Unleashing the Potential of Unstructured Data with Automation

May 23, 2023

DIGITAL TRANSFORMATION | 9 min READ

Exponential growth of Unstructured Data

Unstructured data has remained an untamable beast that has frustrated enterprises globally. They evade storage in traditional row-column databases as well as on spreadsheets. Unstructured data is difficult to search and almost impossible to analyze. The growth of such data brings in challenges and create issues associated with data silos, processing, compliance, and security. Yet unstructured data can’t be ignored. This is, in part, because unstructured data is everywhere – from text, image, audio, and video files, to sensor data and web pages. More importantly, it contains a trove of information that can drive true value for insightful decision-making.

Harshad Parmar

Practice Head

Customer Experience & Automation

Birlasoft

Rishu Sharma

Practice Director

Digital Evangelist and Storyteller, Digital BU

Birlasoft

The massive latent potential of unstructured data

Organizations can’t turn a blind eye to the massive untapped potential in the market for unstructured data. They must invest in developing their AI ecosystem which involves Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Robotics, Expert Systems, Fuzzy Logic, Neural Networks, and Computer Vision (CV). Only then, can they process the enormous amounts of unstructured data in their database, derive relevant sense, and make far-sighted decisions.

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Take customer analytics, for instance. It leverages tools to analyze social media usage, the kind of pages being visited, online product reviews, and conversations with chatbots. Then, using AI, it derives customer insights in the form of repetitive patterns that help enterprises enhance the customers’ experience.

Challenges in automating unstructured data

Using Deep Learning, a subset of AI, is the first step to overcoming challenges posed by unstructured data. In this approach, Deep Learning acts as the brain. It receives data in unstructured form, analyses it using various tools such as CV and NLP, and then gives the data a structured form that may be automated and used to benefit in different ways.

Complete integration of the AI ecosystem will help enterprises overcome all the challenges of unstructured data since the machine can be trained explicitly on handling data, and the level of moderation that needs to be taken care of while processing such data.

Document processing issues can be overcome using robots capable of processing documents automatically, combining various technologies suitable for a wide range of documents irrespective of the data structure, volume, or quality. This reduces time and costs significantly and ensures accurate and fast data extraction with end-to-end AI-powered solutions.

The results of automating data processing are plainly visible in accelerated productivity, higher efficiency, better customer experience, and more motivated employees.

Automation of unstructured data for multiple business functions

Now that we have understood unstructured data, the challenges they pose, and ways of overcoming them, let’s find out how unstructured data can be automated and leveraged to efficiently run business functions ranging from the supply chain, finance, legal and customer service to HR and marketing and sales.

Supply Chain evolution with automation

The rise of automation isn’t hidden from enterprises, nor is the fact that unstructured data volumes are surging. Supply-chain enterprises can utilize these trends in the following sub-functions of task automation, strategic sourcing, and contract management and achieve enhanced efficiency.

Task automation with unstructured data in the supply chain uses technology to automate repetitive and manual tasks that involve processing unstructured data in the supply chain. Unstructured data includes information that cannot be easily organized into a table or database, such as images, videos, audio files, and free-text documents.

Examples of task automation with unstructured data in the supply chain include:

Automated data extraction from invoices, purchase orders, and other unstructured documents.
Automated categorization and classification of unstructured data, such as product descriptions or shipment details.
Automated data entry into supply chain management systems, reducing the risk of errors and increasing efficiency.
Automated image recognition and analysis, such as detecting damage to goods during shipment.

Strategic sourcing with unstructured data automation in the supply chain uses technology to automate the processing of unstructured data as part of the strategic sourcing process. Strategic sourcing is the procurement process of identifying, evaluating, and selecting suppliers based on criteria such as cost, quality, and delivery time.

Examples of strategic sourcing with unstructured data automation in the supply chain include:

Automated data extraction from supplier proposals, contracts, and other unstructured documents, reducing the time and effort needed to evaluate suppliers.
Automated categorization and analysis of unstructured data, such as supplier ratings and reviews, to support supplier selection decisions.
Automated risk assessment and monitoring using unstructured data, such as news articles and financial reports, to assess the stability and reliability of suppliers.
Automated contract management, including creating and tracking contracts, reducing the risk of errors, and improving the speed of contract execution.
Automated image recognition and analysis of supplier facilities, allowing organizations to assess their operations and make informed sourcing decisions.

Contract management with unstructured data automation in the supply chain makes use of technology to automate the processing of unstructured data in the contract management process, helping organizations to manage contracts with suppliers more effectively.

Examples of how unstructured data automation can be used in contract management include the following:

Automated data extraction from contracts, purchase orders, and other unstructured documents to streamline the contract management process.
Automated classification of contract terms, such as delivery dates and payment terms, to ensure compliance and reduce the risk of disputes.
Automated data entry into contract management systems, allowing organizations to track contract status, deadlines, and other important information.
Automated image recognition and analysis of signed contracts, reducing the risk of errors and improving the accuracy of contract management.

Task automation, strategic sourcing, and contract management with unstructured data automation improve the efficiency, accuracy, and security of processes, leading to improved supplier relationships, reduced costs, and increased competitiveness in the market.

Financial data automation

Automating financial data involves intelligent document processing in terms of accounts payable and receivable, risk management, verification and fraud detection, process automation, smart claims, validation, and exception handling.

Intelligent Document Processing: Automated data extraction from loan applications, insurance claims, and other unstructured documents to streamline application and claims processing.
Accounts Payable and Receivable: Automated data entry and reconciliation of financial transactions, reducing the risk of errors and increasing efficiency.
Risk Management: Automated data entry into risk management systems, allowing BFSI organizations to identify and manage risks more effectively.
Verification and Fraud Detection: Automated image recognition and analysis of ID and financial documents, reducing the risk of fraud and improving security.
Process Automation: Automated workflows and processes in areas such as customer onboarding, loan processing, and claims management.
Smart Claims: Automated processing of insurance claims, including data extraction, classification, and decision-making.
Validation and Exception Handling: Automated checks and balances to validate data accuracy, with automated exception handling to ensure that any errors are quickly identified and addressed.

Automation in the finance sector improves accuracy along with security, which positively impacts turnaround time.

Legal data automation

Legal and compliance, with the help of virtual legal Assistants, contract management, and document automation, aims to enhance legal drafting and client communications by automating routine tasks and providing more efficient support.

Virtual Legal Assistants: AI-powered tools providing legal support and guidance, including research, document drafting, and contract management
Contract Management: Automated tracking and management of legal contracts, including data extraction, classification, and entry into compliance systems. This help to reduce the risk of errors and ensures that contracts are managed in accordance with legal requirements.
Document Automation for Enhanced Legal Drafting: Automated document generation and assembly, including data extraction and formatting, to streamline the legal drafting process and improve efficiency.

Virtual legal assistants, contract management, and document automation improve the efficiency and accuracy of legal support and compliance operations while reducing the workload of legal professionals. By automating routine tasks, legal professionals can focus on more complex and value-added activities, thus improving client communication and overall satisfaction.

Customer service automation

Automated customer service aims to enhance call-center interactions by providing customers with more personalized and efficient support via self-service, recommendations, and insights from past transactions.

Self-Service: A customer-facing platform that allows customers to access information easily and solve common problems on their own, reducing the need for support from a call-center agent.
Recommendations: Personalized suggestions for products, services, or solutions based on a customer's past transactions and interactions. This information helps customers quickly find what they need and reduces the time spent on the phone with a call-center agent.
Insights from Past Transactions: Data analysis of a customer's past transactions and interactions to identify patterns, preferences, and potential issues. This information can be used to inform call-center interactions, improving the accuracy and efficiency of support provided.

The goal of improving customer experience by providing more personalized and efficient support is achieved by reducing the workload of call-center agents; these tools can also help to improve the efficiency and cost-effectiveness of customer service operations.

Marketing and sales data automation

Unstructured data automation in marketing and sales makes use of trending metrics to automate various marketing and sales activities to improve efficiency and personalization.

Communication: Personalized messaging and interactions with customers, using data analysis to inform communication content and timing.
Campaign Management: Automated planning, execution, and tracking of marketing campaigns, including targeting, segmentation, and measurement.
Content Creation: Automated generation and optimization of marketing content, including product descriptions, social media posts, and email content.
Forecasting: Predictive analysis of future sales and marketing performance based on historical data and market trends.
Lead Generation and Prioritization: Automated identification and ranking of potential customers based on data analysis and lead scoring.

Improved customer experiences, increased sales, and better alignment of marketing and sales goals can be seen as a product of increased efficiency and personalization.

HR data automation

Automation of HR data focuses on resume mining and document automation for onboarding to reduce turnaround time.

Resume mining is the automated analysis and categorization of resumes to identify and extract key information, such as education, skills, and work experience. This information can be used to quickly identify suitable candidates for open positions.

Document automation for onboarding is the automated processing of onboarding documents, such as employment contracts, tax forms, and benefit enrollments. This includes data extraction, classification, and entry into HR management systems, reducing the risk of errors and increasing efficiency.

Resume mining and document automation for onboarding in HR management streamlines the hiring process, improves efficiency, and reduces the time and effort required to onboard new employees. This improves candidate experiences, reduces HR workload, and ensures more accurate and timely employee data management.

In conclusion, the automation of unstructured data is a valuable tool for organizations looking to gain a competitive advantage and improve operations. By automating the processing and analysis of unstructured data, organizations can uncover insights, streamline operations, and improve overall efficiency. This technology can be applied in a variety of industries, including finance, healthcare, and retail, among others. By utilizing unstructured data automation, enterprises can improve customer experiences, reduce costs, and increase their bottom line. As technology continues to evolve, we can expect to see even more innovative applications and use cases for unstructured data automation in the future. The automation of unstructured data is a powerful tool for organizations looking to harness the value of their data and drive business success.

Birlasoft helped a leading manufacturer extract unstructured data from purchase orders in .pdf format and e-mail attachments with ML-based models and automated the creation of sales orders. This resulted in the transformation of a manual and repetitive process into a streamlined flow of activities, reducing backlogs, eliminating chances of errors and rework and enhanced sales order accuracy.

Birlasoft can help your organization achieve enterprise-level transformation powered by automation and accelerate your digital journey. Visit our Automation page to unleash your automation potential!

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