Opportunities provided by data mining have been well documented over the past few years. State-of-the-art data mining tools and techniques have opened up new domains and created billion-dollar enterprises. However, the road to the holy grail of data exploration is not straight forward. With the change in user behavior during COVID-19, the complexity while delivering personalized products and services to users has become increasingly difficult. Consequently, organizations fail to bring value from the collected/unstructured data as they struggle to create pipelines for streamlining insights delivery. 

In a unique feature by Prabhod Sunkara, Co-founder and COO of nRoad.Inc., on the insideBIGDATA website, stated that the volume of unstructured data is set to grow from 33 zettabytes in 2018 to 175 billion terabytes by 2025. It is essential to draw value from unstructured data in such a scenario. There are many facets to processing and making effective decisions. The question is how. 

Unlike structured data systems, unstructured data applications need robust architecture to store and locate text and media files and content generated by user feedback, customer support, internal communication IoT (IoT) devices and more to simplify the process of gaining valuable insights. 

Gathering insights 

Today, ML algorithms are capable of detecting data in all kinds of formats. So. How can organizations missing out on rich insights of unstructured data apply these advanced methods? 

The accuracy of NLP and Image recognition tools has increased over the years in analyzing unstructured data. For example, while e-commerce companies use sentimental analysis to detect customers' modes from feedback, image recognition algorithms are used in finance for identity verification or even for reading doctors' prescriptions in pharmaceuticals.  

The role of NLP is significant in understanding unstructured data, like text and audio. One of the key techniques is sentiment analysis. 

Mentioned following are some steps that every organization should follow in leveraging sentimental analysis for tapping into unstructured data: 

• Deploy virtual agents or chatbots equipped with NLP and CV algorithms 
• The user interface takes inputs from the customers 
• The model is trained with keywords that are flagged as severe and mild 
• The grievance that contains a trigger word is automatically classified into severe or mild 
• The models can scan through customer inputs 
• Scanning through customer inputs generates assisting responses 
• Automated solutions decrease waiting time for customers and can be used to derive insights 

Preserving Privacy 

In sectors such as pharmaceuticals and finance, where regulation is high, it is necessary to derive insights without exposing users' identities. Here is when the rules and anonymization of data come into play. It is required to deploy robust privacy algorithms like federated learning and differential privacy while using users' data to avoid penalties by regulators. These solutions can be incorporated into an ML pipeline, enabling an efficient, compliant and robust data-driven model for any enterprise. 

With federated learning, organizations can avoid centralizing the training data to keep user information on low-end computing devices like mobile phones while still providing data-driven products and services. Retail users in today's world tend to use mobile phones to purchase products. Federated learning also comes with a secure aggregation protocol that implements cryptographic techniques to average model updates before decrypting to limit the tracking of individual users. 

Organizations should decide on the business objective to tackle the struggles with unstructured data. Then, they should all pick the right tools for the programming language, framework model and data storage. Finally, the model should be chosen based on the data that needs to be handled while leaving enough room for scaling.  

It is vital for the data engineering team dealing with unstructured data to be cautious of the results. Involving inter-disciplinary teams to work on solutions is the key to making the most out of unstructured data. 

Sources:

InsideBIGDATA

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