How do you derive intelligence from unstructured data in real time?
When people think of data, most somehow tend to imagine text and numbers, neatly organized into tidy little rows and columns, perfectly formatted—i.e., the unconscious assumption is that all data is structured.
A recent study1 revealed that 80% of enterprise data is unstructured, and that number will continue to grow. This unstructured data includes emails, PDFs, documents, social media, videos, audio, images, and a big chunk of business-critical content.
Human-generated content tends to be unstructured, and differs significantly from machine-generated data, which is invariably structured. For example, the date and time of an email are structured data, whereas the email’s content itself is unstructured.
Unstructured data is growing at 55–65% annually2, which is a lot, and companies that are unable to harness this wealth of information are going to end up losing out. The need of the hour is for the right tools to make sense of this data.
Not too long ago, unstructured data used to be one of the biggest challenges to the growth of AI systems.
It boiled down to just one question — what do you consider unstructured? Does the data have to be spreadsheet-friendly? Are blogs, for example, structured or unstructured? Are images unstructured by default?
Structured vs. unstructured data
Structured data can provide a broad view of the subject, but unstructured data offers a more detailed understanding. However, uncovering insights from unstructured data used to be difficult, making it a costly transition for many businesses. Not anymore!
Today, those capable of leveraging unstructured data have a distinct competitive advantage.
They can uncover invaluable insights that may not be apparent in structured data, gain a deeper understanding of their customers, and identify emerging trends and patterns.
Thanks to the rapid advances in neural networks, in the space of a few years we have leapfrogged from recurrent neural networks (RNN) to convoluted neural networks (CNN) to transformers. Transformers can literally make sense of anything you throw at it – frankly, it’s almost scary!
Let us look at some of the use cases of unstructured data.
Unstructured data use cases
The top three things that come to mind are sales & marketing, product development, and customer insights.
⦁ Sales & Marketing
How do you determine customer purchasing trends and brand perception, besides analyzing sentiment?
How do you examine posts on social media, forum discussions, and other media? This can give you a lot of context.
How do businesses foresee customer needs? The sales team could use insights to upsell existing customers at the ideal time or make better product recommendations for customers.
All of these insights would come from unstructured data.
⦁ Product development
How do you improve your product or service through sentiment analysis of customer forums, customer service calls, and social media?
These insights would come from unstructured data.
⦁ Consumer insights
How do you do sentiment analysis of customers?
How do you make sense of problems and complaints by customers?
These can provide context to your R&D, product management, and customer experience teams on improving the product and service, besides adding specific features for specific customer categories.
Let’s look at how Intelligent Document Processing (IDP) can be used to derive intelligence from unstructured data.
IDP and unstructured data
95% of IDP uses transformers for pattern recognition & classification, text mining methods, document relevance analytics, sentiment analysis, and filter-driven web harvesting technologies to extract intelligence from unstructured data.
To understand why transformers are such a big deal, let’s first step back and look at ancient history — about 2 years ago — when AI meant RNN.
RNN basically looked at recurrent patterns. The main problems with RNN were its long time-to-train and short memory. In simpler terms, if you fed a very long sentence or paragraph, the RNN would not necessarily be able to figure out, e.g., the connection between the first sentence and the last, because its memory was too short.
Enter CNN, which was a big leap forward due to its superior performance with image, speech, or audio signal inputs. It began to transform fields that use these data formats extensively, e.g., marketing, healthcare, retail, automobiles, and so on. And this was before the advent of driverless cars! A CNN is a powerful tool but requires millions of labeled data points for training.
Then came the Transformers (not the movie!) but, yes, it’s practically in the realm of science fiction. Transformers use a coder/decoder block or input/output sequence to literally make sense of, well, almost anything. They are scalable, and can keep track of long-range context as compared to RNNs. CNNs have been replaced in most state-of-the-art visual language models by transformers.
Transformers help you extract relevant information from images, audio, video, and printed/handwritten text and convert it into a structured format such as tables and spreadsheets, which can be used for further analysis, insights, and decision-making.|
The capacity to store, query, and analyze data from any source will open up new business opportunities, leading to future success.
As user data becomes increasingly complex and the number of data sources multiplies, the additional context added by unstructured data will become more valuable.
Unstructured data will be the future, and tools like IDP will help you derive intelligent insights.
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