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insights LIFESCIENCELEADER.COM APRIL 2014 54 INFORMATION TECHNOLOGY Todd Skrinar is a principal in the Advisory Life Sciences practice of Ernst & Young LLP. He is based in San Francisco. By T. Skrinar and T. Wolfram 6 STEPS FOR A SUSTAINABLE APPROACH TO R&D; THROUGH BIG DATA Companies that master these steps will build a more sustainable approach to R&D; and develop competitive advantages in the life sciences space. day running of the business, and fosters a collaborative environment that gener- ates the necessary tools for both creators and consumers of analytics to extract the greatest value from Big Data. The R&D; analytics strategy will be driven by the needs of the business, not technol- ogy. A strategy motivated by available or interesting tech will too often cloud decision making, not clarify it. STEP 2: Identify the most relevant sources of Big Data. Given the large and disparate amount of data now avail- able to life sciences companies, it is easy for an organization to quickly become overwhelmed. The defined R&D; analyt- ics strategy (mentioned in Step 1) will, by nature, provide initial guidance for why any data source is valuable or not. The data that offers value can be filtered further based on the potential impact it holds for the business. The process then advances from targeting the right Big Data opportunities to an assessment of key factors, including accessibility of the data, security requirements surround- ing the data, and the effort it will take to make the data usable. STEP 3: Master large-scale data man- agement. The capability to appropriate- ly access, pool, and maintain large vol- umes of data from varied sources will, of course, be critical to success. While there is a wide range of tools, technologies, and platforms available for delivering this capability, the appropriate choices depend on the sources of Big Data and the analytics targets identified in Step 2. Assessing the current foundational IT and analytical state will present a clear picture of the steps needed to reach the appropriate level of large-scale data management. STEP 4: Pursue meaningful collabo- rations. The structure and demands of today's healthcare ecosystem mean no one organization can go it alone. Data access is one of the many activities that at times requires cooperation between two or more parties. For example, col- lecting patient information is certainly not something that a biopharma compa- ny can just go out and do. However, what that company can do is gain access to the right information from electronic health record data by partnering with the insti- tutions that are able to collect and main- tain it. Establishing data partnerships with other life sciences companies as well as academic institutions, provid- ers, and payers is key to gaining access to the widest range of Big Data possi- ble. Companies pursuing these partner- ships also need to think "win/win" when determining their positions on intellec- tual property, risk, and resource commit- ments. Success will depend on selecting like-minded business partners and using trusted third parties to support the data- management challenges. STEP 5: Optimize your analytics orga- nization for performance, value, and continuous learning. Improving the performance of R&D; requires a constant search for new insights by combining and analyzing nontraditional data sourc- es. Complacency around Big Data will eventually lead to missed insights, over- looked efficiencies, and an inadequate analytics function. To guard against this, establish a continuous feedback loop to understand the results of analytics and apply them to future analytics efforts. This process requires skills, structure, and management behavior that all drive a culture of continuous learning and improvement. STEP 6: Derive and define your value. Successfully utilizing Big Data is ulti- mately about deriving value from the data in a manner that drives effective decision making and that enables a com- pany to demonstrate the value of its product to patients and to the health- care system overall. After providing the right information to drive better decision making, the biggest challenge remains: articulating both the quantitative and qualitative benefits of R&D; analytics efforts and the downstream R&D; efforts to the appropriate stakeholders, includ- ing internal clinical development and operations teams, as well as payers and patients. In communicating the analytics ben- efits, it is important to have predefined short-term and long-term metrics for assessing the impacts. By doing so, the results are aligned to specific targets and can easily be used to inform the selected audience of the value that R&D; has cre- ated for them. Companies that master these steps will build a more sustainable approach to R&D; and develop competitive advan- tages in the life sciences space. They will be better poised to deliver products and solutions that meet the specific needs of patients — individually, stratified, and at a population level. And they will be able to do this with lower R&D; costs, with a streamlined route to market, and with a clear knowledge along the way of exactly which patients can benefit from the therapies they deliver. L Thaddeus Wolfram is a manager in the Advisory Life Sciences practice of Ernst & Young LLP. He is based in Chicago. 0 4 1 4 _ I T _ B i g D a t a . i n d d 2 0414_IT_BigData.indd 2 3 / 2 1 / 2 0 1 4 1 2 : 1 9 : 5 2 P M 3/21/2014 12:19:52 PM