OR WAIT null SECS
Owning a self-service data platform allows emerging pharma and biotech companies to accelerate past data engineering intricacies and focus on ad-hoc analytics to discern how their data can help them meet objectives for clinical operations, medical affairs, and commercial teams.
All but the largest life science companies are grappling with a conundrum that, unless quickly resolved, will eventually derail them. Business lines in this industry depend on analytics to effectively bring new pharmaceuticals to market. But no matter how much subject matter expertise clinical operations, medical affairs, and commercial teams have, they aren’t data engineers. Complicating the matter is these individuals are hard to find, so hiring these professionals and financing their infrastructure is cost prohibitive.
Consequently, small and midsize businesses frequently outsource the data preparation responsibilities necessary for analytic insight. In fact, Gartner predicted1 that by 2022 data preparation will be a critical capability in over 80 percent of analytics, data engineering, and data integration solutions.
However, relying on third parties for this vital function has its own set of challenges; it is cost intensive, difficult to scale, and delays time to market. Today, many companies know that it’s better to own the data platform that pharma teams need instead of depending on outsiders. Gartner predicted2 that next year, end users accessing a curated data catalog will reap twice the value from analytics as those that don’t.
In 2021, small and midsize life science companies will own their data platforms to support self-service access and business agility at a time they’re needed most. Underpinned by a common data model, this unified data platform is a cost-effective solution delivering a single source of truth for all data—and use cases—across business lines.
The enhanced agility of a self-service, single data platform for all business users produces profound benefits for the three core pharma teams in commercial, medical affairs, and clinical operations units. Collectively, it enables quicker responses to changing market conditions—like those created by COVID-19 — for faster iterations and timely updates of business rules and KPIs for adjusting launch release cycles. Instead of piecing together individual components for data engineering pipelines like ETL and data quality tools, organizations can leverage a single investment in a unified data platform reusable for their different teams. Because this solution is powered by a common data model, these departments can utilize the same entities for their respective purposes.
For a typical entity like a patient, clinical operations teams can employ the model to track how many people were involved in a trial, how many finished it, which medications they took, and how many were cured. Medical teams would scrutinize analytics about this same entity for a synopsis of all clinical trials, giving them information about which demographics a drug worked best for and its rate of effectiveness to inform interactions with Key Opinion Leaders. Commercial teams would assess the same entity to target patients and their doctors. Instead of tracking these metrics with different solutions, organizations can use the shared data model of this unified platform with different analytics dashboards, which is much more cost effective.
The two chief distinctions of competitive options in this space are automated data preparation via Artificial Intelligence (AI) for self-servicing one data model across departments and a comprehensive analytics package relevant for each business unit. The common data model makes the analytics component more agile because it’s architected beneath the analytics layer. As such, pharma teams can use different analytics tools without being tied down to the individual data models and business definitions of any particular tool, which is time consuming to reconfigure when the business requires new analytics frameworks.
Therefore, different teams can use any BI tools or visualizations they prefer to achieve optimal analytics agility. Additionally, unified data platforms for life sciences have analytics capabilities germane to the three main business lines. These packages have predictive and prescriptive capabilities for determining which physicians to target in which hospitals, for example. With this approach, organizations can analyze all their data about claims, patient interactions, and providers—united by a single data model—to target individual doctors at different hospitals for particular drugs about to launch.
The ease of use of a unified data platform for life sciences is inextricably related to its cost benefits, both of which reinforce its agility and self-service capacity. Premier solutions in this space rely on serverless computing paradigms in the cloud. Typical data management platforms in this industry — leveraged by Big Pharma companies — require up front purchases of physical infrastructure to implement and maintain. They also involve hiring people to operate these necessities and can take several months or a year to deploy. With serverless computing, companies use the cloud provider’s infrastructure and personnel to begin realizing the advantages of a common data model right away with pay-per-use operational expenses as opposed to traditional Capex.
More importantly, with a unified data platform in the cloud, all the complexity of mapping data, configuring schema, and integrating data has been abstracted from the business end users benefiting from this solution. The shared data model comes with pre-built connectors for frequently used data sources, pre-built rules for data transformations, and common KPIs for business teams. AI automatically maps data to the model while other cognitive computing capabilities let the business quickly declare and modify business rules in natural language. Best of all, the model itself is centered around the pharmaceutical industry, so organizations don’t have to devote an inordinate amount of time customizing it for pervasive use cases like establishing entities for patients or physicians. The result is self-service data preparation that business teams can personalize for their own needs to support the agility required to compete at today’s business pace.
Owning a self-service data platform allows emerging pharma and biotech companies to accelerate past data engineering intricacies and focus on ad-hoc analytics to discern how their data can help them meet objectives for clinical operations, medical affairs, and commercial teams. These affordable solutions support collaborations between respective departments so they can achieve better business outcomes, collectively and individually. The same platform allows them to see how efforts in clinical operations can be used by commercial teams seeking customers, for example. The improved agility of this self-service access optimizes the analytics necessary to efficiently bring new drugs to market.
Janardan Prasad is CBO and Head of Life Sciences at Lore IO.