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Integration and Agility Are Key to Leveraging Big Data in Life Sciences

Kim Shah, Director-Global Marketing & Business Development for the Informatics Business, Thermo Fisher Scientific

Kim Shah, Director-Global Marketing & Business Development for the Informatics Business, Thermo Fisher Scientific

It’s fashionable to say that “Big Data” is transforming this and revolutionizing that, but most industries, including life sciences, are still busy sorting out the hype from reality. The greatest stumbling block seems to be the word “big” because it focuses the discussion too much on database size and velocity and not enough on the more basic question of what are we trying to accomplish. The ends must justify the means.

The word “data” is as much a stumbling block as the word “big” because it can mean different things within an enterprise. CIOs must understand what sources of data are most valuable, how the data will be used and by whom. The key is that there’s no one-size-fits-all approach since each business has its own challenges, so each organization must develop a unique approach.

Is data transforming life sciences? It certainly is, but that’s not new. What is new is how pharmaceutical companies, university researchers and the like are transforming the infrastructures that enable data to be better managed and applied to specific ends. Whether those ends involve proteomics research in the R&D phase or quality control during the drug manufacturing process, data is only as good as the integrated platforms built to produce, analyze and apply it.

The good news for many life sciences companies—and their CIOs—is that for decades many businesses have methodically added technology in preparation for this transformation. They’ve known all along that rivers of data would someday overwhelm most legacy systems. For many, this has led to an accelerated effort to align non-integrated, often disparate systems and technologies to optimize them for discovery on the R&D side and more efficient, but even higher-quality production on the manufacturing side.

To discover new diagnostics and novel therapies and produce them cost-effectively, life sciences companies and, increasingly, their networks of research institutions and contract laboratory partners, need to address the primary drivers of constant business transformation. These drivers, familiar to every CIO, are integration, innovation, automation and business intelligence. Getting these four drivers in sync is the first step in capitalizing on the Big Data opportunity.

Life sciences businesses today require full visibility into operations across the enterprise, from Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES) to integrated Laboratory Information Management Systems (LIMS) . In a transformation-ready enterprise, all of these data-producing centers will have been integrated with each other, allowing for a seamless flow of ‘internal Big Data’ to reach any constituent willing and able to dissect relevant data points and apply that new knowledge to ongoing decision making.

There are, however, still gaps within many life sciences companies where some of this data remains in silos and is disconnected, and this presents an additional layer of complexity when an organization begins to tackle questions related to Big Data.

Having a fully integrated system (e.g., lab data via a LIMS to manufacturing via MES to resource management via ERP) could, for example, give a pharmaceutical company a one-day head start identifying a raw materials' issue in the supply chain, leading to an intervention that saves millions. This may not be what many think of when they hear Big Data, but the fact that critical insight such as this can be mined from starting with a LIMS is actually a more compelling demonstration of how powerful data can truly be.

What a CIO really wants is an agile enterprise, one that is truly business transformation-ready at all times. And this is where the four pillars mentioned above come into play. Across the spectrum of life sciences companies, many CIOs are assessing their readiness, and these are the four areas they are closely scrutinizing within their organizations.

Integration—Integration is critical at two levels. At the laboratory level, scientists need access to the real scientific data to correctly identify any product quality issues or potential environmental contamination as quickly as possible. At a high level, management needs to integrate these lab results with the overall R&D or manufacturing process. This provides true visibility to inform business decisions through executive dashboards built on comprehensive real or near-real time data. When people, processes, technology (and data) are stuck in silos, business agility is impossible.

Innovation—Pharma/Biopharma companies today are looking at Big Data to identify opportunities for improvement, market opportunitites, optimizing costs and improving operational efficiency. Addressing challenges as diverse as accelerating drug discovery to more efficient ways to manufacture product, liberating laboratory data in dashboard form can be a new catalyst for continuous change. A LIMS, for example, provides a centralized way to aggregate all of a laboratory’s critical operational and scientific information and, because it holds the key to solving many of these challenges, is a critical component of any Big Data strategy. The ability to recognize and exploit pathways for innovation is as much cultural as it is process-oriented. By aggregating this information from systems such as LIMS, MES and ERP and centralizing it for all to see, it allows everyone to see the contribution Information Systems make towards organizational change and commitment.

Automation—Automating time-consuming tasks such as instrument calibration, compliance, user training and maintenance liberates more time for science, investing this perishable intellectual capital back into business transformation. Automation creates a conduit for getting even more data into circulation—and more accurately making it more valuable than time-savings alone.

Business Intelligence (BI)—In many enterprises, if a manager or executive wants to see laboratory progress or productivity reports, the IT department has to step in. Today, however, thanks to more mature BI approaches enabled by cloud computing, lab personnel can create real-time dashboard reports that are accessible to managers and executives 24/7 via desktop, tablet or mobile devices. Constant access to information when it’s need; no waiting.

Conclusion

A CIO can’t simply check a box and say they’re Big Data-ready. There are too many permutations involved: is this data critical for Quality by Design (QbD) implementation in drug manufacturing or for deriving insights from next generation sequencing technologies during discovery? Is the data used for periodic compliance with ISO standards or does it inform daily research initiatives? The answers will be different from enterprise to enterprise.

What the concepts of big and data respectively mean to each CIO will be different, but the need for infrastructure cannot be overstated. Without an information platform built on the four pillars above, there’s too much risk in the system, too many inefficiencies and insufficient access to breakthrough insights. Today, predictive analytics can root out costly process nonconformance and optimize drug manufacturing. Likewise, a LIMS could be what breaks the discovery bottleneck in an R&D lab. But none of this is possible without data, and it certainly won’t be big unless a CIO builds an infrastructure that makes it so.