In this special guest feature, Brian Irwin, VP of Strategy at SHYFT Analytics, takes a look at the three market dynamics driving life sciences organizations to evaluate new data analytics strategies and technologies as they transform into value-based care delivery models. He leads strategic account development and partnership initiatives while serving as a key strategist for the company. SHYFT Analytics is a leading cloud analytics company within life sciences. The company plays an integral role as the industry continues to undergo dramatic transformation to deliver more personalized and value-based medicine. Brian has over 12 years of experience in a variety of sales and leadership roles within the life sciences industry. Areas of impact and focus have included organizational leadership, executive account management, and strategic enterprise development. Most recently, Brian served as the President and Managing Director at Informa Training Partners, a company focused on Clinical and Managed Market training solutions devoted exclusively to pharmaceutical, biotech, and med device companies. Additionally, Brian spent 9 years with Takeda Pharmaceuticals N.A. in positions of increasing responsibility, leadership, and organizational development. Brian holds a BA in Biology and Natural Sciences from St. Anselm College.
Life sciences organizations recognize that Big Data is both an opportunity and a challenge for their entire industry. However, the strategies and systems, processes, and platforms in place today are not successful and cannot contend with the demands of a rapidly evolving healthcare industry. As total spending on medicines globally reaches the $1 trillion level annually and with no end in sight to rising costs, there is tremendous pressure across the healthcare ecosystem to improve outcomes and prove value. Core to making healthcare more efficient, measureable, and patient-centric is the ability to integrate vast data resources available across this ecosystem and translate them into meaningful, actionable insights.
The demand for timely and improved use of these data creates pressure across the various channels of healthcare, leaving manufacturers, payers, and provider groups particularly vulnerable to the big data deluge. Tasked with making sense of the exponential volumes of patient-level clinical and financial data, these organizations must also capitalize on opportunities to inform both clinical and commercial strategies simultaneously. A demand for data access across the enterprise, a changing competitive landscape tied to intense cost pressures, and the rapid influx of Real World Evidence (RWE) data is forcing the hand of every healthcare entity. Their vast network of data silos – historically housed in rigid, brittle, inaccessible systems – are no longer fit to serve as the backbone of operations in an increasingly dynamic and often unpredictable marketplace.
Let’s take a closer look at the three market dynamics driving life sciences organizations to evaluate new data analytics strategies and technologies as they transform into value-based care delivery models.
1 — Data Demands across the Enterprise
The rapid proliferation of technology and the overall shift towards patient engagement has generated an unprecedented amount of clinical and commercial data. However, overburdened internal resources have their hands tied with even gaining access to these data as well as archaic reporting processes. Historically, getting data out of these silos and into the hands of decision makers across the different facets of a company’s operations took weeks, even months. To make matters worse the ‘reports’ that were developed and delivered for review were often incomplete, lacking the right data or the right detail to truly inform business decisions. Executives had two choices: Accept the information as they were or ask for modifications and wait another month for the final result.
Today it is clear that pharmaceutical companies no longer have the luxury of time; waiting for insights, which are subpar at best and inaccurate at worst, risks any potential first mover advantage that could be gained. Without a faster, more effective way to manage data across the enterprise, life sciences companies cannot garner insights quickly enough to stay competitive.
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SOURCE: Inside Big Data
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