Key Opinion Leaders (KOLs) are experts who provide valuable insights into the unmet needs and preferences of the target patient market as well as have an influential impact on the decision-making process of other healthcare professionals. They are often involved in clinical trials, research publications, educational outreach, thought leadership, guideline development, and policy-making initiatives. KOLs offer immediate and actionable insight that can significantly impact brand awareness and inform strategic campaign planning. Thus, identifying KOLs is crucial for pharmaceutical and biotechnology companies, as the KOLs can provide valuable insights that these companies can leverage to design and execute effective business strategies across R&D and commercial. However, identifying KOLs is not a straightforward task, as it requires collecting and analyzing data from multiple sources such as clinical trials, research papers, media articles, patient claims, and other healthcare databases. Therefore, a comprehensive and systematic approach is needed to integrate and process the data from various sources and identify the KOLs that match the specific business objectives and criteria. In this paper, we propose a methodological and standardized approach to identify KOLs in any therapeutic area using machine learning algorithms leveraging data from various sources. We have described the main steps and challenges involved in the process, as well as the possible approaches that can be followed to achieve the desired business outcomes.

 Machine Learning, Key Opinion Leader, KOL, Real-World data, Feature Engineering, Digital Opinion Leader.

KOLs are subject matter experts who are integral in driving medical breakthroughs. They create awareness about a drug’s Mechanism of Action and lend credibility to products in the healthcare community. They have a wealth of knowledge in their respective fields due to extensive experience in managing diverse patient cases, adept at diagnosing multiple diseases, contributing to key medical research, and thus, holding significant influence over their peers and patients. They act as a strong bridge connecting patient needs with medical R&D efforts. Thus, pharma companies are always keen to engage with KOLs to position their drug(s) in the market through proven drug characteristics like newer and effective therapies, better efficacy, fewer side effects, ease of use, etc. which can lead to better adoption and positive 6 patient outcomes. By extension, this also helps pharma companies commercially in driving up sales and revenue. For example, as per published data in industry articles1 , companies that had MSLs engage with KOLs before product launch to educate them about the latest healthcare developments, had 1.5 times greater treatment adoption across the opinion leaders’ healthcare organizations in the first six months of product launch.

1.2 Challenges with the existing approaches:

 Currently, there is an over-reliance on internal teams across medical, sales, marketing, etc. to gather inputs on opinion leaders in the target market with limited usage of various available data points. 

ii. Lack of standardization across diverse sources:

 With the increase in number of data sources related to research, drug development, social and general media, etc., collating information is a significant challenge and requires: 

a. Mapping data from different sources to a single HCP using de-anonymization techniques and 

b. Filtering through the noise to find the relevant data.

iii. Lack of robust approach to quantify opinion leaders’ impact:

 Once the required data is gathered, a standard approach needs to be deployed that can measure contributions and sphere of influence for each opinion leader in and across all data sources. 

Figure 1: We consider both HCP Activity and HCP Influence metrics for determining KOL status

 Our KOL identification approach is motivated by the fact that HCPs who have significant contributions as well as high influence in the healthcare community can be considered Key Opinion Leaders. These contributions could include research, publications, innovations, or any other activity that demonstrates expertise. Their level of influence is determined through the adoption of their work and collaborations with other HCPs, suggesting that their work is recognized and valued by their peers. This has been briefly summarized using an example in Figure 1.

All contributions are mapped to HCPs as their activity metric, and all adoptions as well as collaborations are mapped as their influence metric. These metrics are used for feature engineering of each data category to be input into the machine learning models. 

3.1 Identifying Data Categories & Sources: 

KOLs are known for expressing their opinions, thought leadership, and research work through scientific publications, national/international conferences, various media sources, etc. They also actively participate in various clinical trials and hold executive positions in professional associations. Thus, identifying the right data categories and then gathering relevant data from the appropriate sources is an important prerequisite for our identification exercise. For our exercise, we have identified and gathered data from multiple sources for seven broad data categories (Table 1) as shown below:

As we further conducted a deep dive into each data category to identify the source(s), we assessed their data on its Availability, Accessibility, and Stability i.e. selecting data repositories that provide:

i. Availability of comprehensive data coverage across HCPs

ii. Accessibility to relevant data fields for modeling

iii. Stability of data format through historical feeds 

3.2 Mapping consolidated data to HCPs and indication:

 One of the challenges with data gathering is that HCP(s) and Indication(s) might not be explicitly specified within a data source but embedded in accompanying textual information. Thus, a core process for the KOL identification exercise is to map each data information to relevant HCP(s) and Indication(s). Multiple machine learning algorithms are available to conduct this exercise.

For example, to address indication mapping, we employed the BioBERT model, a specialized language representation model. Initially pre-trained on English Wikipedia and BooksCorpus, this model was further fine-tuned using biomedical corpora from PubMed and PMC, enabling effective mining of biomedical text. 

 As the next step of data preparation, we filtered the data for the appropriate HCP cohort. A few of the selection criteria were: 

i. HCP Specialty: To understand the broader outlook on HCP’s specialization and medical interest. 

ii. Activity Timeframe: To identify if the HCP(s) have been active in the last few years (e.g. to consider HCPs in the cohort if they have contributed across any data category in the last 2 years) 

We identified and gathered data from different relevant sources across each data category and applied HCP/indication mapping, and pre-processing steps to store mapped data of relevant HCP cohorts in a standard format. This was further utilized by the machine learning models to identify KOLs (Figure 2).

 We looked at HCP Activity and Influence features for KOL identification.

 To quantify HCP influence, we used network graph analysis within each data category. These help us understand: 

• Efficient exchange/flow of information between clusters of HCPs through these HCPs 

Graphs are mathematical structures used to model many types of relationships and processes in information systems. They are primarily composed of: 

• Nodes: which represent system entities (e.g. each HCP is a single node in our case) 

• Edges: which represent relationship(s) between entities. (i.e. HCPs connectivity with other HCPs) 

Specifically, to identify the important nodes in any graph, we use the concept of centrality. Centrality measures the degree to which a node is central to the network, i.e., it gives us an idea about the number of direct connections a node has or how many other nodes it serves as a link. There exist four centrality measures, each capturing different aspects of a node’s significance within a network. These are:

In the context of our exercise, we selected and employed the following centrality measures to create influence features for each HCP within each data category: Degree Centrality and Betweenness Centrality (refer to Table 3 for their definition and significance)

After the features have been created for activity and influence, we converted these into ordinal scores with values ranging between 0 and 1. These scores are obtained by normalizing and standardizing the observations of the above features. These scores are then utilized by the Machine Learning model that is elaborated in the next section. 

[Normalizing: If denotes the observation of the feature, then the normalized observation would be where and denote the mean and standard deviation of the observations of the feature. Normalizing converts the distribution of observations to one with 0 mean and unit standard deviation. Standardizing: If denotes the observation of the feature, then the standardized observation would be where and denote the maximum and minimum observation corresponding to the feature. Note that standardized scores lie between 0 and 1.] 

As we worked with unlabeled data, we utilized unsupervised machine learning algorithms for our KOL identification exercise. The models were filtered which offered anomaly detection techniques since KOLs are HCPs who are outliers due to their significant contributions and influence compared to other HCPs within each data category. 

We compared two different types of unsupervised models - the Isolation Forest Model (iForest) and the Gaussian Mixture Model (GMM). The iForest and GMM are both anomaly detection techniques but use different approaches for learning and identifying anomalies. By applying these models separately to each data category’s final output, we attempted to filter Key Opinion Leaders within each data category to assess the performance of both models. 

To effectively discover abnormalities, the iForest model makes use of recursive partitioning and random splitting. Anomaly scores are determined using the average path lengths of those anomalies. 

 This is a probabilistic model that assumes a mixture of Gaussian distributions and is used to generate data. For each Gaussian component, it applies the Expectation-Maximization (EM) algorithm to iteratively estimate parameters and assign probabilities to data points.

 The two models classified the HCPs into two categories:

i. Category A (an anomaly) represents individuals identified as Key Opinion Leaders (KOLs). 

ii. Category B (not an anomaly) represents individuals who were not classified as Key Opinion Leaders (KOLs). 

The performance of the models is evaluated using ‘F1 score’ and ‘recall’ across various sources. The F1 score provides a balance between precision and recall, while recall focuses on identifying true anomalies. Visualization of F1 scores and Recall distributions (Figure 4) across sources reveals that iForest has a high precision-recall balance. 

 iForest consistently achieved high F1 scores, emphasizing its precision-recall balance. 

 iForest exceeded in identifying anomalies, ensuring a larger proportion of true anomalies are detected. 

iForest has consistently high score across categories demonstrating robustness.

Based on the above-observed model performance metrics, we selected and utilized the iForest model for our KOL identification exercise. Its notable qualities, such as a consistently higher recall rate, a well-balanced precision-recall performance, and proven resilience across a variety of data categories, constitute the basis of selection. 

 Multiple isolation trees are constructed independently. Each tree is built by randomly selecting a subset of features. The recursive partitioning process continues until each data point is isolated in its leaf node. The randomness in feature selection contributes to the diversity of trees. 

 For each data point, the path length within each isolation tree is computed. The path length is the count of edges (splits) traversed from the root of the tree to the leaf node containing the data point. Anomalies are expected to have shorter paths due to their isolation. 

The anomaly score for each data point of each source is determined as the average path length across all isolation trees. Anomalies, being isolated with shorter paths, will have lower average path lengths. Therefore, instances with lower scores are considered more likely to be anomalies.

where: • s(x,n) is the anomaly score for data point x, • E(h(x)) is the average path length for x across all trees, • c(n) is the average path length for an unsuccessful search in a binary tree of n points.

To facilitate better interpretation and comparison of anomaly scores, they are normalized to a standard range, commonly [0, 1]. 

 Instances with anomaly scores above the threshold are considered as KOLs, while those below are regarded as non-KOLs.

Figures 5 and 6 illustrate the implementation and intuition of the iForest Model respectively. 

4.6 Machine Learning Model Score Generation:

 Normalized features for each data category were used as inputs in the machine learning models to generate scores for each HCP to identify the Key Opinion Leaders (KOLs). The generated scores also help us to identify the relative importance/ranking of each KOL within each data category. 

 across each data category. These category-specific features were then standardized/normalized and ingested in two different machine learning models (iForest and GMM) to derive relative scores for each HCP within each data category. Through comparison of the modeling performance parameters, we also established that iForest proved to be an effective modeling technique for KOL identification and was used to generate scores for each data category (Figure 7)

5. Customization as per business objective: 

To cater to the evolving business needs, we developed a custom scoring mechanism that provides variable weights to scores of each data category before generating the final score (Figure 8). This customization feature empowers the user to modulate the scores’ importance and generate specific KOLs that are relevant to business requirements. For example, if the business objective is to increase engagement towards digital KOLs, a higher weight could be given to social and general media data categories relative to other data categories to generate a KOL list which is a reflection of Digital Opinion Leaders (DOLs).

 In this paper, we have presented a detailed methodology to identify KOLs using Real - world datasets and advanced machine-learning techniques. We also talked about an additional customization module that we have developed on top of this to cater to evolving business needs. Our approach could be used to identify KOLs across any geographic and therapeutic area. 

The outcomes of our current modeling exercise can be further processed to create additional summaries that can add value to the end user for generating insights. One such domain of interest could be defining the “geographical archetypes” of KOLs. This new metric could potentially quantify the sphere of influence of each KOL within a defined country or geography. Some of the key features that could be considered are – the distance between KOLs and HCPs interacting/connected with them, the number of times they interacted with each other, and the number of sources they have interacted through. Using these features, HCPs could be grouped into three clusters, such as National, Regional, and Local, by using unsupervised machine learning techniques like clustering algorithms.

Identifying the Right Key Opinion Leaders in Pharma Using Machine Learning: Creating a Data - Driven Process for Impactful Outcomes

Life Sciences companies have grown significantly in leveraging analytics and technology to solve complex commercialization problems. Their efforts in unifying the in-house data, such as prescriptions, claims, field activity, and other personal and non-personal promotional channels, have started to deliver insights about their target Health Care Professionals (HCPs). However, a lot of organizations are still missing the ability to leverage the power of data available in the digital domain. For instance, the recently concluded ASCO 2024 had more than 42,000 Tweets, with an engagement of over 2 million. 85% of this engagement is from individual oncologists or cancer speakers talking about the studies presented during the conference. It’s not just ASCO and Twitter/X; but hundreds of other digital sources where HCPs go to disseminate and consume information. Harnessing this data will give life sciences organizations the capabilities enjoyed by organizations in high-tech and retail to create a deeper understanding of their customers.

Why should data and insights leaders bother?

Access to specialty physicians has reduced post-COVID-19. Field teams are struggling to get meetings with HCPs, and even when they do, it’s a constant struggle to drive meaningful and engaging conversations. The HCPs are showing us a way. It’s up to us to understand and create a competitive edge. Contrary to popular belief, physicians are turning out to be more tech-savvy. While just 1% of non-physicians are active digital content creators, more than 5% of physicians are active content creators. Here's what life sciences organizations can achieve with an enhanced focus on digital data.

The digital data sources provide a comprehensive view of HCPs preferences, behaviors and engagement patterns.

Types of content HCPs create, share and engage with

Key topics that resonate with HCPs and shifting preferences over time

Events and congresses where HCPs are actively participating

2) Improved utilization of network influence:

 We couldn’t agree more with the saying, “Your network is your net worth.” Understanding the network strength of your target HCPs can unlock many doors.

Physicians who influence or are influenced by your HCP of interest

Physicians who have emerged as Digital Opinion Leaders (DOLs)

3) Deeper understanding of the HCP share of voice:

 Market Research teams play a vital role in understanding brand recall and perception. Their insights, however, are drawn from relatively small sample sizes and double-blinded research. Digital information can augment the impact created by market researchers.

Insights sourced from 15-20 x sample sizes

Targeted information providing actionable insights at individual physician level

The current state of external data utilization

Currently, many life sciences companies have a wealth of internal data. They are spending significant efforts in creating a unified view of the HCPs, calling it HCP 360. This includes sales, activity, sampling, speaker bureau, segmentation, interaction with branded and unbranded websites, and other non-personal promotions. The organization often lack the integration of external data sources. While some organizations are championing the use of social media data to inform their strategies, even fewer leverage other digital sources such as online publications, forums, conference proceedings, research articles, and clinical trials.

Types of external data sources to leverage for a true HCP 360

Twitter/X, LinkedIn, YouTube and other social networks where HCPs actively share and discuss the latest research and medical advances.

2. Online medical journals and publications:

 Peer-reviewed journals and articles that provide insights into latest scientific discoveries and HCP opinions.

 Platforms like Sermo and Doximity where HCPs discuss clinical experience and share knowledge.

 Getting a comprehensive view of latest research and clinical trials happening across different existing and upcoming therapies.

The digital data comes with a set of challenges

 Ensuring compliance with regulations such as GDPR and HIPAA when collecting and using external data.

 The data is largely unstructured and difficult to source. Sourcing this data individually can be technically challenging and resource-intensive.

There’s a lot of inherent noise in the digital data. Cleaning it requires advanced data science infrastructure and understanding. Even with the sophisticated infrastructure, it’s a herculean task to ensure data collected is accurate, relevant and up-to-date.

The volumes of data are enormous, requiring significant investment in maintenance and processing.

 has made significant investments in building Auxo, a proprietary HCP 360 combining the best of both, internal and external (digital) data sources for life sciences companies. Auxo helps companies in unlocking the true potential of HCP 360. Here’s how:

1. Auxo sources data from 100+ sources, integrating at the NPI level, ensuring a comprehensive view of the HCPs.

2. Advanced NLP engine removes the noise, tags every information accurately and uniquely to the HCPs.

3. Enables life sciences companies to integrate their valuable data with Auxo’s digital data and perform sophisticated analytics serving the needs of various functional groups.

4. Helps with the development of custom solutions that align with the specific needs and goals of the organizations, improving their commercial strategies.

By leveraging the capabilities of Auxo, life sciences organizations can fully realize the potential of HCP 360, transforming their approach to HCP engagement and gaining a significant competitive advantage.

How are you planning to navigate these complexities to realize the benefits of comprehensive HCP 360?

The next-gen HCP360: Revolutionizing pharma engagement

In today’s fast-paced and ever-evolving landscape of the life sciences industry, the role of Key Opinion Leaders (KOLs) has never been more crucial. These individuals wield significant influence and play a pivotal role in shaping the direction of the industry. In this article, we will explore the significance of KOLs in the life sciences, the traditional methods of identifying them, and the transformative impact of AI-driven approaches in KOL identification.

Key Opinion Leaders are individuals who possess specialized knowledge, expertise, and influence within a particular field. In the life sciences industry, KOLs are instrumental in driving medical advancements, influencing clinical practice, and fostering innovation. They are the bridge between research and practical application, providing valuable insights, participating in clinical trials, and guiding the development of new therapies and treatments.

Why KOLs are Particularly Important in Life Sciences

In the realm of life sciences, where the stakes are often life and death, the input of KOLs can mean the difference between success and failure. They help companies navigate complex regulatory landscapes, ensure patient safety, and contribute to the development of effective treatments. Their expertise is not only sought after by pharmaceutical companies but also by healthcare providers, policymakers, and patients looking for guidance on the best available treatments.

Historically, life science companies relied on a combination of patient treatment data and subjective, on-ground knowledge to identify their KOLs. While these methods yielded valuable insights, they had limitations. The process was time-consuming, potentially biased, and lacked the ability to uncover emerging leaders in the field.

The Changing Landscape of KOL Identification

The advent of artificial intelligence has brought about a paradigm shift in KOL identification. Companies can now harness the power of AI to analyze vast datasets, including clinical trials, research publications, medical journals, and even social media activity. This data-driven approach allows for the identification of KOLs with a higher degree of accuracy, uncovering experts who might have previously remained hidden.

Moreover, changing market dynamics and the integration of digital channels have given rise to a new breed of influencers – Digital Opinion Leaders (DOLs). These individuals use digital platforms to disseminate medical knowledge, making them influential in the age of information and connectivity.

The Advantages of AI-Driven KOL Identification

The AI-driven methodology for KOL identification offers several advantages over traditional approaches. Firstly, it is more objective, reducing the potential for bias in the selection process. Advanced Machine Learning (ML) and Natural Language Processing (NLP) models help decide parameters for exact use cases by weighing selection criteria in a logical fashion. For example, clinical teams can focus on clinical experts, medical teams on research focused providers and marketing teams on high value physicians.

Secondly, it is efficient, capable of processing vast amounts of data quickly and accurately. Traditional approaches took anywhere between 6 months to a year in addition to cumulated knowledge of field teams to determine accurate KOLs. Latest approaches that use models such as Non-Negative Matrix Factorization (NNF), Gaussian Mixture Clustering, KNN, isolated forests and others; can identify KOLs at the click of a button.

Thirdly, it is dynamic, adapting to changing trends and identifying emerging leaders in real-time. Firms used to refresh their KOL lists once a year and continued to rely on dated information. This has changed with AI-driven approaches. KOL lists can now be updated in real time and are based on latest information that is refreshed at as small intervals as a week.

Lastly, it can uncover DOLs who may be influential in digital spaces, expanding a company’s reach in the digital age. Physicians have shown venerable digital adoption and are increasingly voicing their opinion on social media platforms. This information is important for life science firms to include in their digital strategies to remain ahead of the curve.

For life science companies, transitioning to an AI-driven approach for KOL identification requires careful planning and integration. It begins with a thorough assessment of the company’s current practices and data sources. Companies must invest in the right AI technologies, tools, and talent to harness the full potential of AI in this domain.

Additionally, companies should collaborate with data scientists and AI experts to develop customized algorithms that align with their specific needs and objectives. Regular monitoring and refinement of these algorithms are essential to ensure accuracy and relevance.

In addition to the above benefits, AI approaches are more cost effective and can lead to enhanced collaboration between companies and identified KOLs/DOLs. It can help teams foster stronger and more productive relationships.

The incorporation of AI-driven methodologies for KOL identification is a game-changer for the life sciences industry. It not only improves the accuracy and efficiency of the process but also enables companies to adapt to the evolving digital landscape. Embracing this transformation can position life science companies at the forefront of innovation and ensure that they continue to deliver cutting-edge treatments and therapies to those who need them most.

Advantages of AI-Powered KOL identification in life sciences

Are healthcare professionals (HCPs) actively engaging with our digital channels (websites, emails, social)?

How does digital engagement compare to traditional interactions with sales reps?

How often are HCPs interacting with our content and resources online?

What percentage of HCP interactions through digital channels lead to desired outcomes (e.g., product inquiries, samples requested)?

How do these conversion rates stack up against our pre-commercial benchmarks?

Is our content resonating with HCPs, and are they finding it valuable

Are specific types of content (e.g., clinical data, educational resources) performing better than the others?

Are we actively gathering feedback through surveys and feedback loops to gauge HCP satisfaction with our omnichannel approach?

Can we identify primary pain points and areas of improvement that HCPs highlight?

What is the correlation between omnichannel efforts and actual sales?

Are there measurable revenue increases attributed to our omnichannel efforts?

Can we forecast the long-term impact of current/ongoing omnichannel endeavors?

These are some of the questions that can serve as a starting point to assess the effectiveness of your omnichannel implementation. We recommend setting up a solid baseline as you start implementation, defining actionable KPIs, and establishing a robust monitoring system. This approach ensures that you make informed decisions and set up your omnichannel program on the path of continuous improvement.


Measuring the effectiveness of Omnichannel programs: A question on the minds of many life sciences executives

In the fast-paced and data-driven landscape of the Life Science industry. organizations encounter persistent challenges is the fragmentation of data across various systems and sources, hindering the creation of an accurate and complete picture. Master Data Management (MDM) emerges as a strategic solution to address these challenges, offering a comprehensive approach to data management that ensures accuracy, integrity, and validity. In this article, we will explore how MDM strategies play a pivotal role in enhancing data harmonization within the Life Science sector. 

Master Data Management establishes a unified data landscape by addressing the issue of scattered data across disparate systems. When data is scattered across disparate systems, inconsistencies and inaccuracies may arise, leading to flawed decision-making processes. MDM consolidates data from various sources, presenting it in a consistent manner across departments and job roles. This uniformity ensures that everyone interprets the data in the same way, fostering better collaboration and understanding of the overall data.

Enhancing Data Quality for Informed Decision-Making:

Accurate and complete data is the foundation for informed decision-making. MDM facilitates decisions by providing users with a reliable foundation for data-driven decisions. By cleansing and validating data, MDM reduces the risk of outdated information or errors, ensuring that decisions are based on up-to-date and trustworthy data. This not only minimizes the potential for miscalculations but also strengthens the overall data management strategy.

Real-Time Information for Timely Decision-Making:

In the dynamic landscape of the Life science industry, timely decision-making is paramount. Master Data Management supports real-time integration into CRM systems, ensuring that representatives have access to the most recent and accurate information about healthcare professionals, organizations, and market trends. By streamlining data processes, eliminating discrepancies, and empowering representatives with up-to-date information in CRM systems, MDM ensures that timely decisions become a cornerstone of success in this dynamic sector.

Mitigating Risks Through Comprehensive Data Analysis:

MDM goes beyond just managing structured data; it is a game-changer for business users in analyzing large volumes of data. By leveraging MDM for comprehensive data analysis, users gain a

deeper understanding of the market trends, identify potential risks, and implement proactive measures in the industry. This capability not only enhances risk mitigation but also positions organizations for success in the dynamic and competitive landscape of the life science sector.

Master Data Management emerges as a critical component in addressing the challenges of data fragmentation and inconsistency in the life science industry. By promoting data accuracy, integrity, and validity, MDM empowers Business users to make informed, timely, and proactive decisions that drive positive outcomes. As this sector continues to evolve, embracing MDM strategies will not only enhance data management but also position organizations for sustained success in an increasingly competitive market.

Master Data Management: Catalyzing Data Harmonization in the Life Science Industry    

For pharmaceutical firms, the first drug launch is critical. Without a strategic and technical blueprint, the launch can be challenging.

What can pharmaceutical manufacturers do to achieve a high degree of success without risking millions of dollars?

Commercial teams in pharma firms actively seek data-driven answers to build a reliable launch plan. To support this, technology teams must decide on the technology stack, modules to outsource, and steps to be prioritized. They’re not sure where to start and how they can get it right the first time. These high-impact decisions need to account for scalability, data security, and governance, further adding complexity to the launch strategy.

Recently, one such pre-commercial oncology brand that was struggling to build a reliable on-cloud commercial data infrastructure, reached out to ProcDNA for help.

ProcDNA experts helped the company dodge the most common mistakes. This saved the company millions of dollars in investment while accelerating their decision-making for building a clear and reliable commercial data infrastructure strategy.


Our first step was to involve clients’ business teams in setting up roadmaps for short, mid, and long-term implementation. This included listing detailed business questions that they need answers to and defining what success meant for them. Teams often start by creating a stand-alone analytics sandbox to analyze sales and claims data and answer business questions, without realizing that it introduces unnecessary silos into the journey. Analytics teams usually get so used to the sandbox environment that it becomes hard to shift or adapt when the final infrastructure is ready. To help our client avoid this trap, we used our proprietary EDL framework and created the sandbox within the Azure Data Lake environment. Our client also wanted to create a digital-native marketing strategy and wanted to keep all their marketing data in-house. With early directives like these, the company can successfully prepare to build a robust data infrastructure. The team developed the Azure data lake to house all types of marketing content. We then integrated DataBricks into the architecture to enable advanced data science capabilities allowing real-time communication with the MarTech platforms.

2. Adopt modular, best-of-breed platforms


Analytics and advanced data science market is filled with a wide selection of tools to suit different situations. We help our life-sciences clients filter out the noise and choose the right tools for their needs. Our goal is to source best-of-breed tools that are easy to integrate into your overall data infrastructure. Highly modular data architecture allowed our client to use a combination of licensed tools and open-source components that can be plugged in and out as needed. To bypass any complications that may occur in the application layer, most of the integrations with other analytics and visualization tools were done using APIs.

3. Reduce waste with Common Data Layouts (CDLs)


To generate consistent results across distinct functions and to reduce operational overheads, ProcDNA experts created Common Data Layouts (CDLs). The devised Common Data Layout (CDL) allowed the teams to create reusable assets and encouraged the culture of reusability in the organization. Further on, our experts helped the client conceive a list of Critical Business Questions (CBQs). The CDLs were exposed to the application layer using a Role Based Access Control (RBAC) system to control the level of data access for each of the analytics sandbox, advanced analytics, or visualization layers.


During the lifecycle of a commercial product launch, large volumes of data pour in from multiple channels in a short space of time – product sales, patient claims, call activity, sampling, speaker bureau, branded/unbranded websites, emails, digital campaigns, and others. While it is recommend to design the pipelines in advance with dummy data, companies must also consider scalability to manage these parallel data streams that flush data into the systems in big spurts. To sufficiently manage this situation, data infrastructure teams should be experienced with varied data sources specific to the pharmaceutical industry rather than simply treating it as another IT implementation project. They should test the pipelines with realistic data attributes and volumes. In our experience with multiple life-sciences companies, the absence of real-data testing often leads to failure in meeting critical launch KPIs even weeks after the product launch.

By following the above 4 best practices for building a robust on-cloud commercial data infrastructure, life-sciences companies can better support new brand launches. With a sophisticated data infrastructure built to scale, data and commercial teams can generate insights rather than worrying about data operations.

Are you struggling to build a reliable commercial data infrastructure to support your first launch?

The healthcare data landscape continues to evolve, driven by cloud computing and the increasing technical expertise of business users. While these exciting changes empower users with greater access to data, they also necessitate a fresh data strategy and governance perspective to support key data-driven decision-making.

Modern data governance is not merely about control and access. It fosters collaboration and promotes a data-driven culture. There is a need for:

Encouraging open communication and collaboration between IT and business teams.

Providing analysts with the necessary tools and access while maintaining security and compliance.

Equipping stakeholders with the skills to understand and interpret data effectively.

The weakest link in data-related processes is always the human factor. 

Every story has three sides: your side, my side, and the truth

. Data can also be interpreted in different ways, leading to conflicting conclusions.

For example, different users may use different approaches to evaluate a metric, leading to discrepancies in results. Let's say User A, new to the team, needs to evaluate the 'Total revenue generated by the sales of Brand A.' He uses the Xponent dataset to calculate revenue as TRx (Total Prescriptions) multiplied by the average price per unit. However, this approach may not be ideal.

On the other hand, User B, who is more familiar with the business, relies on Specialty Pharmacies (SP) dispense datasets to evaluate any sales-related metric for reporting. These datasets typically contain more accurate information on actual sales transactions.

The disparate data sources cause a mismatch in results obtained by both the users, leading to confusion and potential misinterpretations of 'Total revenue generated by the sales of Brand A.' While it is well known that Xponent data is projected owing to its limited coverage, it is recommended to use SP dispense data for evaluating sales metrics. User A might not be aware of this nuance or the availability of SP datasets in the first place; the absence of a single source of truth makes these reporting errors prevalent.

Similar challenges are very common across teams. To address these challenges, there is a need to prioritize:

Documenting data sources, definitions, and calculations is paramount.

Establishing agreed-upon definitions and metrics across the organization is crucial.

Clearly defining roles and responsibilities for tech and business teams for data maintenance and updates.

Guiding interpreting and utilizing data to drive business outcomes.

In the constantly evolving data landscape, effective data governance is not a luxury—it's a necessity. By embracing collaboration, empowering analysts, and prioritizing clear documentation, organizations can unlock the full potential of their data and drive meaningful business results.