Life science companies are under intense pressure to discover and leverage drugs and genomics to improve patient and population outcomes while simultaneously managing costs. Increasingly they are looking to utilize the massive amounts of structured and unstructured data they own to achieve these goals through big data (predictive analytics) analysis.
With the many challenges facing the Life Sciences industry today – navigating health care reform, delivering innovation and value, complying with regulatory changes, optimizing supply chain, and operating in a globalized economy organizations are looking to implement business intelligence solutions that can allow for a more agile approach to their operations.
In order to properly utilize and implement these BI solutions, the life science enterprise must have data that is consistent, usable, accessible, accurate, reliable, and secure across the enterprise. This poses a challenge for life sciences organizations with the massive amounts of sensitive data they hold spread across multiple legacy systems. An organizations success in managing and utilizing that data begins with building a framework to establish a comprehensive data management process. The first step of this framework is data governance.
Recent life sciences big data analytics efforts have focused primarily on applying advanced analytics to improve the efficiency of their research and development. It is the industry belief that the costs related to conducting clinical trials and bringing a new drug to market, which are estimated at well over $1 billion, this is clearly unsustainable in the long term. Applying insights from big data sources, such as genetic and claims data, could reduce the cost of trials by enrolling patients most likely to respond to the treatment, improving trial design and reducing the length of trials.
One of the best-known examples of the use of big data analytics is in genomics, the study of human genes and their functions. While the science of gene sequencing is not new, the ability to profile tens of thousands of genes, in hundreds or thousands of patients, has fundamentally changed life science research. Scientists no longer have to focus on just a few individuals, or just a few genes. Now researchers can compare gene behaviors across thousands of individuals and look for similarities and patterns. One of the biggest challenges facing life sciences researchers is managing the enormous amount of data being generated. This torrent of data must be tamed if we are to realize the full potential of applying big data analytics to Life sciences and in turn to healthcare. This is where big data analytics will fundamentally change the life sciences research game. Through the ability to amass and analyze staggering amounts of data, researchers can now leverage a big data strategy to lead them to breakthroughs. Combining the massive amount of genetic data with patient medical records leads to new insights and discoveries that were not possible before.
Life sciences companies are in the midst of a rapidly changing reimbursement environment (Insurance and Healthcare Reform) where payers are changing the incentives for participants in the health care system based on the demonstrable value they deliver. Proper rewards for their innovations will require companies to generate real world outcomes data that show their drug is a significant improvement over current standards of care. This means companies will have to learn to capture and analyze data from patient related social media channels, payer claims and EMR (Electronic Medical Records). This effectively application of data analytics on these unstructured data sources provide new insights and opportunities to take advantage of that data by engaging with payers much earlier in the R&D cycle, which could improve market access.
To achieve this new leverage with big data and analytics, a partnership between the business and technology groups is essential. Data Governance is not only an IT function; it must be a cooperative effort between management, IT, and the end users of that data. It includes the people, roles, assets, procedures, policies, and standards needed to successfully administer and manage a company’s information resources which are spread across disparate systems and owned by different departments. By utilizing less traditional data and data sources, such as customer sentiment data derived from social media and other digital channels, organizations can more effectively analyze activity across marketing channels, put a more human face on the brand and break down information silos between various internal entities like R&D and commercial operations by, for example, applying insights on patient preferences to future development efforts.
The biggest challenge Life science companies will face as they adopt big data strategies into their operations will be in data governance, data security and patient privacy concerns. HIPPA laws on privacy are a zero tolerance policy and the penalties are severe. Being cognizant of this fact from ground up design of data analytics strategy along with robust data security procedures will mitigate this concern. However, the benefits that come with an effective, robust and holistic big data management strategy are too great for any company to ignore the advantages of big data analytics.
RoundWorld Solutions unique 360 degree view of Data governance provides a model for administering data in a standardized way across the Life sciences organization, which reduces expenditures on data issues and results in better quality data that meets clinical, business, privacy, regulatory and compliance requirements.
RoundWorld Solutions’ 360 degree view of Big Data in life sciences that can help that C-Level executive get the right information, delivered by the right tool, to the right resource at the right time. Our unique 3 layer Big Data tool set – a flat file format based data collector and validator, executive templates, and our top layer visualization tool allows manufacturers to consolidate and access their data to truly unlock the power of Big Data and capitalize on the vast knowledge that they have acquired and collected in their siloed applications.
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