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From “Searching” to “Finding”: How AI is Unlocking the Power of Unstructured Data

Unstructured data, which comprises almost 80% of any enterprise’s data, holds untapped value when it comes to addressing challenges and embracing opportunities. Extracting valuable insights from unstructured data sources has traditionally been difficult for businesses because it involves complex and time-consuming data analytics processes. However, with the help of natural language processing (NLP) and machine learning (ML), this is changing fast.

If businesses could take full advantage of the value this information holds as and when they need it, they would be able to resolve and even prevent issues more efficiently across the whole enterprise.

However, unlike structured data such as tables and spreadsheets, which have long been put to good use within enterprises, unstructured data is much more difficult to leverage and a lot harder to proactively analyze. Until recently.

In recent years, pragmatic AI—a collection of multiple technologies that encompasses deep learning, NLP, search, machine learning, intelligent sensors, and robotics has become a critical driver of enterprise evolution. It powers a variety of intelligent tools that are transforming the data supply chain for better insight discovery. Three prominent AI capabilities are driving a lion’s share of the change: Internet of Things (IoT), computer vision, and document understanding.

Of the three, document understanding is helping to make it possible for organizations to extract valuable insights from untapped, unstructured data sources. It combines NLP and ML to help gain insights into human-generated, natural language unstructured text. This should play an increasingly influential role in the future of enterprise transformation, as the number of unstructured sources within organizations will likely increase.

Natural Language Processing Gains Momentum

The practical applications of NLP make it an ideal tool for businesses looking to leverage insights from unstructured data to transform their operations.

As a result, NLP has been going through a transformation of its own, with two main factors driving this change.

First, as business users become more accustomed to search engines like Google and digital assistants in their personal lives, they begin to expect the same knowledge-seeking experience at work. This is fueling high-performing enterprise NLP applications that can understand and respond to natural language queries.

Second, NLP no longer relies on manually written rules alone. For greater automation, scalability and accuracy, NLP is now being paired with ML and enabling tools such as document understanding applications.

This shift also explains why the analysis of unstructured data is increasingly moving away from reactive searches and towards the proactive generation of insights to feed existing or anticipated business needs. Document understanding applications that combine NLP and ML deliver just that.

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