What is Unstructured Data Processing?

Mining Unstructured Data for Actionable Intelligence Using AI Techniques

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By: Elizabeth Mixson, Manish Rai 01/27/2022

Unstructured Data Defined

Drowning in data but starving for insights? If so, you’re far from alone.

Historically speaking, organizations have devoted most of their attention to structured data, digital information that adheres to a pre-defined data model or schema. Generally speaking, this data is:

Easy to store in a warehouse or database environment
Easy to access
Easy to search
Easy to analyze

For example, airline bookings, hotel reservations, payroll records and customer contact details stored within a CRM would all be considered structured data.

However, structured data only accounts for 10% or less of the 5 quintillion bytes of data produced globally per day. The rest is what’s known as unstructured data, information that is not arranged according to a predefined data model or schema. As such, it is not computable and cannot be stored or managed in a transactional system such as a data warehouse, payroll platform or CRM. It is untagged, unfindable and untapped.

Examples of Unstructured Data

Email text
Call transcripts
Photographs
PDF Documents
Social media posts
Geospatial
Live chat transcripts
Text files
Audio files
In-Ap Reviews
Video
Webpage content

On the surface, this information might seem, at best, useless or, at worst, a liability. But hidden within the vast amount of unstructured data generated every day lies tremendous, transformational value. From enabling the development of autonomous vehicles to building better product personalization engines to developing new medical treatments, the future of enterprise innovation is unstructured data. The challenge is tapping into that value.

Making Sense of Unstructured Data

Since the dawn of the digital age, companies have collected and struggled to manage vast amounts of unstructured data. The sheer volume and speed at which unstructured data is generated makes it difficult to control. As such, at this moment, unstructured data remains much more of a burden than a strategic asset for many companies.

Just think about your own email inbox. Perhaps you have an old employee agreement in there or a W2? Maybe an Excel sheet filled with customer contact information? What would happen if your inbox was hacked? Would the attackers walk away with a goldmine of PII and company secrets? With that in mind, it's no surprise that many refer to unstructured data as “PII in the wild.”

As unstructured data files tend to be quite large, high storage cost also tends to be an issue as is scalability (or lack thereof) of storage systems. Thankfully, advancements in NoSQL (non-relational) databases, data lakes, and object storage have made storing unstructured data easier and cheaper than ever before. However, this is only one piece of the puzzle.

Unstructured data is only valuable if it can be accessed and used. For this, you need the help of artificial intelligence (AI).

What is Automated Unstructured Data Processing (UDP)?

Research has found that data scientists spend 80% of their time preprocessing data and only 20% on actually building machine learning models. In an age where data-driven insights are more critical to competitive advantage than ever before, this is simply not acceptable.

However, with the advent of AI-powered unstructured data processing tools, businesses can now transform raw, unstructured data into powerful strategic insights.

For example, Intelligent Document Processing (IDP) systems use a combination of AI techniques such as computer vision and NLP to extract data from text-based documents, structure it, and put it back into a company’s enterprise system. Post-processing, the data is now machine readable and readily available for analysis.

Another common unstructured data processing tool is video content analysis (a.k.a. intelligent video analytics). Using advanced AI algorithms and computer vision, video analytics software can monitor, analyze and manage video inputs. Amongst other things, video analytics is a key component of facial recognition software.

Unstructured Data Processing AI Toolbox

Natural Language Processing (NLP)
Sentiment Analysis
Pattern Recognition
Speech-To-Text
Computer Vision
Intelligent Character Recognition (ICR)
Polygon Annotation
Voice Recognition
Named Entity Recognition

Unstructured Data Untapped: Use Cases

Automotive Insurance

There are few industries more synonymous with paperwork than car insurance. From insurance applications to underwriting documents to claims forms, for decades automotive insurance companies have been drowning in unstructured data.

Now car insurers can use AI techniques to extract and analyze data from thousands of unstructured data sources. Not only does this enable them to digitize and automate previously manual processes, the insights gleaned from this process can be used to enhance strategic decision making pertaining to pricing, risk management, customer profiling and so much more.

Another area of opportunity is AI-powered image recognition. Using AI methodologies such as computer vision and machine learning, car damage recognition programs can scan user submitted photographs to automatically detect and analyze vehicle damage. In addition to providing more accurate estimates, these solutions reduce claims cycle times by eliminating the need for the customers to meet with appraisers and obtain estimates from bodyshops.

Last but not least, AI-powered PII anonymization allows insurance companies (and other industries) to utilize cutting-edge video analytics and image recognition software to collect relevant information without compromising privacy. For example, let’s say an insurance company is reviewing footage of a car accident. To protect the privacy of the individuals inadvertently caught on tape, the PII anonymization tool will automatically blur or obfuscate the license plate numbers of passing cars, the faces of bystanders and any other potential personal identifiable information that may appear.

Retail

In the highly competitive retail space, knowing your customers better than they know themselves is paramount to success. By analyzing the unstructured data generated by in-store cameras, social media, customer chats, and other customer touchpoints, retailers can gain an in-depth understanding of what drives customer behavior. But that’s not all. Insights pulled from unstructured customer data can also be used to predict fashion trends, create personalized marketing experiences and develop new products.

For example, AI-enabled social listening tools enable companies to “listen in” on conversations surrounding their brand, customer base and industry trends.

Image-based personalization tools use computer vision to analyze, categorize and link together thousands of images per minute. Not only does this enable retailers to deliver hyper-personalized product recommendations, it also helps improve search performance by breaking down and properly labeling product features (i.e. color, style, fabric, material, value, price range, category, etc.)

Another big area of advancement is digital twin technology. Behind the scenes, retailers are using unstructured data to build supply chain simulations capable of modeling out various business scenarios. This helps them identify and address potential bottlenecks, supply shortages, and demand curves in real-time. In addition, unstructured data-powered digital twins of in-store environments are helping retailers perfect physical space layouts by enabling the modeling out of foot traffic, product displays and other in store features.

Manufacturing

Achieving success in the manufacturing industry is harder than ever and requires unprecedented levels of speed, efficiency and precision. Complicating matters is the fact that the COVID-19 pandemic - as well as the ensuing labor shortage - has brought industrial workplace health & safety to the forefront of everyone’s minds.

In order to achieve these objectives, companies are building “smart factories” powered by unstructured data. In these high-tech, interconnected environments, IoT devices constantly monitor and collect data on machinery (i.e. speed, temperature, pressure, vibration, etc.). With the help of machine learning, organizations can analyze unstructured machine data to identify and correct performance issues before equipment breaks down - a process known as predictive maintenance.

Similar to the retail industry, unstructured data is widely used to build digital twins of both individual products and the manufacturing environment itself. This allows engineers to prototype new product features, processes and scenarios digitally before trying them out in the real world.

Increasingly, unstructured data is also being used to enhance worker safety by helping organizations more effectively monitor and react to hazardous conditions. For example, computer vision and video analytics can be used to monitor worker behavior, alerting employees when they get too close to hazardous equipment or are breaking a safety-related rule.

Last but not least, unstructured data is also being used to help train the next generation of skilled workers through the enablement of AR/VR training. By incorporating digital elements into a live, direct or indirect view of a physical, real-world environment, augmented reality (AR) and virtual reality (VR) allows industrial workers (i.e. welders, heavy-duty equipment operators, electricians, etc.) to get hands-on training without risking their physical wellbeing.