that have permission to access particular data.
5. Data Analysis
Companies use ECM for storing and organizing their data. Applying AI to this will definitely help companies extract more value from their existing content. Even if it’s completely unstructured data.
Many cognitive platforms as a service (PaaS), such as IBM Watson, Microsoft Azure Cognitive Services, AWS etc., take Enterprise Content Management to a higher level. They apply AI techniques like machine learning, predictive analytics, and data visualization to improve decision making.
6. Entity Extraction
Based on simple sets, AI technologies can learn to tag documents. The ability of AI technologies to learn from repeated actions (machine or human based) enables them to extract various entities in a document, for instance:
- A person, etc.
The ability of AI to recognize if and when a word pair like Little Rock is a name of a place helps organizations extract more value from their unstructured data.
Let’s Wrap Things Up
Unstructured data is a problem for nearly any organization. Especially if you are a well-established enterprise that has decades of data. The struggles of dealing with this data are not around organizing and storing that content. It’s about making that content useful over and over again.
AI technologies, based on mimicking human intelligence, can now easily bring order and extract value from your data. These technologies can help you:
- Classify and process the content,
- Extract data and entities of the content,
- Cluster document without prior classification,
- Analyze data, and
- Provide security and privacy to all of your enterprise content.
If you have any thoughts on this matter, feel free to share them with us in the comments section below.
And don’t forget to share this article with your colleagues, so they can also learn more about AI technologies and how they can help in dealing with the ECM mess.