Man working on laptop; data silos concept

Breaking Down Data Silos in Life Sciences

Data silos remain an ongoing challenge for life sciences companies.

When data is siloed, access, collaboration, and analysis become difficult. Teams requiring the same datasets may be forced to create two copies of the data — increasing the risk of errors.

De-siloing data promises better harmonization, accessibility, and interoperability. Choosing digital tools that can easily harmonize data and provide access without sacrificing security is essential to a successful de-siloing project.

The Intransigence of Data Silos

Two common practices hinder the optimization of data use in the life sciences, writes Adam Marko, director of life science solutions at Panasas. These are data siloing and a practice Marko calls “distribute and delete.”

Distribute and delete is the practice of generating data for external use and deleting it once that data is delivered to its external recipient. Marko notes that life sciences companies may engage in this practice to free up storage space or processing power. Deleting data after distribution has several disadvantages, however, including the risk of unnecessary data duplication and the inability to analyze large, natively interoperable data sets.

Data silos, meanwhile, pose their own challenges. “While Distribute and Delete typically stems from technical and financial limitations, breaking down data silos may present more of a challenge simply because they are so embedded in the culture of life sciences,” Marko writes.

Data silos can also lead to data duplication within an organization, which increases the risk of errors and inconsistencies. It also uses additional storage and processing resources.

Both data silos and distribute and delete practices hinder the ability of life science organizations to maximize the value of other digital life sciences trends. These trends include the use of wearables, leveraging cloud technologies, and incorporating artificial intelligence (AI) into data analysis, writes Pat Jenakanandhini, chief product officer at ArisGlobal.

These obstacles produce measurable impacts in the life sciences. An Aspen Technology study found that approximately half of surveyed pharmaceutical companies said data silos hindered or prevented cross-functional collaboration within the company. These impacts were largest among the biggest pharma companies: More than half (53 percent) of respondents with total annual revenues over $1 billion said siloing caused these problems.

The COVID-19 pandemic pushed pharmaceutical companies to share data on a new scale, both to develop effective vaccines and to improve their own internal data access and interoperability, says Sadiqa Mahmood, general manager and senior vice president of life sciences at Health Catalyst.

The rapid scaling of new digital tools has created new problems. For one thing, it’s raised concerns about security and privacy risks. At the same time, it’s brought data management as a discipline into sharper focus.

Technician working with a microscope and entering data into a laptop while sitting at a table in a lab; data silos concept

Aggregation, Harmonization, Interoperability: 3 Goals for Life Sciences Data

Breaking down data silos and preserving data without clogging systems is a real challenge.

To date, many pharmaceutical and life sciences companies hesitate to take bold steps toward either goal, fearing they don’t have enough information to make sound decisions, write Christian Kaspar, Thomas Solbach, and Holger Schmidt in a PwC report.

The result is a catch-22: Life sciences companies want to use digital tools to make their data more interoperable, harmonized, aggregated, and accessible, but they lack the information needed to make sound decisions because that information lives in the data they struggle to access. Lacking a starting point, life sciences teams can have difficulty overcoming the challenges posed by siloed data.

A set of principles for data interoperability, harmonization, aggregation, and accessibility can help provide guidance to life sciences leadership. One example: the FAIR principles, first articulated in 2016.

The four foundational FAIR principles — findability, accessibility, interoperability, and reusability — “serve to guide data producers and publishers as they navigate” obstacles like data siloing and data deletion, Mark D. Wilkinson, et al. write in Nature. These four principles are intended to work independently and in concert to improve not only data points and sets but also algorithms, workflows and digital tools that help researchers generate, organize, analyze, and manage their data.

Understanding alternate data management models can also give life sciences organizations a starting point for their de-siloing efforts. One way to rethink the approach to data is to choose an alternate image or approach to data storage and access — a goal toward which to direct digital efforts.

To avoid the problems associated with data silos, some organizations are turning to the data lake model. “A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data,” writes Antonio Scaramuzzino, senior product manager at Google. Data lakes centralize data, using role-based access controls (RBACs) and other tools to manage access, privacy, and security.

By optimizing access to and the use of data, life sciences organizations can reduce administrative costs, including administrative costs, and costs of care. At the same time, these organizations can make care delivery more efficient, increase their own revenue, and speed up growth.

Group of medical technicians working and analyzing data research information together in the laboratory; data silos concept

Choosing the Right Tools to De-Silo Life Sciences Data

“The future of health will likely be defined by radically interoperable data, open yet secure platforms, and consumer-driven care,” write Deloitte’s Tony Jurek and Mark J. Bethke.

Currently, life sciences companies are engaging with new ways of using their data to reach these goals. The COVID-19 pandemic accelerated efforts toward better interoperability and access, creating new momentum for breaking down silos. New innovations in healthcare technology are laying further groundwork.

“One way of breaking down the barriers [caused by data silos] is by deploying products that utilise a common data ecosystem enabling real-time data access,” says Raman Bhatnagar, vice president and general manager at Aspen Technology. The benefits of using these products include improved data access, better visibility and transparency, and better decision-making based on more comprehensive and accurate data analysis.

The right transformation choices pay off. Successful life sciences companies’ transformations in recent years “captured 1.5 times more value than management teams thought possible,” write Christian Amberg, Thomas Boitani, Laura Bremme, and Matthews Mmopi at McKinsey.

They recommend that companies set a “stretch target” that demands cooperation among multiple departments and functions — and calls for the digital infrastructure necessary to break down silos and support that collaboration.

Regardless of the size or scope of the challenge, it’s unlikely that any organization will find a one-size-fits-all solution. Rather, life sciences companies are likely to work with a number of digital tools and vendors to realize their goals.

To coordinate these efforts, companies need to set strategy first, then seek out digital tools and experts to provide support, says Michael Leonard, AWS marketplace global healthcare strategy lead. A clear vision can help life sciences companies identify their top data silo challenges and choose effective solutions.

Data silos have proven one of the most persistent challenges in the digital era both in the life sciences and in other industries. Today’s digital tools for life sciences are built to address the primary hurdles of a siloed world by building connections between datasets and systems. These tools improve interoperability, boost access, and harmonize datasets for more robust analysis — and they do so without sacrificing the high demands of privacy and security in healthcare information.

Images by: andreypopov/©, marvent/©, gballgiggs/©

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