Webinars

Pharmaceutical companies have volumes of market data coming from numerous third-party providers, as well as internal sales force data. Instead of being overwhelmed by the torrent of data, teams are now able to leverage AI-powered analytics to receive real-time insights to solve all the complex questions about their brand’s performance. Teams in areas such as sales force effectiveness, market access, and payer analytics are utilizing Tellius Smart Analytics Frameworks to quickly build consolidated views of data, speed data analysis with AI-driven automation, and simplify access to insights with search-driven intelligence.

Together, we will dive deeper into the analytics journey: data manipulation, ad hoc exploration, and uncover actionable insights to help increase your brand's market share. By the end of this webinar, you will be able to leverage AI-powered analytics to answer the most complex business questions, as well as implement best practices on receiving buy-in from upper management.

To make any successful marketing and customer engagement efforts, the first step is to have a well stitched Customer Master data with rich and accurate attributions. The conventional MDM Systems though have certain Fuzzy match capabilities , they still need significant Human Data Stewardship to audit merges and publish the master data. The Human interventions required to enable strong data stewardship introduces a lot of variability due to skill levels and increases costs significantly to scale up. The Data stewards leverage several resources typically unavailable to MDM systems such as Google searches, external websites, and looping through corrected Names, Addresses etc. which makes the data stewardship process difficult to reproduce within the conventional MDM assets.

In this Webinar, we will be discussing about a drastically different approach to Data Stewardship where an Assistant application complements the MDM app and leverages several technology advancements as well as AI/ML to accurately replicate or optimize the Human based stewardship process.

Sammed is a Technical Product Manager at D Cube Analytics, has 15+ years of Industry experience in Data and Analytics, Data governance and syndication. Sammed has managed various initiatives in the areas of building Pharma MDM platforms, Data Warehouse, and advanced personalization.

Pradeep is a Principal Consultant Data Scientist at D Cube's India office. He brings close to 12+ years of pure play analytics and data science experience across various industries like Pharma, Hospitality, Telecom and Retail. He has expertise in laying out complete analytical roadmap for the business. He has extensive knowledge of developing machine learning solutions to help support client in their decision making and process automation.

Presenters:

• Sammed Kumar, Technical Product Manager at D Cube Analytics
• Pradeep Kumar, Principal Consultant Data Scientist at D Cube Analytics' India office

Personalized communication is something the HCPs have come to expect. To do that, pharma sales reps need to know their customers well. In order to know the customer well, they need data. Enterprise pharma companies, however, have many data resources and lots of data at their disposal. In fact, there is a data explosion. So what do you do? How do you quickly provide field reps with contextual insights (not data) about their customers to have meaningful personalized conversations? How can sales ops teams leverage incentive compensation data to drive sales motivation and help sales reps surpass their quotas? Using real-time insights, how can commercial life sciences leaders skip the long, boring reports?

Early treatment is a fundamental principle of MS disease management to help lower the risk of disease progression and prevent disability. However, patient awareness of the positive impact of early treatment and long-term continuous therapy remains an issue in MS disease management. Understanding key factors that delay new patient starts on therapy are critical to reaching and educating patients and getting them on the right therapy early on to prevent disease progression and keeping them adherent to prevent disability.

While pharma has been using real world data (RWD) to generate real world evidence (RWE) for clinical trials, post-marketing, and R&D for decades, the emergence and applicability of RWD to the sales, marketing, and commercial side of the house is now ramping up. Many organizations are exploring the possibilities related to targeting, segmentation, sales force effectiveness, and adherence, allocating growing budgets to acquire, analyze, and visualize data. How can pharma manufacturers leverage those resources to ensure they’re engaging HCPs and patients in a timely and precise manner to increase early start of MS treatment? That’s where advanced analytics in RWD translate into actionable insights that can drive positive outcomes.

In this webinar we’ll discuss how applications in advanced analytics and machine learning, can leverage clinical expertise in analyzing RWD to create predictive profiles of patients who meet the criteria for early MS treatment. In the same way, advanced analytics i can be applied to identify HCPs treating those patients along the journey and predict the best moments to share MS treatment information with those HCPs at the point of care – within their workflow. This ability to reach HCPs supports a proactive approach to treatment since they can be reached using an omni-channel approach, providing information beyond the EHR and even when they’re not with the patients.

Presenters:

• Eze K. Abosi, Head, Real World Evidence Solutions
• Adam Almozlino, Vice President, Data & Products, OptimizeRx
• Mark Bard, Co-Founder, The DHC Group
• Rebecca Love, RN, MSN, FIEL

Life sciences and Bio Pharma companies, small to large scale, in recent years have realized the importance of insights for effective drug launches in the market. It is no more the muscle power of the organization's sales and marketing team that guarantees the success of a new launch but is the ability of the organization to gather and leverage actionable Patient Insights, Physician Insights and Payer Insights effectively that makes the difference.

Companies procure data sets like CRM data, HCP/HCO Master data, Specialty Distribution data, among others for such use cases. Typically, these data sets are stored in data lakes or data warehouses. Building an effective, secure, and scalable data store is challenge which many enterprises are trying to solve.

Further drilling down, it becomes evident that there are 4 main aspects on which an effective data lake or data warehouse must deliver on:

• Data Integration & Preparation
• Data Democratization
• Data Lineage
• Data Ops & Governance

Based on analyst reports on an average it takes 6 to 9 months to build a data lake. Companies which are looking to launch will need the data infrastructure in a few weeks and require an alternative approach to achieve this.

In this webinar, we will talk about how a framework driven solution brings together all the aspects mentioned above in a single platform while specifically providing acceleration for life sciences and pharma companies.

Presenters:

• Karthik Mohan is working as a Product Architect at D Cube Analytics with 14 years of experience building Cloud/On Premise based Data Analytics Platforms and Products.
• Subidya Bharati is working as an Associate Product Architect at D Cube Analytics with 12 years of experience specializing in BI Solutions and Data Management within Cloud and On-Premises Platforms.

In the next few years, Enterprise-wide adoption of AI and Cloud would dramatically increase and the ability to build/utilize AI solutions will move from highly specialized data scientists to other Data Citizens as well. Pharma organizations should adopt a wholistic approach in democratizing their AI and Cloud assets to ensure ease of adoption, seamless governance, and strict compliance in order to be successful. Some of the barriers to this are:

• Overhead effort in installing, setting up, maintaining, and connecting with enterprise data ecosystems with strong security and compliance.
• Fragmented AI/ML Ecosystem, inconsistent technology usage, lack of trust, duplication of effort and difficulty in collaboration.
• Leadership’s lack of ability to understand, estimate and monitor the Analytics costs across people and technology.

In this Webinar, we will be discussing about how a wholistic approach can be adopted by enabling a unified governance ecosystem which strikes a balance between governance and ease of use enabling organizations to adopt AI/ML freely.

Presenters:

• Sammed Kumar is a technical product manager at D Cube Analytics, has 15+ years of Industry experience in Data and Analytics, Data governance and syndication. Sammed has managed various initiatives in the areas of building Pharma MDM platforms, Data Warehouse, and advanced personalization.
• Samuel Jaideep is an Associate Product Architect at D Cube Analytics, has 7+ years of Industry experience in Web Technologies and Cloud Engineering. Jaideep has been involved in architecting and development of various Web Applications for multiple clients in the course of time.
• Meda Ajay Krishna is an Associate Product Architect at D Cube Analytics. He brings close to 7+ years of experience in Cloud – DevOps and product planning. He has experience in designing network and security for various applications. He has experience in designing Data Engineering and Pharma MDM platforms.

In the epoch of thriving Biosimilars launch phase, ever wondered why they haven’t been as successful as their reference Biologic product? The conventional brand launch strategies fail when it comes to Biosimilar launches as there is a huge gap in terms of awareness about Biosimilar products & Bioequivalence, Targeting, and Messaging. Therefore, it is important for the Pharma companies to understand the key drivers and the associated stakeholders that determine the success of a Biosimilar launch.

In this webinar, we will be discussing how to leverage RWD to build robust analytical framework for targeting the most influential stakeholders and positioning the Biosimilar for a strategic commercial launch.

Janani is Consultant at D Cube Analytics, she comes with 6+ years of experience in supporting Market Planning and Brand teams with analytical solutions to make strategic data driven decisions. She has extensively worked on Oncology, Neuro and Immunology therapeutic areas covering a spectrum of business problems - Pre/Post Drug Launch Strategic Analysis, Patient Chart Audits, Physician-level data analytics, Sales Force Effectiveness and Primary Market Research

Daniel is an Associate Consultant at D Cube Analytics, who has around 5+ years of experience in supporting the analytical needs of the regional and global brand teams in their quest towards building-up next-gen strategies across launch and pre-launch space. He brings in expertise on the Inflammation therapeutic area and has solved business problems pertaining to sales and commercial analytics, Payer and Provider analytics, and also Patient treatment dynamics by leveraging syndicated data sources and extensive market research.

Presenters:

• Janani Damodaran, Consultant at D Cube Analytics
• Daniel Britto, Associate Consultant at D Cube Analytics

Pharmaceutical industry is rapidly adopting Machine learning and Advanced Analytics to enhance their commercial strategies. To make any successful marketing and customer engagement efforts, it is necessary to know your customer/physician you are targeting and predicting new prescriptions/ patient’s growth gives us an edge while planning brand strategy. The ML applied engagement model can help in segmenting the prescribers into Brand Champions, Loyalists, Churners which can further help the marketing and SFE teams to effectively formulate messaging tactics accordingly. Primary objective of this model is to devise strategies to retain market shares among churners and reach out to them to get them migrate to a business driving segment. In this Webinar, we will be discussing about machine learning approach to segment the prescribers into Churners, Loyalist and Champions. This information can be used for adjusting call plans & targeting exercise, field force communication plan, personalizing communication across various channels, used to drive next best engagement communication models, etc.

Ankit Kohli is Data Science Lead in the space of AI, Machine Learning and Big Data helping organizations across globe in enabling the application of Advanced analytics. With over a decade of his professional experience, he is the lead in data sciences at D Cube Analytics. Prior to this he has worked in data sciences business engagements at Absolutdata, EXL and Cognizant (MarketRX) across industries implementing analytical frameworks to business strategies to augment revenue streams for the businesses.

Dheeraj Kathuria is a Consultant at D Cube's India office, has 6+ years of Industry experience in Data Analytics. He has analytics and data science experience across various industries like Pharma, Retail, FMCG, Automobile and Digital OTT platforms.

Presenters:

• Ankit Kohli, Data Science Lead in the Space of AI, Machine Learning and Big Data
• Dheeraj Kathuria, Consultant at D Cube's India Office

Insights at the intersection of claims and integrated delivery network data can empower pharmaceutical and medical device companies to identify and prioritize high-value sales targets and opportunities. With many internal layers, connections, and networks invisible to outsiders, the modern Life Sciences universe has created new challenges for brand teams at pharmaceutical and medical device companies.

In recent years, Life Sciences organizations have operated in a challenging healthcare environment. Value-based care reimbursement models seek to improve outcomes while reducing costs; patient access to high-cost branded therapies is sometimes restricted; and acquisitions among hospitals and physician practices are now commonplace. One result of the altered operational landscape is a new model for care delivery: the Integrated Delivery Network (IDN).

Join Rebecca Hauck and Anja M. from LexisNexis® Health Care to learn how you can bring clarity to this complex universe by combining IDN provider and facility level data with medical claims volumes to help Life Sciences organizations determine the value of IDNs for a particular therapy area. These insights help transform challenges into opportunities by optimizing business processes, increasing market share, reducing coverage gaps, and improving revenue growth.

Presenters:

• Rebecca Hauck, LexisNexis Health Care
• Anja Maciagiewicz, LexisNexis Health Care

Anonymized Patient Level Data (APLD) has been gaining popularity in recent times in commercial analytics. Since it provides rich longitudinal patient history, it is routinely used to understand patient journey, payor dynamics, and define HCP targeting strategies.

However, this is only scratching the surface when it comes to realizing the full potential of the data asset. Patient Claims Data has many applications across various stages of the product lifecycle, right from pre-clinical stage to loss of exclusivity.

This data can be used to guide strategic decisions such as acquisition of pre-clinical assets and can aid in identifying geographic concentration of patients of interest for enrollment into clinical trials.

Anonymized Patient Data also offers what traditional HCP level Rx data is unable to offer – estimating the “Potential Opportunity” for your product among different providers. Machine Learning models that predict volume of patients in different stages of a disease across different time intervals are useful in targeting HCPs that have the larger volume of specific types of patients of interest. This enables precise targeting of HCPs for sales and marketing efforts, as well as customization of messaging based on prescription and referral patterns, particularly at launch.

When combined with call and promotional activity data, it can be used to measure campaign and message effectiveness, responsiveness to new communication channels, and to design near real-time tactics to gain and maintain market share. This is particularly critical in today’s times when sales reps are unable to meet with physicians in person due to the COVID-19 pandemic.

SRI has unique expertise in leveraging Anonymized Patient Data across all stages of the drug life cycle.

Speakers:/p>

• Sudhakar Mandapati, Principal, Strategic Research Insights, Inc.
• Harshad Chiddarwar, Director, Strategic Research Insights, Inc.
• Ren Zhao, Sr. Engagement Manager, Strategic Research Insights, Inc.
• Gina Nelson, Senior Director, New Product Planning, Gossamer Bio, Inc.