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Enable a modern data analytics platform ecosystem to empower data-driven culture, purpose-built use cases, and business-driven outcomes

As biopharma companies adopt digital platforms across business processes, the need for an out-of-the-box modern data management software to enable advanced analytics to address real-life business challenges is emerging rapidly.

Due to COVID-19, pharma and life sciences business functionaries are implementing digital processes, moving to the cloud, and handling challenges to continuously improve the human touch with patients, providers, and payers. As the digital footprint grows, data is rapidly increasing in all directions of the business value chain across R&D, clinical, manufacturing, internal systems, and sales and marketing business units. It has become critical to collate, analyze, and research a variety of data to make the right business decisions and be compliant with data privacy and regulatory policies, and also it is imperative for life sciences organizations to control data assets through advanced and modern analytics data platforms for the benefits of scale, speed, and consistency.

With analytics, data enablement and data quality is essential in improving decision making and ROI on digital investments done by life sciences organizations. In our experience across pharma, higher data quality leads to higher operational efficiency, ultimately lowering the associated cost and improving business outcomes.

The first thing we’ve learned is the importance of having outstanding data to actually base your ML on. In our own shop, we’ve been working on a few big projects, and we’ve had to spend most of the time just cleaning the data sets before you can even run the algorithm. That’s taken us years just to clean the data sets. I think people underestimate how little clean data there is out there, and how hard it is to clean and link the data.”1 Vas Narasimhan, CEO Novartis 

Business functions

For pharma and life sciences companies to realize the full potential of data, business use cases are critical in providing current business insights. Future predictive insights and recommendations should come out from the data analytics software, resulting in accelerated market opportunities and better patient outcomes. The business use cases for pharma organizations spread across the brand life cycle, such as launching a new brand with go-to-market strategies, tracking brand performance and sales outcomes, gauging provider/payer market assessment, marketing effectiveness, brand competitive intelligence, patient centricity, and consumer affinities, with the common goal to have patient satisfaction, adherence, awareness, and loyalty towards a brand.

Modern analytics products need to provide integrated and clean data to enable real-time analytics and immediate call-to-action across business functionaries, achieving operational excellence. A few business use cases are listed to be considered for the modern analytics platform to enable out-of-box analytics and visualization features.

  • Patient centricity
  • Customer engagement
  • Payer-based opportunities
  • Omnichannel marketing approach
  • Sales and marketing effectiveness
  • Care delivery model
  • Patient and HCP engagement and content recommendations

While the products provide advanced AI/ML-driven algorithms to address business use cases and several out-of-box features to gauge improvement in commercial sales and patient outcome, the most critical is managing data appropriately for faster search and integrated insights.

“At the end of the day, the challenges for applying AI to healthcare aren’t really about the development of the algorithms - it’s about the data.  Sophisticated AI prediction algorithms have been developed in many industries, especially those where discrete, voluminous, well-organized, and readily analyzable data are the norm (meteorology and finance). For AI to be applied to healthcare, however, the underlying data need to be organized and readily accessible. That is not the case with most healthcare data.”1 Dr. Amy Abernethy, Principal Deputy Commissioner FDA

Companies encounter several business cases throughout a product’s life cycle and look forward to business insights from software and visualizations. The key business use cases to be considered across a product’s life cycle are as follows:

PDUFA (FDA response date)
  • Sunshine reports
  • RWE analysis
  • Payer segmentation and behavioural analysis
 
Soft launch
  • Market review
  • Customer and payer segmentation
  • Analog-based forecasting
  • SPP/SD data quality, performance
  • Gross-to-net analysis
  • Inventory analysis
  • Patient cost analysis
  • Supply chain product movement analysis
  • COGS analysis
Hard launch
  • Market basket review
  • Sales call planning
  • Salesforce and alignment setup
  • Speaker program execution measurement
  • Disease management program participation
  • Incentive compensation design
Launch + 3 months
  • Sales budget tracking
  • Market sizing
  • New writers
  • Treatment algorithms and switching analysis
  • ATU-based KPIs
  • Sales vs goals
  • Speaker program execution
  • Share of voice
Launch + 6 months
  • Patient journey
  • Referral patterns
  • Adherence/persistency
  • Target customer profile
  • Coupon analytics
  • Speaker program execution
  • Claim analytics
  • Patient assistance program effectiveness
Launch + 12 months and beyond
  • Sunshine reports
  • RWE analysis
  • Payer segmentation and behavioural analysis
  • Copay elasticity and coupon analytics
  • ROI analysis
  • Plan profiling
  • Campaign design
  • Message effectiveness
  • Commercial effectiveness
  • Marketing mix



Technology Advancement

While the technical use cases will certainly differ across business units from R&D to clinical to manufacturing to commercial sales, the underlying solution components need to be agile and flexible to readily adopt and customize as per business unit-wise technical use cases.

The key technical use cases a pharma company considers out of modern data analytics platform are as follows:

Self Care Capabilities SELF-SERVE CAPABILITIES
  • Self-serve capabilities for data scientists, business users, and IT partners
  • Features such as data ingestion, business/data quality rule configuration, setting notifications/alerts on events, data rendering, and data visualization
Cloud-First Approach CLOUD-FIRST APPROACH
  • A cloud-first strategy unlocks flexibility, agility, and computing advancements required to enable complex business use cases across business units
productionization PRODUCTIONIZATION
  • Adaptation of project to product mindset
  • Data analytics platform as a product needs to be enabled with features such as smart data in-take, operation cockpit view, data catalog, lineage, ETL pipeline, and intelligent connectivity
transparency and control TRANSPARENCY AND CONTROL
  • Modern analytics platform needs to provide complete in-depth visibility and transparency across its components and architecture
  • Companies to have complete control over the platform, its operations, and upgrades
Building Connectivity Across BUILDING CONNECTIVITY ACROSS
  • Integration across multiple internal and external applications in batch, continuous, and stream modes using APIs, CRM connectors, stream sets, and file exports
  • Ease of integration with secure connectivity
Easy to scale EASY TO SCALE
  • Expansion across therapeutic classes, geographies, brands, and business functions
  • The configurable, replicable, and scalable architecture needed to scale across business dimensions

 

Compliance

Pharma and life sciences companies across the globe need to follow various regional compliance and regulations to maintain, secure, and use personally identifiable information. Every region is mandated to follow its data regulatory policy to protect the privacy of its citizens. While expanding business geographically, modern data analytics platforms should enable compliance policies of the concerned region as and when personally identifiable information (PII) is being used and processed for R&D, sales, marketing, and commercial business areas.

  1. GDPR - The General Data Protection Regulation (GDPR) was implemented in 2018 for data protection and privacy in the European Union and European Economic Area to regulate individuals’ collection, usage, storage, and processing of personal data.
  2. HIPAA – The Health Insurance Portability and Accountability Act of 1996 (HIPAA) was implemented to safeguard any individual’s medical and personal information to protect health data created, maintained, processed, or shared electronically.
  3. CCPA - The California Consumer Privacy Act of 2018 (CCPA) was implemented to empower individuals for privacy rights, data protection, and consumers’ right to opt-out/know/delete/non-discriminate. Companies must share privacy policies and practices to maintain California resident data.

While there are many regulatory policies and compliance requirements across nations and regions, pharma and life sciences companies must protect PII through their own policies, standards, or governance processes.

Summary

To be a truly data-driven organization, pharma, and life sciences companies rely on robust data platforms supported by next-gen technology. The technology will play a central role in bringing AI/ML and data science into every data-driven decision. Accelerating the journey toward modernization is the first step for an organization to move toward digital transformation. Modernization is not just moving to the cloud but also involves building the data infrastructure with added capabilities that support any level of the data science workload and delivers valuable predictions or insights.

Learn More

Transform your commercial data platform into a business outcome-driven cloud data management platform

Axtria DataMAx™ is a purpose-built and ready-to-deploy modern data platform that reimagines how pharma commercial and technology teams interact with data by removing the barriers between back-end data management and front-end analytics. Axtria DataMAxTM enables commercial business units across sales, marketing, and patient subject areas with its deep pharma-focused business use cases and next-generation technology architecture. With Axtria DataMAxTM, business teams can source, connect, and transform data – structured and unstructured – to make business decisions with high data reliability. The platform enables cloud data warehouse and data lake capabilities with scalable and configurable architecture, configurable rule engine, data catalog, and lineage features. Axtria DataMAx™ promises to be the enterprise-grade data management platform for the organization by being the central data foundation to drive collaboration, discovery, and synergy across functions.

References

  1. Forbes Article “Pharma's Desperate Struggle to Teach Old Data New Tricks” published in May 2019 https://www.aei.org/articles/pharmas-desperate-struggle-to-teach-old-data-new-tricks/

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