Big Data Analytical Platform in Pharma- Need or Must-have?




By DataZymes Team


25 October, 2017

The US Pharma industry is undergoing a massive transformation – technological, financial as well as cultural one in the last few years. On one hand, there is a massive decline in the generic drug prices (with prices declining as much as 59% between Q1 2010 and Q2 2015) and players turning to M&A and consolidation for the top line growth. This has resulted in a worry on whether the projected organic growth rates are sustainable in the future, leading to an erosion in the market cap of large players including Teva, Mylon and Endo Pharma in the recent quarters.

On the other hand, Pharma companies are under the radar of the government. While the Affordable Care Act made the Payers and Providers more accountable for outcomes, Pharma came relatively unscathed from it. In fact, it benefitted from the additional patients who received insurance under the act. In order to move away from the threat of generics, many Pharma companies have pivoted to a specialty drug strategy, making huge gains in the process over the last few years. The business model has moved from a mass market, big blockbuster model to a niche, targeted, expensive highly specialized and complex product model. But the high drug prices have invited a lot of scrutiny from payers, patients and the political community alike since last year’s run up to the US general elections. In the recent weeks, California Governor Jerry Brown signed a drug pricing transparency law requiring manufacturers to give a 60-day notice if the prices are raised by more than 16 percent over a two-year period.

There is an increasing pressure on Pharma to start taking steps towards a value-based business model where it collaborates closely with other healthcare participants, shares the financial risks and gets paid on outcomes.

There is an increasing pressure on Pharma to start taking steps towards a value-based business model where it collaborates closely with other healthcare participants, shares the financial risks and gets paid on outcomes. Multiple Pharma companies have started this journey by signing outcome-based contracts with payers, investing in digital health startups and contracting with providers to get access to de-identified patient data.

But a successful move to value-based model for Pharma has one major roadblock - its broken, medieval IT and analytical infrastructure. Today, Pharma lags many industries in terms of analytical maturity and has not met the needs of its business users. It still deals with data silos, making collaboration between different departments difficult, a must-have to graduate towards a value-based model. The result is that Pharma spends significant time in collecting, transforming, integrating and reporting data, thereby leaving very little time as well as investment for predictive and advanced analytics. As healthcare adopts value-based models and starts dealing with Big Data, traditional patch work and point solutions employed by Pharma to deal with Rx and Sales data alone will not suffice.

Clearly, the need of the hour is an Enterprise Big Data Analytics platform that can help different departments across Pharma such as R&D, Medical, Commercial and Access to come together and get timely access to relevant insights in an extremely consumable form. While there is a natural tendency to start building this internally with large traditional services and consulting companies, Pharma must carefully evaluate before investing millions of dollars and precious time in building this capability. The lessons from the large, unsuccessful Enterprise Data Warehouse projects should not be forgotten.