The use of AI, including machine learning for file and content analysis is in high demand and for good reason. Organizations deal with a large volume of data and content that is nearly impossible to deal with manually. But we can’t forget the intelligence that employees have that provide insights we can’t necessarily get from an algorithm. Any solution you use must offer both human curation and machine intelligence, and here’s why.
What AI Brings to File and Content Analysis
I probably don’t have to tell you the benefits of AI for content analysis. The volume of content stored across disparate information silos explains it all. There’s too much information for a person (or persons) to manually find, review, organize and analyze for insights. There’s also a lot of information stored in places your employees don’t even realize.
What you don’t want to do is spend hours searching through content that isn’t relevant to your needs, but you also don’t want to miss content that could have a meaningful impact on your analysis.
That’s where AI capabilities, like machine learning, help. Machine learning enables you to analyze large volumes and variety of data much faster than people can. It can identify patterns, perform auto-classification and provide auto-recommendations. It creates a model of your information by analyzing not only metadata but also the content itself. ML algorithms learn from the data it analyzes to improve its analysis, which means the more data it analyzes, the better it is at defining the relationship between datasets, classifying it and offering recommendations.
What Human Curation Brings to File and Content Analysis
We know that machine learning can do a lot to improve how we work with data and content. But we can’t forget the value that our employees offer. Self-service reporting and analysis tools give more employees the ability to find and work with data they need to make decisions. As they work with the data, they develop their own understanding of how valuable the data is, the relationships between datasets, when one dataset is better than another.
Employees also understand the data they work with well enough to enrich it. They can group and tag data, label it and add custom fields with additional information. They can also provide insights into how good the data is and how they have used it to help others. When employees provide their own details and insights to data we call it “human curation.”
Then there is the verification of what the machine does. Supervised machine learning is a type of machine learning where the algorithms improve as they receive feedback from humans. So as the algorithm does its work, it is reviewed by a person who confirms the analysis is correct or makes changes that the algorithm can then apply going forward.
It’s Not Machine vs Human; It’s Machine + Human Curation
In most situations, it’s not an “either/or” situation, but an “and”; both machine and human work together to provide the best file and content analysis. A machine can connect, index and analyze content, providing a first set of models, classifications, and recommendations, and a human can review, perform additional data enrichment and add comments and other feedback.
One of the biggest challenges with analyzing unstructured data is that it’s difficult to extract raw text concepts, entities, and other key information. That’s where machine learning can help, surfacing metadata and outlining the schema. Employees who understand the data best can then go in an enrich the metadata, add comments and other information that is then available to other users.
It’s important to remember that as data becomes ever more important in organizations today, it’s used by more departments, groups, and people; not just data analysts. Data is the lifeblood of successful companies, so it needs to be not only accurate but accessible to everyone in a way that’s easy to understand and work with. It needs both human curation and machine intelligence to do that.
You can learn how Everteam supports human curation and machine learning in file and content analytics here.