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insights LIFESCIENCELEADER.COM APRIL 2014 52 INFORMATION TECHNOLOGY minimizing the number of patients and trials needed to deliver the neces- sary regulatory information, as well as limiting the timeline of trials needed to produce required endpoints allowing for the monitoring and incor- poration of data collected outside of the clinic, including data gathered through smartphones and other device trackers analyzing data to reveal where a drug can positively impact a new population/ indication, allowing for a new, stream- lined trial. Given the rapid changes in technology and the ambiguous regulatory environ- ment surrounding the use of such data, many companies are struggling to meet compliance, privacy, data quality, and other challenges. However, there are sev- eral steps companies can take today to keep pace with, or even leapfrog over, their competitors in harnessing the power of Big Data to improve their R&D; efforts. STEP 1: Establish a clear analytics strategy. The first step in incorporating Big Data into your R&D; operations and decision making is to define an analyt- ics strategy and operating model that includes a center of excellence. The cen- ter of excellence provides a sustainable core to drive the ongoing execution of the analytics strategy within the day-to- T O D D S K R I N A R A N D T H A D D E U S W O L F R A M Near the end of 2013, many in the life sciences industry were looking for clear evidence that the FDA was willing to work with industry to get more needed drugs to patients. Eyes were focused on the "scorecard" of new drugs approved, which for the first eight months of 2013 reached 18. W By T. Skrinar and T. Wolfram 6 STEPS FOR A SUSTAINABLE APPROACH TO R&D; THROUGH BIG DATA 6 Steps For A Sustainable Approach To R&D; Through Big Data hile this number was down from 22 during the same period in 2012, it still outpaced what was a very sluggish approval pace through much of the 2000s. The FDA's most recent trend seems encouraging. The bigger challenge is the sustainabil- ity of the R&D; process itself. Clinical trial costs, in particular, are driving intoler- ably high R&D; expenditures. In the midst of slow sales growth, it is no surprise these costs remain a focus of the indus- try's continued belt-tightening. The industry is demonstrating an interesting range of strategies to make R&D; more efficient, including dispers- ing risk through open innovation, col- laboration, and partnerships, as well as diversifying by targeting personalized medicine and orphan and niche disease markets. But a key question remains: How does one reduce clinical trial costs while still meeting the rising demands of regulators and payers for more data that demonstrates that the drug is a significant improvement over current standards of care? Effective use of Big Data is increasingly seen as the path forward. It offers oppor- tunities for cutting clinical trial costs while providing the type of robust data required for both approval and reimbursement. THE VALUE OF BIG DATA Big Data for R&D; is less about veloc- ity and more about variety, viability, and sometimes volume. The key analytics capability for this data is the ability to visualize relationships and patterns. By combining real-world outcomes data with clinical data and through the min- ing of genetic data and a broader under- standing of regional and population data, analytically savvy organizations can begin to recognize research failures faster, design more efficient clinical tri- als, and speed the discovery and approv- al of new medicines while lowering costs along the way. Whether it is "-omics" data, patient- relevant social media, payer claims, or patient electronic health records, a limitless amount of patient data is now available and is enabling companies to achieve impactful R&D; goals including: streamlining patient recruitment and informing patient selection and enroll- ment to recruit the right patients for trials, as well as excluding patients who are likely not to benefit from treatment or likely to suffer adverse events tying trial outcomes to real-world out- comes data and health economic data 0 4 1 4 _ I T _ B i g D a t a . i n d d 1 0414_IT_BigData.indd 1 3 / 2 1 / 2 0 1 4 1 2 : 1 9 : 4 1 P M 3/21/2014 12:19:41 PM