There is research that says data scientists spend 80% of their time preprocessing data and only 20% on actually building machine learning models. This is not the best example of efficiency in an age where data-driven insights are more critical to competitive advantage than ever before. However, with the advent of AI-powered unstructured data processing tools, businesses can now transform raw, unstructured data into powerful strategic insights. Let’s take a look at three use cases where automated unstructured data processing is most suitable to apply.
There are few industries more synonymous with paperwork than car insurance. Now car insurers can use AI techniques to extract and analyze data from thousands of unstructured data sources. 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 body shops.
IAI-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 personally identifiable information that may appear.
Knowing your customers better than they know themselves is paramount to success in the highly competitive retail space. 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, 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, but 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 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.
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.
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. Unstructured data is also being used to help train the next generation of skilled workers through 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.