Digital Analytics & Pharma
OK. I’ll say it. I know many are thinking it:
Data can be a distraction. Data can be overwhelming. Data can be just ... too much.
Do you agree?
Data can be a distraction. Data can be overwhelming. Data can be just ... too much.
Do you agree?
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The promise of predictability is the holy grail of our time. Seeing into the future with a basis of fact. Through studying history and its trajectory, we can predict the future. Of course, we mix into the fold all aspects we can think of and in a way, then sit back as the information presents itself on a ticker tape.
So let’s talk potential. Digital and health technologies have the potential to predict health needs. Breakthroughs, in fact. Don’t we agree however, that a breakthrough in health is inevitably up to the patient and consumer? The idea that we have a vaccine, for instance, is only as good as those who had been vaccinated. The revolving conversation around adoption, adaption, compliance, etc. triggers thoughts about the marketing researcher’s dilemma of the act of the purchase versus the purchasing intent. The intent might be very high, but the actual act of the purchase might be a very different story. So how is the predictable crystal ball so effective and promising? Old school phrase of “garbage in : garbage out” ties back to the data put in equals the quality of the data put out. The margin of error can be daunting if the information put into the analysis is not correct, or ‘clean.’ Many have suffered frustration at the controls of the spreadsheet built on inaccurate information. The analytical pros would refer to this as ‘the data going in must be of “being of quality.”’ It's common knowledge that clinical studies are highly managed. It became popular knowledge as we sat news-side to learn of the COVID vaccine and its path to commercialization. 30,000 people were chosen as part of the clinical study for each vaccine by each of the different manufacturers. Half were given the dose and the other half were not, in a blind study, that led to the FDA’s Emergency Authorization. It had a set of parameters to measure with structure, framework, governance, processes, and the systems in place. |
Predictive analytics uses statistics, data mining and modeling and machine learning that analyze current facts and historical data points then makes a prediction about the future.
s it practical to approach this statistical methodology with believability of our future? The answer is: yes and no and it depends. It mainly depends on what we are attempting to predict. In business, every industry can benefit in some way with data analytics. Financial analysis, inventory control and value chain are a few business opportunities that come to mind. Other activities may include production, marketing and after sales services; assisting to manage operational costs, identify the proper technology to enhance operations, and gain customer insights. The use of strategic pricing models and other data driven marketing techniques blend together to maximize profit in a highly competitive environment. The true holy grail is that it may just lead to cultivating life-long customers. 
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In many ways, predictable analytics has been part of humankind since primitives were drawing on sides of rock inside caves marking weather, seasons, herds, and other patterns for survival. As homo sapiens continue to evolve and recognize the business opportunity, maintaining a focal point on the personalized, value-driven, outcomes-focused approach would create a trajectory of success. We want things fast; we want things now and we want things to be accurate. Done correctly by using the right tools, businesses can transform using data in the perfectly right way.
As this topic is much larger than a few words, it will be our continual exploration.
As this topic is much larger than a few words, it will be our continual exploration.
