Data is the new gold for life sciences companies today. It is the most valued currency. It is the fuel that powers the best forecasts. It is the ultimate hedge against the inevitable vagaries of the marketplace. And commercial teams can never have too much of it.
But given the vast amounts of data available – some of which may be the information-equivalent of fool’s gold – it’s not easy to aggregate, manage thoughtfully and deploy data in a manner that: 1) helps life sciences companies predict and act based on what they know today; and 2) equips them to respond and adjust to what might happen tomorrow.
That’s why companies need an agile data management strategy that collects data with the future in mind and commits the commercial operations team to remaining nimble with this data. Such a strategy reduces the risks of making decisions based on outdated information – wasting time, duplicating work and missing opportunities for growth.
In efforts to help life sciences companies agilely mine and manage their data treasure, we’ve identified three steps to establish thoughtful strategies:
1. Tap all the data mines you can
You can’t have too much of the right kind of information. Commercial teams should mine several types of data – including sales activity data, anonymized patient-level data and complementary data such as Open Payments (Sunshine Act) – to ensure they can address potential future issues. Notably, Sunshine Act data serves as a great option when other data sources are cost-prohibitive – yet sometimes companies fail to include these in their data management strategies. Commercial operations teams can turn to this data to better understand a physician’s prescribing habits, identify key decision makers and enrich sales force targets.
2. Consider all data uses
When buying data, commercial operations leaders consider each dataset’s cost, its owner and its intended uses. Often, one set of data can serve many purposes. If the team fails to investigate or realize its full value, however, team members will miss important insights.
Further, the commercial team may need to reference a particular dataset often and for an extended period of time. If the team must purchase this data on a set schedule, it should consider the recurring cost and compare this cost to that of alternative datasets. For example, the commercial team may purchase select, pre-filtered data unaware that, for the same price, it could buy the larger, unfiltered data set that could help address future commercial needs.
Alternatively, another type of pre-filtered data may provide cost savings at launch but lead to additional expenses down the road. For example, the commercial team may only need data for one particular region or customer segment at product launch. If the company decides to expand post-launch, however, the revised sales strategy may require unfiltered, nationwide data and necessitate a new purchase.
3. Let data give you options
No mining effort, expedition or product launch goes exactly as planned. Commercial teams must take time to examine their data both from many vantage points and with upcoming market events in mind. These include when a product enters the marketplace, goes generic or receives a new indication.
Too often, companies forge ahead in lockstep without considering new information that becomes available. Instead, commercial teams should welcome change and embrace the imagination required to anticipate new challenges and pivot strategies. If a competing product is going to enter the market, for example, the team should turn to product volume data and claims data to identify physicians who are likely to switch to the new product. It can then utilize this data to update the targeting and call planning strategy for its sales force to visit these physicians more frequently prior to the competitor’s product launch. Failure to plan ahead leaves the sales force playing defense.
Data management plays an essential role in a life sciences company’s commercial operations. If companies don’t collect as much as they can and deploy it adroitly, they risk wasting resources, making uninformed decisions and missing out on golden opportunities that arise during the product lifecycle.