In this special guest feature, Sagar Anisingaraju, Chief Strategy Officer at Saama, discusses how new approaches to big data analytics are helping the pharmaceutical industry address major shifts toward value-based-care. Sagar has helped Saama architect an advanced analytics engine and solutions to help life sciences companies manage data across all stages of the value chain for business outcomes. A winner of the CSO of the Year Award from Innovation Enterprise, Sagar is passionate about helping customers derive a differentiating advantage with their data assets.
Current Data Complexity
While innovation drives the life sciences industry, increasing complexity, cost pressures, and a strict regulatory environment are making new discoveries increasingly difficult for companies to achieve. Given these factors, pharmaceuticals providers are seeking better ways to understand hidden trends, improve operations management, identify relevant insights to reduce costs, increase revenue and drive innovation.
New Big Data sources and analytics technologies have the capability to tackle these issues in disruptive ways while also answering the hard questions being asked by pharma stakeholders across the enterprise. But significant challenges remain—from R&D to marketing to commercial to managed care, data often can not be corralled into a form that has the quality, currency and flexibility to base life-altering medications on.
Pharma as an industry is notoriously data-intensive, having reams of data, which include clinical trial data, structured and unstructured electronic medical records, lab test results and claims data. As the data becomes more heterogeneous, parsing the information is ever so critical. IT managers can easily become lost in the multitude of metrics that relate to various business use cases, including safety signals, treatment pathways and population characteristics.
So far, many (up to 75%) of big-data initiatives have not produced the hoped for results, as they were not specific enough to the business use case, could not handle the data deluge, or were based on a technology that was looking to be applied, but could not deliver as a standalone solution.
A Hybrid Analytics Prescription
The key approach to successful implementations for large pharma businesses needs to be one that provides the combination of the best technologies with the best data and analytics team available (hybrid solutions). Blending technology with human expertise is critical to drive meaningful results from any data lake. Combining human expertise and the power of technology to aggregate, clean and consume mass volumes of data is the foundational element of any big data implementation, and it is essential for enabling predictive, real-time and prescriptive analytics.
In the life sciences, new analytics capabilities combined with rich human experience in data science help companies to accurately track the entire pharmaceutical lifecycle – from research to commercial launch, growth and maturity – and proactively develop drugs that address unmet market needs. This enables the business to rapidly leverage clinical and commercial data for actionable insights that enhance drug performance and overall organizational efficiency.
With a firm foundation built from a hybrid approach, pharmaceutical companies can analyze and orchestrate data to create very specific and targeted insights at previously impossible speeds. Often the first step to mastering these new capabilities is to invest in a data management strategy. This will grow in importance as data volumes continue to grow exponentially, and as the variety of data changes with new technologies.
Improving the Quality of Care
The biggest opportunity for life sciences companies lies in digitizing the value chain of a new drug as it relates to improving the quality of care given to patients.
The Affordable Care Act provisions move the focus to paying for quality of care and improved outcomes, and outcomes-based contracts rely on real world data for drug reimbursements. Thus, real world evidence is becoming the center of the conversation between payers and manufacturers, who are both racing to gain competitive insights into effectiveness and efficiency of treatment options.
The proliferation of IoT and genomic data is a major opportunity for pharma companies to expand the pool of data, and better personalize care and treatment. However, these new data sets have the potential to cause major headaches for technology providers and IT departments, which can significantly slow down the time to insight – rendering many of the insights obsolete. The pharma companies that will be successful in this new era of connected device data will be the ones that have the human expertise, and technology, that can hone in on specific insights at speed to deliver real-time value.
A New Future
Next generation Big Data implementations don’t rely on technology alone, and hybrid solutions are the new standard to deliver insights with speed and specificity. A recent Forrester report highlighted the ability for hybrid analytics solutions to reduce time-to-value and significantly improve ROI to beyond 400% and payback in less than 6 months – including life science use cases during the clinical development phase and after drugs enter the market. By analyzing and orchestrating data to create insights at previously impossible speeds, companies have greater power than ever to developer cheaper, safer drugs.
Sign up for the free insideBIGDATA newsletter.