I call this my data dress. I wore it to talk to a group of continuing medical education professionals about--you guessed it--data.
Continuing medical education is challenged by three basic barriers:
1. Who is going to support the education (pay for it)--most often the answer is pharma--but should industry be solely responsibly for reframing an outdated framework?
2. This is how we have always done it ("if it ain't broke, don't fix it")
3. Upton Sinclair quote:
“It is difficult to get a man to understand something, when his salary depends upon his not understanding it”
Another advantage to a diversified client portfolio? You can be honest and tease out the threads of possibility. If one stock tanks, you have other relationships to grow and bring value and expertise.
This past week I was reminded that friendships are only as solid as the last round of collaborations and industry support. It is amusing because I don't live in that world anymore. Trust me, it isn't hand holding and singing Kumbaya in health economics, policy, or clinical medicine either but I honestly don't typically see the ruthless self-serving interactions. In fact, many people I hadn't seen in too long stopped by the lecture or grabbed me in the lounge to say hello.
Continuing medical education isn't the "only as good as your last thing" industry. I did have one HEOR co-presenter approach me at a table of influential conference attendees and introduce herself to my companions as blah blah, PhD--as a dig at my lack of PhD. I do have a MSc where I studied population genetics and a Doctorate of Chiropractic where I treated patients. Not sure how the PhD took over the I Can Do This Better space but their grip is slipping as multiple perspectives are valued to drive the change we need in healthcare.
My point here is we need to be less territorial. I will keep repeating my favorite definition of innovation. Innovation happens in the gaps between disciplines--not by navel gazing and self promotion.
This is the time when the enterprise has to adjust its expectations and its analytics modus operandi. If pipeline problems exist, they need to be fixed. If quality problems exist, they need to be diagnosed (data source quality vs. data analysis quality). In addition, an adjacent possible approach to insights needs to be considered and adopted.
Looking adjacently from the data set that is the main target of analysis can uncover other related data sets that offer more context, signals and potential insights through their blending with the main data set. Enterprises can introspect the attributes of the records in their main data sets and look for other data sets whose attributes are adjacent to them. These datasets can be found within the walls of the enterprise or outside. Enterprises that are looking for adjacent data sets can look at both public and premium data set sources. These data sets should be imported and harmonized with existing data sets to create new data sets that contain a broader and crisper set of observations with a higher probability of generating higher quality insights.
The process of data analysis is often fraught with silo’d context i.e. the analyst often does not have the full business context to understand the data or understand the motivation for a business driven question or understand the implications of their insights. Applying the theory of adjacent possible here implies that by introducing the idea of collaboration to the insights generation process by inviting and including team members who each might have a slice of the business context from their point of view can lead to higher valued conclusions and insights.
Combining the context from each of these team members to design, verify, authenticate and validate the insights generation process and its results is the key to generating high quality insights swiftly and deterministically.--KUMAR SRIVASTAVA, CLEARSTORY DATA