Acorn AI’s Commercial Data Model: How the Strength of Experience (and the Right Technology) Can Impact Your First Drug Launch
Lots of companies build analytics platforms. It’s a growth industry over the past few decades. Some companies are generalists, working across many categories by solving a couple basic data problems. Others are bespoke, offering consultants who come in with very specific tasks in mind.
Acorn AI’s Commercial Data Solutions team blends both approaches while also offering a distinct level of expertise. We’re specialists in a sense—our collective experience in the pharmaceutical industry spans decades and dozens of product launches. We work solely in life sciences, and we bring vast industry knowledge to every project. We were able to build the Acorn Commercial Data Model precisely because we understand the pharmaceutical product lifecycle. Its elemental features—a central data engine, data-agnostic validation standards, a data mapping network—were born of our involvement in product launches over the decades.
But we’re not like the specialists who come in and build custom solutions, then walk out with all the know-how. Consultants can do wonderful work, but problems don’t emerge on a consultant’s schedule—problems can emerge when consultants are unavailable, leaving you stranded with a very specific problem.
Acorn AI’s Commercial Data Solutions team takes a different approach. We built an agnostic data analytics platform that works on the general principle of a centralized data engine. It ingests all the data a pharmaceutical company generates and maps them to parameters specific to pharmaceutical launches, while remaining agnostic to the type of data ingested.
We accomplish this with a data validation system that cleans data streams and standardizes data sets. Within our specific niche, we built the Acorn AI CDM to be a generalist platform so that it answers very specific questions with unerring consistency.
When we built the Acorn AI CDM we brought together our collective insights. We talked about the central data engine and data validation standards. We talked about mapping that could avoid the time-suck of constructing data warehouses for every new launch. We thought about the output, how it might look, but also how it might be adapted to fit other analytics programs a company might want to use.
Beyond the technology development, we brought expertise in pharmaceutical sales cycles, territories, and anomalies. Other launches have taught us that conditions can change rapidly in the marketplace. A medical journal report might cause a re-think of target markets. Certain health care providers may not be distributing a product at the same rate as others. This required a data model that could flex and change to remain valuable and, without altering the data model, keep revealing insights.
The Acorn AI CDM incorporates all of that flexibility and thinking on the fly. It’s built to take on all data challenges, by people who spent their careers analyzing the industry.