ML Enabled Commercial DataMart
Reliable single version of the truth
The Datamart holds hundreds of historical, factual, and predictive attributes about every HCP/account in your therapeutic universe. Historical and factual data includes characteristics of HCPs and various metrics calculated per drug, per line of products, per therapy, etc., at various time periods – last quarter, last year, etc. To enable highly accurate personalized analytics, it also includes aggregated longitudinal (anonymized ) patient level data at the HCP level. The factual and historical data is used to generate new, predictive data, using the AI/ML analytic engine to calculate propensities of HCPs to prescribe, to switch, to churn, etc.
Purposely built to enable data-science and advanced analytics for commercial life sciences. The DataMart facilitates automation and re-use of models to ensure long-term, repeatable use of methodically developed predictive models rather than requiring new models upon change of data.
Systematic data preparation takes in data from any available source, internal or external to the organization, as well as from the enterprise’s data warehouse, cleansing, mapping, integrating and transforming data to ensure the relevancy of data driven insights. Stale, non-reliable data often drives poor decisions, and hence, data integrity and governance are essential to achieve valuable insights.
Key challenges addressed by the commercial Datamart:
- Operationalizing disparate data sources– Integrating data and putting it into operation requires substantial effort by multiple stakeholders, as well as high costs and long implementation times. It must be a continuous effort to stay relevant
- Integrating the Business logic into Datamart– The organization and pertinent industry business logic is dispersed and difficult to maintain, making it hard to use in ETL and analysis workflows. Integrating the business logic into the Datamart is the only way to ensure calculated metrics are standardized as well as transparent
- Automation– Automation allows native support for AI & ML, saving manual data preparation processes by IT and data specialists, to enable a marketer-controlled system.
