The Industry Landscape
Strategic Intelligence, a Catalyst for Pharma Innovation
In the fast-paced pharma and biotech sectors, companies rely on high-quality, timely and actionable intelligence to accelerate global expansion, R&D and clinical trials. Competitive intelligence platforms leverage advanced analytics and data insights to navigate market complexities, adapt to changing customer expectations and optimize pipeline development. These platforms enable pharma companies to make informed decisions, streamline operations, reduce costs and bring life-saving therapies to market faster. Research and intelligence flrms are crucial in supplying calibrated and connected data to support these essential objectives.
The Client's Challenge
Unlocking High-quality Insights from Unstructured Data
The client sought an efficient, scalable way to process vast amounts of unstructured information, extract precise insights and maintain a comprehensive global and regional overview of the pharmaceutical market.
The firm’s objectives stemmed from a growing demand for:
Comprehensive Coverage and High-quality Analysis:
The company was pressured to deliver clear, detailed and accurate real-time information on global oncology-related drug pipelines, clinical trials and company benchmarking.
Specialized Data Processing Skills:
The flrm needed to efficiently and systematically process large volumes of complex and unstructured oncological data – obtained from multiple sources such as scientiflc articles, web forums and audio transcripts – to extract meaningful insights.
Digital Transformation of Data Operations:
The flrm sought to replace manual reviews of daily alerts and materials from extensive sources – including its data warehouse, which covered multiple disease areas, associated products and pipeline assets – with an intelligent digital solution that could streamline data aggregation, taxonomy management, content authoring, data validation and publishing.
Scalability:
A lean team with generic skillsets led to challenges in work allocation and handling seasonal workload spikes, prompting the need for a scalable solution to support expanding demands across regions and portfolios.
The Solution
Re-imagining Pharma Intelligence with AI and Automation
WNS Analytics (WNS’ data, analytics and AI practice) engaged as a strategic partner to transform the client’s pharmaceutical intelligence platform. Our approach was rooted in leveraging deep domain expertise, deploying specialized business analysts and embracing best practices in operations delivery. We orchestrated a seamless integration of AI and ML tools tailored to the pharma and life sciences sector and focused on streamlining processes through customized automation.
Key elements of our approach included:
Establishing robust, automated workflows to optimize collaboration and decision-making and reduce operational costs
Developing tailored algorithms using Natural Language Processing (NLP) and ML to enhance data extraction and classification
Strategically deploying domain experts to monitor and curate critical competitive intelligence and refine analytical models, ensuring the value of insights delivered
Harnessing digital platforms alongside AI and ML to transform the content aggregation process, expand global insights coverage and personalize experiences to retain / expand customer base.
This comprehensive methodology ensured that every facet of the transformation – from data ingestion to actionable insights – was addressed holistically, driving innovation and efficiency across the organization.
Key Solution Components:
Advanced Content Aggregation Platform
Built a sophisticated platform to acquire data from various structured and unstructured sources, including public sources, company-owned, third-party sources, syndicated reports, patent sources, regulatory registries and clinical trial registries across multiple countries.
AI / ML-driven Data Scraping and Analysis
Enabled data scraping, followed by data ingestion, transformation – including data cleansing and normalization – and subsequent analysis using AI / ML-enabled data curation models such as BioBERT. The objective was to enable insights generation for decision-making.
AI-powered Data Ecosystem
Utilized pre-built reusable components from WNS’ AI utilities hub to transform the client's data operations by:
- Streamlining data orchestration and governance
- Delivering real-time analytical insights
- Optimizing AI-generated content through NLP and Generative AI capabilities
Tailored Algorithms
Developed customized algorithms using NLP and ML to meet the speciflc needs of our client’s pharmaceutical intelligence platform. These algorithms were designed to accelerate processing and analysis while enhancing:
- Data extraction accuracy
- Data classiflcation and categorization
- Data quality and consistency
- Predictive modeling capabilities
Sophisticated Authoring and Reporting Tools
Deployed automation-enabled authoring tools to streamline the global creation, dissemination and analysis of strategic content. The tools were created to:
- Centralize and automate content creation
- Data classiflcation and categorization
- Enhance content quality
- Accelerate time-to-market
- Support data-driven insights
Advanced Workflows
Created robust, automated workows by leveraging AI and ML to streamline operations, enhance efficiency, reduce operational costs, scale productivity and accelerate go-to-market strategies for new drugs. The workows targeted:
- Automating routine tasks
- Optimizing decision-making processes
- Enhancing collaboration
- Improving data quality
AWS components and services used for the solution:
The Outcome
Accelerated Insights, Expanded Reach, Operational Excellence
The digital transformation of the pharma intelligence platform gave our client a competitive advantage through:
Our outcome-based commercial arrangement on a “no win, no fee” basis ensured there was no upfront investment by the client. WNS further put skin in the game by underwriting the outcomes.
Benefits delivered
Streamlined Content Management:
~ 0% gain in operational efficiency from sophisticated authoring tools and improved workow management, meeting operational goals and the expectations of editorial teams.
Improved Turnaround Time (TAT):
For key projects, the reporting TAT reduced from a monthly update to less than 24 hours.
Scalable and Adaptable Solutions:
The scalable solution helped accommodate new information sources and adapt to changes in existing one.
Efficient Operations:
Intelligent automation for data ingestion from diverse sources meant minimal manual intervention; tailored algorithms enhanced the accuracy and value of insights, signicantly boosting the efficiency of content operations.
Broader Coverage and Expansion:
The initiatives helped retain existing customers while expanding global coverage and geographic reach.