‍In our most recent webinar, our Automation Project Lead Simon Hochepied and Ruben Vermaercke, Managing Partner at Roborana’s automation competence center Caudata, explored the possibilities of intelligent data processing for organizations that have already embraced the available tools within the hyper automation toolbox. They discussed how this technology can help automate the structuring and analysis of unstructured and highly variable data, which makes up around 80% of most company data. This blogpost explores the benefits of natural language processing and intelligent document processing, two key components of intelligent data processing, and how they can help organizations optimize their data systems.

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Test and deploy

Many organizations struggle to structure and analyze unstructured data, which can be both labor-intensive and prone to errors. However, new technologies such as natural language processing and intelligent document processing offer a solution. Both technologies are key components of intelligent data processing (IDP). IDP allows organizations to automatically structure and analyze all incoming information, resulting in more standardized and connected processes. This, in turn, boosts the development of new digital transformation projects, which are key to surviving in the rapidly evolving tech landscape.

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To ensure a successful deployment of intelligent data processing, an in-depth process analysis is required to identify possible optimizations. Once the IDPis developed, it needs to be thoroughly trained using a labeled set of training data. When complete, IDP will generate a return on investment that requires minimal manual intervention.

Natural language processing

Natural language processing is a critical component of intelligent data processing. It involves natural language understanding and natural language generation, enabling computers to understand sentiments and intents of sentences, allowing for automated text classification, information extraction, answering questions, sentiment analysis, text summarization, and machine translation. Conversational AI, currently popularized by Alexa, Siri and Google Assistant, is an excellent example of natural language processing.

Intelligent document processing

The second component of intelligent data processing is intelligent document processing. This model is used to digitize, classify, interpret, validate, train and export documents automatically, using tools such as intelligent character recognition, automated document scanning and document generation. IDP can be implemented quickly to automate bills of lading, invoices, invoice management, order confirmations and order requests. Additionally, employees can spend their time completing more challenging and fulfilling tasks whilst the administration becomes more error-proof.

Increased efficiency and accuracy

Automating the processing of unstructured data can improve organizational efficiency by reducing the time and effort required for manual data entry and processing. This allows employees to focus on high-value tasks. Additionally, intelligent data processing can increase accuracy by eliminating errors and inconsistencies that may occur when humans manually process data. More accurate data can lead to better decision-making. Furthermore, organizations that process customer datamore quickly and accurately can provide faster and more personalized service, leading to increased customer satisfaction and loyalty.

Automating data processing can also save costs by reducing manual labor expenses and preventing costly errors. Finally, a more consistent and accurate procession of data enhances compliance with regulatory requirements and industry standards. Overall, intelligent data processing and intelligent document processing provides organizations with a range of benefits, including improved efficiency, accuracy, customer experience, cost savings and compliance.

IDP in practice

Intelligent data processing has various applications, including invoice processing, customer service, healthcare and legal document analysis. For instance, intelligent document processing can extract pertinent information from invoices, which is a time-saving approach for organizations that receive a large number of invoices from suppliers. Similarly, when customers contact an organization, intelligent data processing can extract their unstructured data, such as their name and issue description, and quickly route their inquiry to the relevant department or representative, resulting in a faster and more effective customer service.In the healthcare industry, intelligent data processing can be used to extract relevant information from clinical notes, test results, and medical images to improve patient care and outcomes. Lastly, in the legal field, intelligent document processing can extract essential information from legal documents, such as contracts and briefs, which saves time and reduces the effort required to review documents and ensure compliance.

Conclusion

Intelligent data processing offers highly efficient ways to structure the vast amount of unstructured data existing within organizations. Natural language processing and intelligent document processing are two critical components of intelligent data processing that can help organizations optimize their data systems. By automating the structuring and analysis of unstructured data, organizations can standardize and connect processes, develop new digital transformation projects, and generate a quick return on investment with minimal manual intervention.

Do you want to find out what intelligent data processing could mean for you and how we can get real value out of your data? Feel free to contact us!"