Insight by: Debbie Lin
We are in an era of “Show me the Data.” Healthcare companies are in a data acquisition land grab. Making the data meaningful and actionable is bigger challenge. It is where the real value lies. In his recent post, David Whelan discussed the importance of showing how data can be meaningfully used and the need to avoid data silos when building healthcare solutions. For resource strapped early stage startup companies, the strategic decisions they make around how to best create actionable data insights will be the real value differentiator that sets them apart.
Early stage companies often find themselves at a crossroads. Do they focus their efforts on acquiring customers and “Going Wide,” taking in as much data as possible from as many disparate sources as possible, or alternatively, do they “Go Deep,” leveraging existing channel partners to gather a deep dataset around one subpopulation, one disease? Faced with limited resources, companies can’t do both. In fact, if a strategic path is not chosen early on, companies may divert valuable resources in their efforts and waste valuable time and money.
I recently found myself in a friendly strategy debate while advising an early stage company on what data to gather and how best to use the data to develop its value proposition. The company has a very compelling vision, to be a diversity focused precision medicine company aimed to better understand genetic biomarkers from one ethnicity. Using a patient facing app, the company would engage these patients, focus on a few diseases of interest, collect blood samples, conduct genomic sequencing and find disease associations to identify potential genetic variants. These variants would serve as drug targets for highly personalized medicines.
The debate focused on two critical paths. Should the company acquire data by ingesting any and all data from any person who might qualify, no matter the disease or ethnic subtype? Or does the company focus by going deep and honing in on a very specific disease, finding the exact drug indication and carefully parsing out the prevalence of the disease in the subpopulation?
Attracting a wide customer base and gathering genomic data from a patient set with many diseases and many different ethnic subsets would naturally be much more difficult to find drug targets, in the short run. The sample size needed to find a disease-variant association would need to be very large. In other words, because of wide genetic variability in a population, the dataset that would have to be assembled would have to be much larger to be able to extract scientifically actionable drug targets. The complexity only grows when multiple diseases are involved.
On the other hand, a key success factor would be to demonstrate that the company could attract and retain a wide customer base. If the company could gather data from patients at scale, this would have substantial demonstrable value. Launching a marketing campaign and building a product that gains trust and resonates with a wide audience is not a simple feat.
The second strategy entails honing in on one small subpopulation and a specific disease state, sequencing a fraction of the set of patients who meet stringent criteria and identifying variants that could be potential drug targets. Following this path, the company would ideally leverage its existing channel partners to acquire a highly curated dataset of one disease and patient population. This would demonstrate that the company is capable of finding a subpopulation of interest to join, conduct sequencing, doing the analysis, interpreting the data and linking the data to a potential drug target of interest. While accessing and gathering genomic data from a more focused subpopulation may not be the main roadblock, sequencing, interpreting and identifying the specific genomic variants which would qualify as “good” drug targets would be a significantly difficult.
What matters most? Which strategy is best? In fact, the answer is that it depends. While investors and potential partners might have different preferences, the two different strategies showcase very different core competencies. A customer acquisition strategy using strong marketing and sales, a smooth onboarding and good post engagement support processes are the core strengths required in the former, while the latter demands capabilities that are pharma-centric and clinical in nature.
To be successful, the company needs to have a clear picture of how their data insights can enhance healthcare outcomes. Second, a company will need to understand its core strengths and weaknesses. Only then will it be able to make clear decisions on how it can leverage those strengths to generate clinically actionable insights from data.
