Independence Day: Honoring Big Data’s Achievements and Strides Toward Healthcare Improvements


On Independence Day, we hope you’re relaxing at a barbecue with friends and family, enjoying the fireworks and buffet of indulgences. Back in the office, there’s still a buzz around big data in pharma, as organizations continue to learn how to best derive value from it. While some experts are citing cautionary tales, others are encouraging organizations to harness the power of big data to shift costs away from IT and toward business empowerment and better results. The question of who will integrate data is still up in the air, while big data is making huge strides in clinical studies and in the fight against cancer.

1. Big Data: 5 Reminders to Keep It Real

By Daniel Gutierrez, published on Inside BigData

The Cold, Hard Facts About Big Data and Pharma

All the news and case studies of big data’s success in pharma shouldn’t dissuade those who haven’t yet stepped into the ring from doing so. In fact, many organizations that haven’t adopted a big data policy or implemented big data in any significant way can learn from those who’ve gone ahead. Over-the-top promises on delivery should be ignored, and instead, newbies are advised to remember that the service isn’t free, it does come at a cost, it isn’t easy, but it’s well worth the journey to capture big data’s potential for organizations.

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2. How Big Data Is Transforming The Fight Against Cancer

By Bernard Marr, published on Forbes

Big Data’s Impact on Cancer

Big data’s possibilities are not just limited to business potential. It’s also creating significant opportunities in advancing the march against long-standing diseases like cancer. The capabilities that big data expounds has now made its attack on the Big C. With the ability to capture data and the analysis that’s currently possible, big data is helping the fight against cancer by improving patient care, outlining risks and eventually, the hope is, that it will generate cures, too.

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3. Who Will Take The Lead On Herding Patients’ Medical Records?

By Dina Gerdeman, published on Forbes

Who Will Ultimately Harness and Integrate Patient Data?

While many patients are likely to have healthcare data in a variety of locations and through many types of testing facilities over their lifetimes, harnessing all of that data into one place is akin to, as Forbes puts it, “throwing a lasso around a tsunami”. While healthcare parties involved agree that integrating medical data is a step in the right direction, it remains to be seen as to whom will take on the very difficult, cumbersome task. Ultimately, the best candidate will be one that wins the trust of both physicians and patients.

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4. The Importance of Real-World Data to the Pharma Industry

By Genevieve Bonnelye, published on PMLive

Real-World Data Driving Clinical Studies

The longtime standard for evaluating product safety, efficacy and driving prescribing methods may be set to take a backseat. With the advancement of real-world data improving health outcomes, randomized controlled trials will continue to inform testing, but big data’s ability to deliver insights into healthcare delivery practices in real time seems to be taking the driver’s wheel in terms of importance and usefulness. As such, pharma is using real-world data in decision-making processes for clinical trials and promoting non-interventional studies.

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5. Get Value from the V’s of Big Data by Using the D’s of Analytics

By David Pope, published on SAS Voices

Moving From Big Data to Big Data Analytics

For any pharma or healthcare organization to get value from analytics, there’s a natural progression of understanding big data to getting insights from that data. To understand what big data is, one must learn about volume, velocity and variety (as well as value); however, to fully embrace the value of big data analytics, a separate lingo exists. Namely, organizations must embrace data (data storage location, management and security), discovery (getting actual value from the data) and deployment (taking action from the data).

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