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Traditional data warehouses have limited utility in today’s healthcare ecosystem because they often can’t process the types of unstructured data that give pharmaceutical organizations a competitive edge. Rigid data storage systems and old-school modeling no longer cut it. Pharmaceutical firms need a modern data infrastructure that’s capable of handling structured and unstructured data, sufficiently scalable and technologically able to support advanced analytics, resulting in actionable insights that can enable commercial teams to make data-driven decisions in real time. 

We recently caught up with ZS Principals Kapil Nayyar and Niroop Singh to discuss why data warehouses are dying and how pharmaceutical sales organizations can benefit from moving to new data infrastructures.

Q: What has led to the “death” of the traditional data warehouse?

A: If you think about how the landscape has changed, data warehouses are facing significant challenges to meet all of the new needs that commercial teams are throwing at them. They’re too rigid for constantly evolving data ecosystems. They can’t scale to accommodate the huge volume of data and the new variety of data that’s around, which is pushing up the cost.

Also, many of the data sources coming in are semi-structured and unstructured. It’s not something they can easily ingest. And there is little to no native support for advanced analytics, machine learning or languages like R. Meanwhile, commercial teams are using the power of big data technologies that harness the paradigm shift that has occurred in the last 10 to 15 years. 

Q: It seems that many of these changes revolve around the types and amount of data that have become available, as you’ve mentioned. Please discuss how this has impacted the need for change in the way that organizations use data. 

A: With data warehouses, historically, the data was tightly guarded, and it would sometimes take weeks and months to get new data in the system. And the business case for why you needed that data would be almost dead by the time you got that data in the system. 

That’s why you need to evolve your data warehouse into something that’s flexible, agile and scalable. It needs to take in terabytes of data without worrying about IT system budgets and procurements. And it needs to support a variety of data, whether it’s a social media feed coming in or a clickstream, or a ton of call recordings from your patient support call center. You need the ability to ingest all of that data and be able to make sense of it through techniques like advanced analytics and machine learning. 

When you’re launching new brands, you’ll require a ton more data to analyze your competitor landscape. You can look at dark data that nobody thought would ever add value and get small insights. And a combination of all of those small insights could lead to big insights—and a big edge over your competitors.

Q: Can you give us a scenario in which these advances can benefit pharmaceutical sales teams? 

A: By processing call center recordings, analytics systems can provide new insights into patient behavior and help with patient adherence. You could leverage the concept of dynamic targeting in which your targeting would always be on. Your system is constantly monitoring HCP interactions and patient journeys. Whether it’s sales, marketing, patient diagnostics, claims data, etc., it’s processing all of these various types of data, dynamically selecting the best physicians every week to engage with to get the best outcome—because you only have so many reps, and those reps have only so much time. 

Your system could be dynamic where it’s giving you a target list and you provide feedback. Did you pick the right targets? Meanwhile, the system will monitor the reps’ responses and become smarter so that you get an even better target list the next time. With this concept, you can leverage the reps in the field to push up sales very quickly, compared to their previous static target list, which didn’t refresh for three months or more. 

It’s especially important for specialty pharmaceuticals or rare-disease companies where patient engagement and adherence is a key driving factor. It also helps engage the right physician at the right time based on where patients are in their journey. For example, once a patient gets to a diagnosis, they get a blood test, and the blood test report has a certain threshold that shows your therapy would work very well for that patient. You also know the doctor is going to see that patient in another few weeks. That would be the perfect time to have that conversation with the doctor rather than calling him a month later, as in the past, with static lists and finding out that you missed your window because he has already prescribed something else. 


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Topics: big data, sales force effectiveness, Pharma, Analytics, Sales operations, data warehouses, insights, sales enablement, machine learning, Impact Summit, Impact Summit 2017