Why data analytics should be Pharma’s new core competency
Defining your company’s core competency—what you do really well—can set you apart and secure a trajectory of success. Pharma’s core competency has evolved over the years. Initially, it was research and companies that were fortunate enough to discover major blockbuster drugs enjoyed profitable periods of market exclusivity.
However, once the pharma market consolidated into large corporations, research alone no longer produced sufficient profits. Pharma companies were faced with a rise in generics and a limited timeline for making proprietary sales of blockbuster drugs. As a result, the core competency in the pharma space naturally transitioned from research to marketing. Pharma companies had to focus on achieving greater ROI by promoting their products.
Now, it’s time for another shift: from marketing to data analytics. Just as pharma companies once had to develop marketing as a core competency to remain competitive and profitable, they now have to become experts at data analytics to maintain a competitive edge. A competitive advantage is now given to whoever can analyze the most data and quickly derive a common, minimally invasive orthopaedic procedure used to treat a number of joint injuries insights by making intelligent, meaningful connections.
Let’s examine how companies can cultivate data analytics as a core competency.
There’s no lack of data — it just needs to be brought together
To cultivate data analytics as a core competency, pharma firms need to realize that the problem is not a lack of data. There is an abundance of data everywhere. Pharma is constantly inundated with a massive inpouring of data that goes beyond information obtained from clinical trials. Firms receive both structured and unstructured data from a variety of sources, ranging from doctors notes and data captured through mobile devices to structured clinical research reports and patient questionnaires.
The first step in honing their data analytics capabilities is for pharma companies to become more adept at aggregating this data all in one place. Data that is distributed across multiple servers and locations ends up getting siloed wherever it’s stored — whether in doctor’s offices, on researcher’s computers, or elsewhere. If analytics teams aren’t able to access the full breadth of data that’s available on a given subject, they are slower to arrive at novel insights and are more likely to generate faulty hypotheses.
Since the volume of data is so large and multifaceted, pharma companies struggle with searching and connecting all the available information. As Gaurav Tripathi, our CTO, has explained, “Due to difficulties in searching for medical data, researchers are often unaware of other works or even breakthroughs pertinent to their work. As a result, they continue spending time heading in the wrong direction.” This is why a tool like iPlexus can be so useful; it empowers researchers by immersing them in an ecosystem wherein all relevant data from a wide variety of sources can be analyzed at once.
But simply collecting all the data into one place is just the beginning. To piece together these disparate data points into meaningful insights, analysts must master the three V’s of Big Data: volume, velocity, and variety. Volume depicts the amount of data coming in. Velocity describes the speed at which new data is added. And variety indicates the various ways that structured and unstructured data can be represented.
Aggregation isn’t enough — context normalization is the key
To build data analytics as a core competency, pharma companies must do more than simply aggregate all of the available data. Companies must learn to normalize the context of these various data points — to structure the raw, unstructured data — before it can be effectively analyzed.
As much as 80 percent of the world’s data is unstructured, and real-world data is often represented in mixed, messy formats. Therefore, it is critical for pharma leaders to invest in technologies that map the right unstructured datasets together and translate these datasets into structured formats. Trying to manually structure unstructured data is an expensive and time-consuming process that today’s pharmaceutical companies cannot afford if they want to be competitive. Instead, companies must embrace AI and machine learning technologies that can automate the data normalization process.
For example, one major challenge faced by pharma is finding and selecting the right molecules for manufacturing a new drug. Many molecules belong to the same “chemical space”—an entire group of potential pharmacologically active molecules. AI-powered analytics software can empower teams to make faster predictions about promising compounds (or combinations of compounds) for treating diseases. The same AI-powered analytics can also be applied to other stages of the pharma value chain, such as helping to define the scientific story around a drug launch or predicting the optimal marketing channel mix across various therapy franchises.
More innovation, faster
By leveraging AI and machine learning, pharmaceutical companies can aggregate higher volumes of data and can analyze a greater variety of data, all at a much higher velocity than has ever been possible before. Only by mastering these three V’s of Big Data can pharma companies truly make data analytics their new core competency. When they do, researchers will be empowered with the insights they need to make “Aha!” moments happen.