A specialty biotech approaching first commercialisation. Twelve people across brand, medical and market access. No data engineer, and a board that had been promised the first cycle would be measurable.
What we did, in order
Data Foundation went in during week one, because the target list was a spreadsheet three people were editing independently. Resolution across the three feeds took nine working days and moved usable target coverage from roughly two-thirds to 91%. Everything downstream depended on that number moving first.
Field Effectiveness followed in week three. At twelve people covering a national footprint the question was less about balance and more about workload — making sure nobody carried more priority accounts than they could reach in a cycle. The payout curve was simulated against two access scenarios before the plan went to the team.
The eight weeks before launch were rehearsed against synthetic data. Two handoffs needed work: access decisions were not reaching the target list, and enrolment data had no route back to the field. Both were straightforward to fix once the dry run made them visible.
Where it landed
The first cycle closed on time with a measurable read, which was the board's actual requirement. Two of the four accelerators are now run by their own team. We continue to run the response modelling quarterly, which is the part that benefits most from a specialist.
The aim was never to build them an analytics department. It was to make one unnecessary for another two years.