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While “Big Data” is a great buzzword in management circles, small data (with big problems) never gets much attention.
As an industry, we produce data. Lots of it. It is inherent to what we do and clearly necessary for bringing innovative therapies to patients. While much of our time is dedicated to ensuring quality for the important safety and efficacy data in our submissions to regulatory agencies, the other data we indirectly produce-our operational data-can at times flounder (un-queried for quality) in our clinical systems (or ugh, myriads of spreadsheets). While “Big Data” is a great buzzword in management circles, small data (with big problems) never gets much attention.
Our operational data is extremely important. It is a reflection of what we do and how well we do it. We can use it to evaluate our processes and initiate discussions for improvement. We can use it to forecast and extrapolate trends. We can use it to predict what will happen next and optimize expected outcomes. We can analyze it for hidden patterns that can help direct future decisions. So, what is preventing us from using this rich resource as other industries have? Every major sports organization has invested in analytics to better understand which players will succeed or which plays work best in a given situation, but how can we translate this capability to our own industry to resolve challenges with unproductive clinical sites? Apart from the pharmacokinetic/pharmacodynamic (PK/PD) modeling and simulation that our industry currently utilizes, we fail to prioritize operational data to make better decisions. The question is why? While there are likely many different reasons for this (i.e. lack of a data culture and/or strategy, technology limitations, lack of adequate skill sets, etc.), I would like to focus on a very basic, but important requirement: data quality.
All the interesting things we can use our operational data for relies squarely on its quality. While analyzing data is exciting, unfortunately, cleaning it takes a great deal of time investment. A survey in the New York Times indicated that Data Scientists spend up to 80% of their time in data preparation.1 We may be very familiar with the effort required to lock a clinical database, but we seem to continue to ignore that our other data require the same type of rigor to ensure it is able to be used for measurement and decision making.
So, as we look back on 2019 and look forward to the New Year, I would like to share some data resolutions that I will continue to evangelize throughout 2020 and beyond.
These resolutions are in no particular order as they are all important. And some are certainly more labor intensive than others. A critical underlying component is having a culture that understands the importance of data and fosters supportive behaviors around it. Just like having a partner to exercise with increases the likelihood that you will go to the gym, having a leadership team (equipped with a data strategy) that evangelizes data as an important asset is the most important element for analytical success.
The question is not really if you will implement these, it is when you will need to. In 2020, the life sciences industry will inevitably see new growth in virtual trials, wearable technology, personalized medicine, artificial intelligence and machine learning – and GOOD data is the cog that makes all of this possible. Unfortunately, BAD data can go through these motions, too, and can get you to the wrong conclusion. Other than your people, data is the most valuable asset your company will produce. This year does not have to be the same as previous years when it comes to instilling a new appreciation and understanding of data. You can start today.
Todd Johnson is a Senior Consultant at Halloran Consulting Group, Inc.
1. For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights, Aug 17 2014.
2. Only 3% of Companies’ Data Meets Basic Quality Standards, T Nagle, T Redman, D Sammon, Harvard Business Review, Sep 11, 2017.