Bigdata & Natural Language Processing(NLP)

Natural language processing (NLP) is increasingly discussed in social media and other verticals of businesses, but often in reference to different technologies such as speech recognition, computer-assisted coding (CAC), and analytics. NLP is an enabling technology that allows computers to derive meaning from human, or natural language input.

Media is data intensive from customer satisfaction, product reviews and business perspectives. While the industry’s transition to electronic data collection and storage in recent years has increased significantly, this has not actually forced physicians to code the majority of meaningful content. Eighty percent of meaningful data remains within the unstructured text, as it does in most industries. This means that it remains in a format that cannot be easily searched or accessed electronically.

NLP can be leveraged to drive and directly impacting on improvements in financial, production, and operational aspects of business workflows:

For financial processes, automating data extraction for claims, banking transactions, financial auditing, and revenue cycle analytics can impact the top line. NLP can automatically extract underlying data, making claims more efficient and offering the potential for revenue analytics.

                                   

For production processes, automatically extracting key quality measures existing products and customer reviews, reporting and analytics. NLP can infer whether a product meets a quality measure. prelaunch response from customers, so decide a product launching stategy.

For operational processes, descriptive and predictive modeling can support more effective and efficient operations. NLP can extract hundreds of data elements similar available product rather than the 2-4 available products, producing better models and supporting business insight.

So, NLP is a powerful enabling technology, but it is not an end user application. It is not speech recognition or revenue cycle management or analytics. It can, however, enable all of these.

There is a battle underway that is increasingly recognized in the business space. Individual business divisions seek turnkey solutions and frequently purchase NLP-enabled products. But at a broader level.

We can use natural language processing for customer sentimental analysis, customer segmentation and many of the business cases, and find out the customer response and satisfaction from similar available products in market and to maintain quality of already released product, to decide business strategy to be a different in market.