D Cube Analytics

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

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

To make any successful forecasting model, the first step is to identify the key supporting metrics required to build an accurate forecast model. The conventional data sources (e.g. chart audit, survey data) present only a small part of the very big picture, where a lot of indirect factors affect the share of the drug, thus limiting the insights and scope of the analysis. Real-world data can be the supplementary evidence, if not the substitute for conventional data sources, in increasing the confidence of forecasting inputs.

In this webinar, we will be discussing how to efficiently merge information from both real-world data and secondary research data and build robust KPIs which can support development of an efficient forecasting model.

Presenters:

• Vishnu Prashanth, Principal Consultant, D Cube Analytics
• Ajitha Surendran, Consultant, D Cube Analytics

The increasing costs of drugs in the US is a growing concern for payers. Customarily, payers were less willing to confine the market access of drugs that address perilous conditions like cancer; however, as newer and more efficacious treatments have become available, payers have begun to concentrate on strategies to abridge the expense of specialty drugs. Along with payers, provider organizations are also playing a significant role in driving therapeutic choice and managing the cost of specialty drugs. To respond to this evolving landscape, pharma companies need strong and novel analytics approaches to understand how payers and providers are driving drug utilization.

In this webinar, we will be taking you through how to target the right payers and providers and optimize contracting strategies for each of them by leveraging formulary, sales and real-world data. We will also be discussing how pharma companies can enhance contracting strategies across a portfolio of drugs by analyzing the utilization management patterns of payers, level of competition, market dynamics, etc.

Presenters:

• Keshav Kabra, Principal Consultant, D Cube Analytics
• Ajitha Surendran, Consultant, D Cube Analytics

Use of advanced analytics has been widely accepted by pharma companies in both drug discovery and development phases as well as for developing commercialization strategies. With the growing richness of new-age data sources like Real-world data, the application of advanced analytics has become relevant in place of conventional analytical methods.

In this webinar, we will be taking you through the application of advanced analytical techniques in building deeper understanding of a therapeutic area by analyzing the patient, provider and disease related factors that are driving treatment choices. We will be discussing the complexities that exist in Real-world data sources and how to systematically navigate through the same in making them analytics-ready. We will also be covering best practices in utilizing popular unsupervised classification techniques.

Presenters:

• Srikanth Katasani, Principal Consultant, D Cube Analytics
• Swetank Gupta, Associate Consultant, D Cube Analytics