The incredible opportunities for mining data have been well documented over the past couple of 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 straightforward. And with the advent of changing user behavior due to the pandemic, the complexity while delivering personalized products and services to users has become increasingly strenuous. Consequently, organizations are failing to bring value from the collected/ unstructured data as they are struggling to create pipelines for streamlining insights delivery.
Data collected by enterprises for extracting insights can be anything — user engagement on websites, payslips, contract documents, MRI scans, mobile screen time, tweets, etc. Apart from the misread, missing, and uncategorized data, it can also be generated in formats such as PDF, PNG, and Excel. The challenge of unstructured data is so significant that it ends up exhausting resources and increasing costs.
Bill Inmon, the father of data warehouses, was famously quoted saying that many people spend their entire careers looking at structured data and not spending any time on unstructured data. Therefore, 98% of corporate decisions are made only on 10% of the data. In other words, organizations are ignoring the value of the majority of the data they collect, which is unstructured.
To extract value from unstructured data, there are many facets to process and make effective decisions. Unstructured data applications, unlike structured data systems, need robust architecture to store and locate text and media files, as well as content generated by user feedback, customer support, internal communication, Internet of Things (IoT) devices, and more, to simplify the process of gaining valuable insights.
How To Gather Rich Insights From Unstructured Data
Before the proliferation of advanced analytics and artificial intelligence, developers used to code the solutions for each new use case. Today, machine learning algorithms are capable of detecting data in all kinds of formats. So, how can organizations that are missing out on rich insights of unstructured data apply these advanced methods?
For instance, if you are building a chatbot that takes feedback and grievances from customers regarding a particular product, customers can share their feedback through various methods — emoji, text, and even images of damaged packages. Natural language processing (NLP) and image recognition tools have become accurate over the past five years in analysing unstructured data. While e-commerce companies use sentimental analysis to detect customers’ moods from feedback, image recognition algorithms are used in finance for identity verification or even reading prescriptions of doctors in pharmaceuticals, just by taking inputs from smartphone cameras.
NLP plays a crucial role in understanding unstructured data, like text and audio. One of the key techniques in NLP is sentiment analysis. At first, in sentimental analysis, tokenization is carried out to split text data into smaller parts. Various techniques, including Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN), also leverage tokenization to understand natural language. The token can further be split into subwords to simplify the text using methods like stemming and lemmatization. These techniques trim words to their simplest form to get the most common terms. After further processing text data to remove noise, a model is created to analyze the sentiments (negative, positive, or neutral).
Such models can not only help analyze customer feedback in various sectors but also in media firms where a company can explore its editorial standards by processing articles. Knowing emotions provide an in-depth understanding of where a company is heading to further improve products and services for its customers.
Let’s take a look at a step-by-step guide on how to leverage sentimental analysis for tapping into unstructured data from feedback to automate customer support services, which is essential for every organization:-
- Deploy virtual agents or chatbots that are 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
- Grievances that consist of trigger words are automatically categorized into severe or mild
- The model scans through customers inputs and generates assisting responses to guide users to solutions
- The automated solution finding module in the machine learning pipeline not only decreases the waiting time for customers but also can be used to derive insights and check patterns in customer behavior of specific issues
Preserving-Privacy While Processing Unstructured Data
In highly regulated sectors such as pharmaceuticals and finance, it is also necessary to derive insights without exposing users’ identities. This is where regulations and anonymization of data come into play. To ensure organisations comply with several regulations for privacy such as GDPR, CCPA, and more, there is a need for deploying robust privacy algorithms like federated learning and differential privacy while using users’ data to avoid penalties by regulators. These solutions can be incorporated into a machine learning 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 provide data-driven products and services. Primarily, in the retail sector, users today mostly use mobile phones to purchase products. Since every users’ requirements vary, organizations can use federated learning for hyper-personalization without exposing users’ privacy. 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.
Trying to pull user information from models has been a common practice by hackers through reverse engineering. Adversaries to machine learning models can reveal information, leading to privacy violations if not checked before shipping ML-based products and services in the market. This is where companies use differential privacy, adding randomized noise without significant change in the results to protect users’ privacy. Noise is added using Laplace and Gaussian mechanism, Exponential mechanism, and more. However, organizations require experts to assess which methods should be used based on the type of data being handled and processes being deployed.
Here are a few best practices for organizations that are struggling with unstructured data:
Decide on Business Objective
Identify processes that heavily rely on manual assistance to adopt advanced analytics solutions only where it is necessary.
Pick the Right Tools
Tools can be the type of programming language, framework, model, and data storage. Choose a model based on the kind of data that needs to be handled while leaving enough room for scaling the model when required. Scaling comes into play when the use cases involve both batch and real-time data generation. For example, the number of transactions increases tremendously on a Black Friday sale compared to any other day of the year. Sometimes a pipeline that relies on daily data collection might not be enough. Models might have to be deployed on the edge. This can lead to different kinds of unstructured data, which, in turn, can be used to grasp fresh insights.
Keep It Real
Artificial intelligence can lead to quarterly profits, but machine learning models are known to be back boxes. Consequently, 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 make the most out of unstructured data. This is where the domain experts can play a crucial role in tackling biases in the results and avoiding unethical and anti-regulatory practices while making profits.
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