Big Data Governance for the Life Sciences Sector

Companies in the life sciences sector are, at heart, interested in what all businesses are interested in: remaining profitable, defending against competitors, protecting internal assets and establishing and maintaining a stellar reputation. But what sets companies in the life sciences apart is their primary focus on improving patient outcomes — and the driving force for improving patient outcomes is increasingly Big Data. Time and experience have proven again and again that those who fail to manage Big Data put profits, prominence and reputation at risk.

Data governance can be thought of us the “rules of engagement” for deciding who has a right to access or use data, who is accountable for certain data and how data should be governed and kept secure. An ideal governance strategy will capture, curate and store data while also providing a means for searching, sharing, transferring, analyzing and visualizing data, all while maintaining regulatory compliance. According to Informatica, roughly 70 percent of any data project now involves simply managing data before analysis can even begin.

Knowing whether or not a particular drug or treatment will cure a patient or cause severe side effects is primarily a data governance problem. Maintaining relationships with physicians and providers while also demonstrating compliance with FDA and state regulations is primarily a data governance problem. Protecting intellectual property to stave off competition from generic drug companies, protecting patient data to comply with HIPAA regulations, carefully documenting research from clinical trials to reduce waste and duplication of effort, even understanding how weather in far-flung locales will affect supply-chain demand — these are all primarily data governance problems.

And perhaps the biggest problem of all is the sheer volume of data being generated. According to some estimates, the total amount of data related to all aspects of the life sciences industry is 150 exabytes — or one million terabytes — in 2011, and the rate of data generation has been increasing by 1.2-2.4 exabytes per year. Big Data from both internal and external sources can provide life sciences companies with a greater understanding of their customers and enhance clinical decision-making, disease surveillance and clinical analytics. But proper data governance — characterized by a consistent architecture, standardized data assets and real-time aggregation — is the key to preventing chaos and providing valuable insights and meaningful predictions.

It’s no surprise that given this complexity, data governance in the life sciences is not free from stigma. Many life sciences CXOs — especially CFOs, with their finger constantly on the pulse of the company’s financial viability — associate data governance with a hit to their company’s speed, agility and bottom line. Yet even for those who see the value of data governance, exponential growth of data in the life sciences and healthcare expected in the coming years will render traditional methods of governance and analytics insufficient and outdated, if not sooner than later.

Data governance, therefore, is key to providing the kinds of insights that can allow CXOs to make effective decisions, whether it’s to redefine a business model, partner with other companies or optimize a supply chain.

Supply chain optimization, for one, is crucial amid a drug discovery and product development climate marked by spiraling costs, longer development timelines and increasing savvy among consumers about their ever-growing care options. Data governance techniques, when properly applied, can build in flexibility and responsiveness within the supply chain, drive down the time out of development or even alert life sciences CXOs to the need to stop a project early in the development cycle. Smart, regimented access to data combined with effective metadata, inventory tracking and other governance tools can help life sciences companies shift from a stock-based paradigm to one based on demand — and even a model that bypasses the wholesaler and goes direct to the consumer.

A robust data governance approach can ultimately prove to be a selling point from a sales and marketing perspective, as well. The move toward whole-patient care means that patients — like life sciences executives — are aware of the value of data to aid their decision-making. Providing visibility and access to patient data can set a company apart from competitors that take a more ‘paternalistic’ approach by siloing this data. However, given the complexity of regulating patient data, a governance plan must take into account all factors involved, from adhering to federal regulations to providing role-based access to determining which quantities of data would be most meaningful to consumers.

In the life sciences, data governance isn’t only pertinent to customer-facing functions such as sales and marketing. Predictive modeling — derived from existing molecular and clinical data — is playing an ever-increasing role in identifying molecules that can be developed into safe, effective drugs. Bringing a new drug to market, however, remains one of the riskiest (and potentially most costly) moves a company can make. A mere 10 to 12 percent of new drugs progress from the early phases of the drug discovery process to the consumer market. A safe, effective drug can cost billions of dollars to develop — and requires, on average, 12 years of development, from start to finish.

For a drug to make from the lab to the pharmacy — and for companies to profit — corporate leadership must provide strategic access to molecular databases that can be exploited, in real-time, by researchers in the lab as well external partners such as contract research organizations (CROs). To achieve this, life sciences companies must integrate into their data governance strategy a cyberinfrastructure with data & application integration software that can process, merge and store multi-structured data from a variety of sources. Also crucial are data governance tools capable of providing common metadata definitions derived from multiple sources to ensure that governance is consistent throughout the drug discovery process.

From a pharmacoeconomics perspective, data governance can help life sciences companies identify, measure and compare the costs and side effects of a particular drug vs. its potential impact on a defined population or on society as a whole. These modeling studies use existing clinical and/or epidemiologic data from various sources both within and outside a specific life sciences company in order to project future outcomes. This requires, however, that access to such data be provided to those involved in making financial projections. Effective data categorization — eg. direct medical, direct nonmedical, indirect nonmedical, intangible, opportunity, and incremental costs — can be built into a data governance plan early on in the development cycle to inform economic valuation and decision-making going forward.

Data governance, when administered properly, can enable companies to make more accurate predictions about the profitability of a given drug based on historical analysis of similar agents and the current and likely future regulatory environment, as well as market conditions, the effect of patent law on the drug and overall demand for the product. From a regulatory point of view, algorithmically-driven data governance can also link laboratory and clinical data to identify potential threats to safety and serve as an important tool in risk management.

According to a survey by PWC, 62 percent of pharmaceuticals and life sciences executives have changed their organization’s approach to big decision making as a result of data and analytics. That same survey found that 39 percent of executives rely on data and analytic inputs to make important decisions, while only 23 percent rely on their own experience and intuition alone.

For many executives in this sector, then, the challenge lies in obtaining high-quality, accurate and complete data — but not in quantities that will actually have a detrimental effect on decision-making if it becomes too time-consuming or cumbersome to govern. Successful data governance approaches will provide fast, scalable technology that can integrate vast amounts of data from various sources and in various formats, including treatment data, clinical trials, electronic medical records, even social media.

product-overview-slide“For data governance in this sector to succeed, life sciences companies must collaborate with technology providers and regulatory agencies to develop ways to evaluate data in a meaningful way while also providing robust quality assurance and workflow management,” says Ajay Sarkar, CEO of RoundWorld Solutions. “Our Big Data 360-degree tool can help those in the life sciences and pharmaceutical sector balance costs and compliance with innovation and enterprise data integration.”

“Our template-driven tool,” continues Sarkar, “provides checkpoints along the way toward more complete, trusted and timely data, allowing CXOs and those involved in data governance to streamline data collection, provide logical access to data, reduce the amount of time needed to analyze the data and ultimately optimize the time allowed for turning data into decisions — decisions that will steer those in the life sciences sector toward greater profits, notoriety and patient outcomes.”

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This article was provided by:
Tiffany Fox
Public Information Officer
RoundWorld Solutions