Imagine a mid-sized pharmaceutical company in the immunology space that recently launched a new product in the psoriasis market. Their primary goal is to understand how their product is performing in the market, especially where it's working and where it’s not. To do this, they need to identify the true source of their business, analyze how their product is being utilized across lines of therapy, and understand the key healthcare providers (HCPs) driving the sales. To gather these insights, the company needs to leverage claims data that captures detailed patient treatment journeys. This will allow them to answer key questions, such as:

Where is their product being prescribed in the treatment sequence: first-line, second-line, or later?

What is their market share in each line of therapy?

What therapies are patients using before they start on their product?

Are they new patients, switch-ins from other treatments, or are they adding the product onto their existing regimen?

What is the current fulfillment rate, abandonment rate, and rejection rate?

What are the rejection reasons? How does the abandonment rate vary with an increase in out-of-pocket amount?

Which healthcare professionals are consistently driving uptake of the product?

Are these early adopters, high-decile writers, or do they have specific prescribing behaviors?

By leveraging claims data analytics, the company aims to bring all this information together to deliver actionable, data-driven insights. These insights will refine their sales and marketing strategies, helping them make informed, timely decisions to maximize the impact of their new product.

However, the challenge they face is that if the claims data is not organized and handled in a structured manner, the complexity of the data and the numerous business rules involved can significantly delay the process of obtaining actionable insights. This could result in a substantial lag in brand strategic decision-making, potentially being made months after the optimal moment has passed.

Several operational challenges contribute to this lag -

, causing inconsistencies and reconciliation delays

Heavy reliance on manual application of business rules

 (e.g., market baskets, patient stability rules, line-of-therapy methodologies)

, slowing down decision-making across teams

, i.e., benchmarking against historical data before final reporting

As a result, generating actionable claims insights can take a month or more, 

delaying decisions on treatment trends, marketing strategies, market access, competitive benchmarking, etc.

Introducing Claims360: Structured, Scalable, and Built for Speed

At ProcDNA, we are solving this challenge with Claims360, a structured analytics accelerator that eliminates inefficiencies and accelerates time-to-insight.

Imagine Aliza, a Commercial Analytics Director, wants to analyze year-over-year new patient starts for Biologics in the psoriasis market. Traditionally, she would need to request consulting vendors, wait for data extraction, and review reports that could take days. With the Claims360 self-serve analytics platform, she can simply type a question like: 

“How many new patients start do we see each year for Biologics in the psoriasis market?”

 Within seconds, the system provides: A data table with exact figures, a trend chart for visual analysis, and LLM-generated insights highlighting key takeaways.

This is just one example - more complex queries can now be answered in real time with intuitive access to insights, making Claims360 a powerful solution in claims analytics.

Our Claims360 solution is built on three key pillars -

1. Unified Data Framework & Business Rules Parameterization

Claims analytics require a flexible structure to adapt to evolving market needs. Claims360 centralizes fact and dimensional data while parameterizing business rules, ensuring agility without compromising consistency.

Aggregates fact and dimensional data appropriately from the claims database by creating structured data modules - raw, pre-processed, and final processed layers, enabling data readiness for analytics and insights. The framework is designed for easy integration with leading cloud platforms like Databricks, Snowflake, and others.

Claims analytics process involves multiple business rules at various stages, many of which are tailored to specific analytical needs, adding complexity. These rules, ranging from patient inclusion criteria to patient journey logic, are often tailored to specific brand or indication needs, which adds complexity. Without an organized way to manage them, analytics become inefficient, error-prone, and inconsistent across use cases. Therefore, parameterizing these rules is critical to improving efficiency, reducing quality risks, and ensuring consistency across workflows.

In Claims360, we built a structured framework that modularizes business rules based on user inputs, allowing dynamic rule adjustments without disrupting the workflow. Below are some key business rules we have parameterized to enhance robustness and efficiency:

ICD Code & Market Basket Drug Rules to identify patients relevant to the indication of interest. For example, the ICD codes for your indication of interest might not change, but the market basket may change with the launch of new products. Therefore, with a parameterized input option, you can update the market basket in real-time

Patient Stability Criteria to ensure patients meet diagnosis/treatment thresholds before inclusion. For example, in metastatic breast cancer (mBC) analytics, a single diagnosis may indicate misclassification. However, a patient with two or more diagnoses reduces the risk of misdiagnosis. If you're tracking multiple indications where the patient stability rule differs, this parameterized input ensures each variation is handled accurately and efficiently

Line of Therapy (LoT) Rules such as - Grace periods for market basket drugs to ensure accurate treatment tracking, Product episode creation rules to define treatment phases, Line advancement logic (e.g., an addition or substitution of a drug is a line change, whereas discontinuation is not), Regimen creation rules to structure therapy pathways effectively.

..and many others, such as exclusions based on comorbid conditions

This parameterized approach allows our pharma clients to modify business rules dynamically based on real-time market insights, ensuring more accurate analytics and faster decision-making.

2. Automated Quality Controls for Accuracy & Efficiency

Ensuring data integrity while maintaining speed is always a challenge. Claims360 embeds automated quality checks at every stage of the workflow:

 – Our standard Data Quality Management (DQM) framework helps maintain completeness, consistency, and accuracy by applying expected benchmarks. For example: Checking that patient ID and claim ID are not null; if they are, flag and quantify the impact, running outlier detection on key attributes like Days of Supply (DOS), and similarly applying additional quality checks across other attributes

 – Before starting the data processing, compare incoming data feeds against previous ones to make sure there are no unexpected discrepancies that could skew the analytics

Specific quality checks are built into the business rules implementation phase to ensure the logic is being applied correctly. For example, in Line of Therapy, we apply checks for various edge cases to confirm that line advancement and regimen naming are correctly implemented in the patient journey

Further, automated checks ensure patient and HCP demographics, control totals, and other key metrics are aligned with expected ranges

Report Benchmarking Against Historical Trends

Automated comparison of the latest reports against the previous version to track percentage changes and spot any data anomalies. This helps flag potential inconsistencies early and ensures accuracy before the reports get published for end users

By catching issues upfront, Claims360 makes it easier for teams to trust their data, leading to more reliable insights and better decision-making.

3. Large Language Model (LLM) based, Self-Serve Insights for Faster Decisions

Traditional claims analytics often involve multiple handoffs between analysts, vendors, and commercial teams, leading to delays. Claims360 solves these challenges through LLM-based, self-serve analytics, enabling teams to get the insights they need in real time.

Natural Language Processing (NLP) for Instant Insights

: With Claims360’s intuitive interface, business users can explore data instantly, without relying on technical teams

 Insights are presented in intuitive graphical formats with key takeaways, enabling commercial and access teams to make informed decisions quickly

 Insights that once took days can now be generated in seconds

By integrating LLM-powered, self-serve tools with claims analytics, Claims360 empowers business users to independently access critical insights in seconds, eliminating delays, reducing dependencies, and dramatically accelerating decision-making across commercial teams.

, we know that claims data is the key to commercial success by adjusting the brand strategies in real-time. Want to see how Claims360 can help your team move at the speed of the market? Let’s connect and explore a tailored solution for your business.

Accelerating Time-to-Insight in Claims Analytics: A Smarter Approach with Claims360

Pharma teams are swimming in data—but often lack a unified view of what it means.

Primary Market Research (PMR) provides insight into what HCPs, patients, and payers believe. Real-World Data (RWD) shows how they behave. When used in isolation, each leaves gaps that result in blind spots and inconsistent narratives. When integrated, they deliver a complete, actionable view of the market, improving the speed, depth, and confidence of decision-making.

Many pharma organizations still commission PMR and RWD initiatives separately, often through different partners. This fragmentation slows down insight generation, creates duplication, and makes it harder to connect the dots between stakeholder perceptions and actual behavior.

Engaging a single partner to design and execute both PMR and RWD allows for faster execution, deeper synthesis, and a shared context across research efforts. The result is stronger triangulation, more cohesive narratives, and greater alignment across cross-functional teams.

Delivers a more robust, holistic view of market dynamics

Enables more actionable and context-rich strategies

Builds leadership confidence through unified, data-backed narratives

Supports more targeted, effective omnichannel engagement

RWD identifies real-world treatment transitions, drop-offs, and switches. PMR adds context—why those events happen, what emotions or knowledge gaps drive them.

Example:
If RWD shows discontinuation between lines of therapy, PMR may reveal causes such as lack of perceived alternatives, treatment fatigue, or access confusion—insights critical for shaping support and education initiatives.

PMR reveals how payers and HCPs view a therapy’s value. RWD shows if that translates into favorable access or exposes barriers like prior authorizations or step edits.

Example:
Even if PMR suggests payers are supportive, RWD may reveal restricted access in practice—prompting more aggressive pull-through strategies in specific geographies.

RWD supports uptake modeling and analog benchmarks, while PMR adds intent-to-prescribe signals that fine-tune projections.

Example:
A product may resemble past launches in claims data, but if PMR shows HCP hesitancy around differentiation, forecasted uptake should be adjusted accordingly.

4. Field Force Strategy & Incentive Design

RWD highlights high-value prescribers and territories. PMR reveals how best to engage them—whether through peer education, digital channels, or clinical storytelling.

Example:
If RWD identifies key prescribers and PMR reveals preference for non-personal promotion, reps can shift to hybrid touchpoints and IC plans can reflect preferred engagement style.

PMR defines segments by beliefs and prescribing logic. RWD grounds those segments in real-world behavior and allows them to be projected to the HCP universe.

Example:
A PMR-defined segment of “proactive switchers” becomes useful only when RWD confirms which HCPs consistently switch therapies—enabling precision targeting.

PMR uncovers message resonance, objections, and preferences. RWD adds timing and behavioral cues, helping marketers align messages with real-world decision points.

Example:
If PMR shows safety concerns are a barrier to adoption, and RWD shows adherence gaps in legacy therapies, messaging can highlight real-world safety outcomes to shift prescribing habits.

7. Launch Readiness & Early Performance Monitoring

PMR informs launch plans by gauging awareness and intent. RWD validates those assumptions post-launch, tracking prescribing velocity and regional variation.

Example:
If prescriber intent is high in PMR but uptake is slow in claims, it may point to access or communication breakdowns—triggering targeted follow-up.

PMR tracks shifting perceptions; RWD shows evolving usage patterns or niche uptake.

Example:
If RWD shows increased use in a specific patient subgroup, PMR can validate this trend and guide messaging or trial investment to support an indication expansion.

In a high-stakes, rapidly evolving environment, pharma leaders can’t afford to operate with fragmented insights. Integrating PMR and RWD—with a single, insight-driven partner—unlocks faster, more confident decision-making grounded in both belief and behavior.

ProcDNA’s Value in Integrated Insight Generation

ProcDNA helps pharmaceutical companies bridge the gap between siloed research and strategic execution. Our approach delivers value through:

Integrated teams driving cross-functional insight generation: 

Our collaborative, cross-trained teams work across RWD analytics and primary research to deliver cohesive insights spanning patient journey mapping, market access strategy, segmentation, field force planning, and forecasting. This integrated model eliminates handoffs, accelerates execution, and ensures strategic alignment.

Our proprietary GenAI platform is custom-trained on your internal ecosystem—scientific publications, market research studies, claims data, strategy decks, and more—enabling teams to instantly surface insights, connect the dots across datasets, and accelerate decision-making. It acts as a tailored, trusted knowledge engine built specifically for your organization, providing faster access to evidence-backed answers when you need them most.

Why Pharma’s Siloed Data Strategy Is Holding It Back

In the first part of this discussion, we explored why emerging pharma companies must prioritize data governance and how it drives business value. Now, we take a deeper dive into laying the foundation for a strong data governance framework and implementing it effectively. By establishing clear governance structures and adopting a phased approach, emerging pharma companies can ensure compliance, operational efficiency, and long-term success.

Laying the Foundation for a Strong Data Governance Framework

A well-structured data governance framework is essential for managing data efficiently and ensuring regulatory compliance. Here’s a closer look at the critical elements that form the backbone of this framework:

A successful data governance framework requires clear roles and responsibilities. Data stewards and data owners play a pivotal role in ensuring data integrity and accountability:

 Business leaders or domain experts who are accountable for the accuracy and security of specific datasets

 Responsible for enforcing data policies, monitoring data quality, and ensuring consistency across systems

Establishing a governance committee with representatives from IT, compliance, and business units fosters collaboration and drives governance success

Emerging pharma companies must navigate a complex regulatory landscape, including:

HIPAA (Health Insurance Portability and Accountability Act): 

Ensures the protection of patient health data

GDPR (General Data Protection Regulation):

 Governs data privacy and security for European markets

 Regulates electronic records and electronic signatures for FDA compliance

Defining retention and deletion policies based on legal requirements to minimize risk and ensure compliance

Establishing standardized data quality metrics is essential for operational efficiency and accurate decision-making. Key aspects include:

: Ensuring data is correct and up to date

: Maintaining uniform data formats across systems

: Keeping data relevant and up to date for business use

: Implementing automated tools to track anomalies and ensure adherence to quality standards

Metadata management enhances data discoverability and lineage tracking. It involves:

Creating an organized inventory of data assets

 Understanding how data flows across systems to maintain accuracy and transparency

 Standardizing terminology to improve data understanding across teams

 Leveraging AI-driven tools to categorize and classify metadata effectively

Seamless data integration is crucial for leveraging insights across various functions:

Ensuring smooth data exchange between R&D, clinical, regulatory, and commercial departments

Implementing industry-standard formats (e.g., CDISC for clinical data) to facilitate integration

Utilizing modern data pipelines to ensure efficient data movement and transformation

Implementing Data Governance: A Pragmatic Approach

For emerging pharma companies, implementing data governance should be structured and scalable. Here’s a step-by-step approach to ensure success:

Conduct a data maturity assessment to identify gaps and prioritize governance initiatives

Define a governance roadmap aligned with business objectives

Develop key performance indicators (KPIs) to measure governance effectiveness

2. Stakeholder Buy-In and Culture Building

Engage leadership to secure support and investment in governance initiatives

Foster a data-driven culture through training and awareness programs

Establish a governance council to oversee implementation and policy enforcement

Define clear data governance policies, including data access, classification, and retention standards

Establish Standard Operating Procedures (SOPs) for data handling, validation, and remediation

Ensure alignment with regulatory frameworks to mitigate compliance risks

Implement data governance platforms such as Collibra, Informatica, or Atlan to automate governance workflows

Leverage AI-driven tools for data discovery, cataloging, and lineage tracking

Establish role-based access controls and encryption mechanisms to enhance security

5. Continuous Monitoring and Optimization

Define key metrics to track governance effectiveness, such as data accuracy rates and compliance adherence

Conduct periodic audits and refine governance strategies based on evolving business and regulatory requirements

Establish feedback loops to ensure governance practices remain relevant and effective

Conclusion: Building a Sustainable Governance Strategy

Implementing a strong data governance framework is a long-term commitment that requires continuous improvement and adaptation. Emerging pharma companies that invest in structured governance early in their data lifecycle will not only achieve regulatory compliance but also unlock the full potential of their data assets. By fostering a culture of data stewardship, leveraging advanced technologies, and integrating governance into daily operations, these organizations can position themselves for sustained success in an increasingly data-driven pharmaceutical industry.

Building a Robust Data Governance Framework for Emerging Pharma Companies: A Deep Dive

In the fast-evolving world of pharmaceuticals, staying ahead of the curve is not a luxury — it's a necessity. From understanding competitor pipelines to tracking shifts in physician sentiment, Competitive Intelligence (CI) has become the strategic backbone of decision-making for commercial, R&D, and medical affairs teams. And now, with the rise of Generative AI (Gen AI), CI is undergoing a transformational shift — from reactive reporting to real-time, predictive insights.

Why Competitive Intelligence is Crucial in Pharma

Pharma is a high-stakes, high-investment industry. A single missed signal — be it a clinical trial update, regulatory shift, or change in market access — can impact billions in revenue. Here's why CI is indispensable:

Monitoring competitors' clinical trial progress helps pharma companies benchmark their timelines, anticipate launches, and pivot strategies accordingly.

Keeping tabs on approvals, designations (e.g., orphan drug status), or safety warnings helps companies manage risk and opportunity in real time.

3. Market Access and Pricing Intelligence

CI helps track how payers respond to new therapies, what access barriers exist, and how reimbursement landscapes evolve across regions.

Understanding how key opinion leaders (KOLs) are engaging with competitors — through publications, conference presentations, or social media — is vital for shaping medical strategy.

Whether it’s launch timelines, messaging, or digital campaigns, CI helps teams assess what’s working for competitors — and what can be improved internally.

Medical congresses are where strategies unfold in real time — from late-breaking clinical trial results to visible shifts in KOL alignment. With Gen AI, companies can now analyze hundreds of abstracts and posters at scale, while simultaneously tracking live sentiment from social media during major events like ASCO, ESMO, or ADA. This enables teams to extract timely, actionable insights while the conference is still ongoing — not weeks later.

Challenges with Traditional CI Approaches

Despite its value, CI in pharma has traditionally been:

– Relying on analysts to sift through publications, trial registries, and press releases. 

– Data sits in silos: scientific, regulatory, commercial, and digital sources often aren’t integrated. 

– Insights are often retrospective, limiting their strategic utility. 

Enter Generative AI: A Game Changer for Pharma CI

Generative AI is redefining the entire CI workflow — from data gathering to insight generation. Here’s how:

Gen AI tools can crawl, extract, and summarize structured and unstructured data from diverse sources: clinical trial registries, publication databases, investor presentations, social media, and more — in real time.

2. Smart Summarization and Trend Detection

Instead of dumping data, Gen AI can synthesize insights: "What’s new in competitor trials this week?" or "What topics are trending among oncologists post-ASCO?" This allows for proactive, insight-driven CI.

No more complex dashboards or SQL queries — business users can simply ask, “Which KOLs are shifting support from Drug A to Drug B in NSCLC?” and get an answer in seconds.

4. Scenario Simulation and Competitive Forecasting

With Gen AI-powered models, teams can simulate market responses: “If Competitor X receives FDA approval in Q3, what’s the likely impact on our brand uptake in Q4?”

Gen AI can tailor alerts based on therapeutic areas, brands, or competitive threats, ensuring that medical, marketing, and BD teams get what they need — and not what they don’t. 

Use Cases: How Pharma Teams Are Already Using Gen AI in CI

 Monitoring conference activity, identifying rising KOLs, and tracking shifts in scientific narratives. 

 Benchmarking digital campaigns and sales force activity against competitors. 

 Identifying white spaces based on competitor trial designs and unmet needs. 

 Assessing emerging biotech innovations and acquisition targets more efficiently. 

The Road Ahead: Human + Gen AI = Competitive Advantage

Gen AI is not replacing human intelligence — it’s amplifying it. The best CI strategies will combine:

Analyst expertise for contextual interpretation. 

AI-powered tools for scale, speed, and precision. 

Cross-functional collaboration to ensure insights translate into action. 

Pharma companies that embrace this hybrid model will be better positioned to anticipate change, respond faster, and gain sustainable competitive advantage in an increasingly crowded market. 

At Auxo, We’re Building the Future of Competitive Intelligence

The competitive intelligence function is no longer just about tracking competitors — it’s about empowering decisions with speed, clarity, and foresight. With Gen AI in the mix, pharma companies have a rare opportunity to leapfrog from lagging indicators to leading intelligence.

Now is the time to invest, experiment, and embed Gen AI into the CI fabric — before your competitors do. We’ve seen the gap firsthand — and that’s why we built Auxo. Auxo combines real-time data aggregation, Gen AI-powered insights, and customized views tailored for commercial, medical, and BD teams. 

We bring together data from 100+ different sources like publications, social media, clinical trials, and more — and use Gen AI to help our clients cut through the noise and act with clarity. 

If you’re building your CI muscle, let’s connect. The future of pharma intelligence isn’t just faster — it’s smarter.

The Critical Role of Competitive Intelligence in Pharma — And How Generative AI is Redefining It

In today’s rapidly evolving pharmaceutical landscape, data is more than just a byproduct of operations – it is the backbone of innovation, regulatory compliance, and strategic decision-making. While large pharmaceutical corporations have established robust data governance frameworks, emerging pharma companies often overlook the necessity of structured data management in their early growth stages. However, the need for data governance begins the moment data enters the ecosystem, and proactive implementation can be the differentiator between sustained success and operational chaos.

Why Emerging Pharma Companies Must Prioritize Data Governance

Emerging pharma companies operate in a dynamic environment where agility is key. They manage diverse datasets spanning clinical trials, regulatory submissions, supply chains, and commercial analytics. Without a well-defined data governance strategy, these companies face inefficiencies, compliance risks, and missed opportunities for data-driven insights. A structured approach to data governance enables organizations to maintain high-quality data, ensure regulatory adherence, and enhance collaboration across departments.

The foundation of any data governance initiative lies in a clear understanding of data ownership, accountability, and lifecycle management. A well-defined framework should include:

 Assigning data stewards and defining ownership ensures accountability for data accuracy, consistency, and security

Ensuring adherence to regulations such as HIPAA, and GDPR safeguards against legal repercussions and maintains stakeholder trust

 Establishing standardized metrics for data accuracy, completeness, and consistency reduces errors and operational risks

 Effective metadata management enhances data discoverability, lineage tracking, and usability

Seamless integration across research, development, and commercial functions enables organizations to leverage data cohesively

Driving Business Value Through Data Governance

A well-executed data governance strategy fuels business growth by enhancing operational efficiency and decision-making. High-quality data empowers emerging pharma companies to:

 Streamlined data management shortens research timelines by reducing redundancies and improving trial efficiency

 Reliable commercial data enables better forecasting, competitive analysis, and targeted go-to-market strategies

 Clean and structured data is essential for deploying AI-driven models, predictive analytics, and machine learning applications.

Conclusion: Data Governance as a Competitive Advantage

For emerging pharma companies, data governance is not just a regulatory necessity – it is a strategic enabler that drives innovation, operational excellence, and market leadership. By embedding governance principles early in their data lifecycle, organizations can unlock the full potential of their data assets, ensure compliance, and build a foundation for sustained growth. As the pharmaceutical industry continues to embrace digital transformation, companies that prioritize data governance will gain a competitive edge, positioning themselves as leaders in an increasingly data-centric world.

This concludes Part 1 of my article, where I provided an overview of data governance for emerging pharma companies. In Part 2, I will delve into the foundational elements and key pillars of data governance, outlining a structured approach for effective implementation in an emerging pharma company.

Data Governance for Emerging Pharma Companies: A Necessity at the First Point of Data

For years, Business Intelligence (BI) tools have helped organizations make data-driven decisions, relying on dashboards, reports, and analytics. But accessibility remains a challenge.

While data analysts and BI teams can easily use these tools, business users – like leaders, sales teams, and marketers – often find them complex. As a result, they depend on data teams for insights, slowing down decisions and creating inefficiencies. 

Traditional BI relies on static dashboards with predefined reports. If users need new insights or have follow-up questions, they have to wait for analysts to generate answers, causing delays.

Now, imagine simply asking a question in plain English and getting instant, data-driven answers – no dashboards, no SQL, no delays. 

– making self-serve analytics a reality for everyone. 