How to Tackle the Challenges and leverage the Opportunities of Unstructured Data

In today's data-intensive businesses, 80% to 90% of enterprise data is "unstructured". Images, videos, audio, and text and PDF documents are all examples of unstructured data. Processing this form of data can often be a time-consuming and costly task. These processes demand more resources and mostly involves manual work that is prone to errors.


The advent of Robotic Process Automation (RPA) has helped enterprises automate structured data sources; however, content-centric documents (unstructured data) cannot be automated using conventional rules-based solutions. Often companies respond by adding more human resources to it.

Today, enterprises are looking for solutions incorporating Artificial Intelligence (AI) elements to process unstructured data. The AI-based solutions often called Intelligent Automation (IA) solutions, possess computer vision, machine learning, and NLP capabilities that integrate within the workflow to provide an end-to-end automation experience.

Intelligent Automation solutions support technologies, including:

  • OCR: Technology that converts print or handwritten text into machine-encoded text. OCR can reduce and even eliminate manual labour. Using AI, and deep learning algorithms help achieve high operational efficiency. As a result, it can expedite backend workflows while freeing workers to take on more important responsibilities.

  • ML: These are pre-trained models that help classify and extract the data from unstructured sources. These can be further enhanced using deep learning algorithms.

  • NLP: With language detection, the extraction of unstructured data, and sentiment analysis, IA solutions extend the scope of automation to knowledge-based processes. They handle the automation of unstructured content (paper invoices) and interpret content and apply rules (unhappy social media posts). 

These capabilities have enabled RPA to work and perform more cognitive tasks and learn and improve in time, just like humans. For example, in the HR department, RPA can now process and scan CVs and select the candidates fit for a role. In the insurance industry, RPA can now process various claims and detect any discrepancies in the documents. In healthcare, during this ongoing COVID-19 crisis, RPA is used to automate the test results reporting and used to manage the timely delivery of urgent medical supplies to correct storerooms in hospitals.

Conclusion

Automating your business does not have to be difficult, no matter if you process structured or unstructured data. Since data is the new oil, then Intelligent Automation platform could be the refinery that you use to extract true value from it in a cost-effective and efficient way.