Unlocking Insights from Unstructured Data with NLP: A Conversation with Pavan Kumar Bandaru

Category

Blog

Author

Wissen Infotech Team

Date

February 20, 2025

Editor’s note: At Wissen, we help companies in BFSI, Manufacturing, Telecom, Retail, and Healthcare with their digital transformation journeys. We take pride in our team of senior leaders, who are also domain experts and technologists, for helping our clients solve business problems and achieve their business goals through digital transformation.

Pavan Kumar Bandaru is the Vice President and Chief of Data and Analytics at Wissen Infotech. 

Armed with a BE degree and MBA in Finance from Symbiosis Institute of Management Studies, Pavan Kumar Bandaru has over 25 years of experience in the IT industry. A multi-faceted technologist, Pavan specializes in Product Management, Cybersecurity, Data engineering, and Analytics. From BFSI to Manufacturing, Pharma, and Oil & Gas, Pavan has successfully delivered innovative and customer-centric products across various domains. Currently, he is driving the Data Analytics and Engineering practice at Wissen Infotech. 

We recently interviewed him to learn his perspective on unstructured data and how Natural Language Processing (NLP) and Generative AI can help enterprises extract insights from it. 

Join us as we gain valuable insights on data analytics from him.

The good and bad of Unstructured and Multi-lingual data

As information flows from various sources, such as social media, IoT devices, and audiovisual content, enterprises are overwhelmed with unstructured data. Pavan says unstructured data holds many valuable insights. He opines that combining it with structured data can help enterprises drive innovation and make informed decisions across various industries. 

While advanced technologies like Artificial Intelligence (AI), Machine Learning (ML), and natural language processing (NLP) can help enterprises extract insights from large, complex, unstructured datasets in real-time, the process is riddled with several challenges.

  • To begin with, unstructured data needs extensive cleaning as it is available in various formats. Enterprises need the right tools to transform, process, and analyze data.
  • Next, unlike structured data stored in a database and easily retrievable, unstructured data does not have a proper storage solution. Enterprises are currently grappling with this challenge. 
  • Enterprises need scalable infrastructure and technologies to process and analyze the large volume of unstructured data.
  • To add to the woes, the sensitive nature of unstructured data and the privacy and security challenges prevent enterprises from leveraging unstructured data for insights. 
  • Despite using natural language and advanced technologies like AI, enterprises would require additional human intervention to process data.

Similarly, enterprises face challenges processing multilingual data without large, high-quality datasets. Pavan says this can pose a challenge while training models and impact their performance. Multilingual models also need high computational power for training and inference, which enterprises lack.

So, how can enterprises solve these problems? The answer, Pavan says, lies in using NLP.

Why is NLP more effective than traditional data analysis?

Although traditional data analysis methods are commonly used, they primarily rely on regression analysis and descriptive statistics, which are better suited for structured data. Structured data can be stored in the storage layer in a retrievable format, making it easy for enterprises to use visualization tools to gather insights.

Pavan says that methods like NLP are more effective for processing unstructured data.

NLP processes and analyzes largely unstructured text data. It uses machine learning models, neural networks, and other techniques, such as sentiment analysis to understand the emotional tone of text data, and tokenization to break phrases into words. 

Pavan’s top recommended NLP techniques

When quizzed about the most effective NLP techniques for gathering insights, Pavan cited the following ones:

  • Named Entity Recognition (NER) to identify and classify entities such as names, dates, locations, and organizations within the text
  • Text summarization to create concise summaries of longer texts
  • Part-of-speech tagging (POS) to label every word in a sentence with the corresponding part of speech
  • Text classification to categorize text into pre-defined categories like spam
  • Transformer models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformers (GERT) to understand words through full stance context

Pavan suggests combining various NLP techniques to derive maximum value from unstructured data.

Pavan’s most recommended data analytics tools and platforms

Besides the aforementioned techniques, Pavan also recommended the following tools and platforms to analyze unstructured data and extract insights from large text data volumes.

  • Google Cloud Natural Language API for sentiment analysis, entity recognition, and content classification.
  • IBM Watson Natural Language Understanding for emotion detection, keyword extraction, and semantic analysis.
  • Microsoft Azure Text Analytics for sentiment analysis, key phrase extraction, language detection, and named entity recognition.
  • Amazon Comprehend to identify the language, extract key phrases, and perform sentiment analysis and entity recognition.
  • Hugging Face Transformers to pre-train models for various NLP tasks, including text classification, translation, and summarization.
  • spaCy to accelerate the speed of processing large text volumes.
  • Prodigy to build high-quality labeled datasets for training NLP models.

Pavan suggests choosing the ones that meet the business use case and needs. 

Pavan’s tips for ensuring data quality while applying NLP techniques

While NLP tools and techniques are helpful in processing and analyzing unstructured data sets, enterprises must take additional precautions to maintain the data quality by keeping away irrelevant data. Here are a few tips Pavan shared to maximize the quality of data:

  • Continuously monitor the dataset for irrelevant data. This is crucial because irrelevant data can reduce quality and lead to inaccurate insights and decision-making.
  • Cleanse the data using techniques such as tokenization, Stopword removal, and grouping similar words to ensure that only consistent and relevant data remains.
  • Implement a Data Quality Index to measure, assess, and reduce the linguistic and semantic anomalies in the data to improve its quality.
  • Use imputation or other strategies to eliminate incomplete sentences or missing values.
  • Pre-process text data to remove irrelevant content, such as punctuation and HTML tags.
  • Use filters to eliminate irrelevant text and transform the data’s quality.

Top strategies to scale NLP solutions

For enterprises that have large datasets and need scalable NLP solutions, Pavan suggests the following strategies:

  • Use distributed computing frameworks like Hadoop and Apache Spark to process large datasets across multiple nodes for efficient data handling and parallel processing.
  • Harness cloud-based platforms to scale infrastructure and resources based on demand.
  • Reduce NLP models' size without impacting performance using quantization techniques and eliminating unnecessary filters.
  • Break large datasets into small ones for easy processing and analysis.
  • Use advanced indexing to retrieve data quickly from large datasets.
  • Accelerate the training and inference of NLP models using graphics processing units (GPUs) and tensor processing units (TPUs).
  • Monitor the performance of NLP models and infrastructure to optimize resource usage.

Future of NLP integrations

When asked if NLP can be integrated with other technologies, such as speech recognition and computer vision, Pavan answered in the affirmative. 

He stated that multimodal AI, which combines speech recognition and computer vision, will enable enterprises to process and understand multiple forms of unstructured data simultaneously.

They will provide richer insights, enabling enterprises to create unified representations from text, images, and audio, gain contextually relevant insights, and improve user interactions. 

Role of Gen AI in gaining insights from unstructured data

Besides NLP, Pavan is also optimistic about using Gen AI to extract insights from unstructured data. He believes Gen AI models like GPT 3 and GPT 4 can understand and generate human-like text from unstructured data. It can process and integrate data from audio, images, and text, understand the context and nuances, and provide meaningful information that can enhance decision-making. The best part is that Gen AI can process data in real time, making it a perfect choice for making time-sensitive decisions. 

We hope you enjoyed reading Pavan Kumar Bandaru’s take on data cleansing processing and the use of NLP and Gen AI to structure unstructured data. You can follow him on LinkedIn