How Unified Data Platform Can Help Automate Workflow in Life Science Companies

Self-service data platforms allow new pharma and biotech companies to focus on ad-hoc analytics to determine how their data may help clinical operations, medical affairs, and commercial teams fulfil objectives.

FREMONT, CA: With a self-service, a unified data platform for all business users, pharma's commercial, medical affairs, and clinical operations teams gains tremendous agility. It provides faster iterations and timely updating of business rules and KPIs for altering launch release cycles. Firms can invest in a single unified data platform instead of investing in separate ETL and data quality solutions for each team. Because our solution uses a single data model, these departments can use the same entities.

The model can track how many people participated in a trial, how many finished it, what medications they used, and how many were healed. To report contacts with Key Opinion Leaders, medical teams would evaluate analytics for this same organization to summarize all clinical trials. It would target patients and doctors. Instead of tracking these metrics with numerous solutions, enterprises can use the unified platform's shared data model.

Optimizing analytics agility and cloud adoption

A complete analytics package relevant to each business unit and automated data preparation via Artificial Intelligence (AI) is the two main competitive advantages in this field. Because it's architected underneath the analytics layer, the standard data model makes it more agile. As a result, pharma teams can utilize several analytics tools without being tethered to specific data models and business requirements that require reconfiguration when the company wants new analytics frameworks.

To achieve optimal analytics agility, teams can use any BI tools or visualizations they like. Also, life sciences unified data platforms provide analytics capabilities for the three main business lines. 

These tools can anticipate and prescribe which physicians to target in specific hospitals. Using a common data model, corporations may target specific doctors at different hospitals for new drugs.

The cost-savings and agility of a single data platform for life sciences are intimately linked. The best solutions in this domain use serverless cloud computing. Typical Big Pharma data management platforms involve upfront purchases of the physical infrastructure to establish and maintain. They also require recruiting people to operate and can take months or years to deploy. With serverless computing, businesses can start reaping the benefits of a shared data model right now, with no upfront capital expenditures.

More crucially, a single data platform in the cloud abstracts all the complexity of mapping data, creating schema, and integrating data from the business end-users. In addition to pre-built connectors for commonly used data sources, the shared data model includes standard KPIs for business teams. Other cognitive computing features allow businesses to establish and alter business rules in natural language swiftly. Best of all, the model is built for the pharmaceutical sector, so companies don't have to spend time changing it for use cases like patient or physician entities. As a result, business teams can customize self-service data preparation to enable the agility required to compete today.

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