Challenges and Solutions for Data Management in the Life Science Industry

Bio-IT teams must focus on five major areas in order to improve research efficiency and outcomes

Life Science research organizations need to collect, maintain, and analyze  a large amount of data in order to achieve research outcomes. The need to develop efficient, compliant data management solutions is growing throughout the Life Science industry, but Bio-IT leaders face diverse challenges to optimization.

These challenges are increasingly becoming obstacles to Life Science research, where data accessibility is crucial for gaining analytic insight. We’ve identified five main areas where data management challenges are holding Life Science research teams back from developing life-saving drugs and treatments.

Five Data Management Challenges for Life Science Research Firms

Many of the popular applications that Life Science researchers use to manage regulated data are not designed specifically for the Life Science industry. This is one of the main reasons why Life Science research teams are facing data management and compliance challenges. Many of these challenges stem from the implementation of technologies not well-suited to meet the demands of scientific research.

Here, we’ve identified five areas where improvements in data management can help drug R&D efficiency and reliability.

1. Manual Compliance Processes

Some drug research teams and their Bio-IT partners are dedicated to leverage software to automate tedious compliance-related tasks. These include creating audit trails, monitoring for personally identifiable information, and classifying large volumes of documents and data in ways that keep pace with the internal speed of scientific discovery.

However, many Life Science researchers remain outside of this trend towards compliance automation. Instead, they perform compliance operations manually, which creates friction when collaborating with partners and drags down the team’s ability to meet regulatory scrutiny.

Automation can become a key value-generating asset in the Life Science research process. When properly implemented and subjected to a coherent, purpose-built data governance structure, it improves data accessibility without sacrificing quality, security, or retention.

2. Data Security and Integrity

The Life Science industry needs to be able to protect electronic information from unauthorized access. At the same time, certain data must be available to authorized third parties when needed. Balancing these two crucial demands is an ongoing challenge for Life Science researchers and Bio-IT teams.

When data is scattered across multiple repositories and management has little visibility into the data lifecycle, striking that key balance becomes difficult. Determining who should have access to data and how permission to that data should be assigned takes on new levels of complexity as the organization grows.

Life Science research organizations need to implement robust security frameworks that minimize the exposure of sensitive data to unauthorized users. This requires core security services that include continuous user analysis, threat intelligence, and vulnerability assessments, on top of an MDM-based data infrastructure that enables secure encryption and permissioning of sensitive data, including intellectual properties.

3. Scalable, FAIR Data Principles

Life Science organizations increasingly operate like big data enterprises. They generate large amounts of data from multiple sources and use emerging technologies like artificial intelligence to analyze that data. Where an enterprise may source its data from customers, applications, and third-party systems, Life Science researchers get theirs from clinical studies, lab equipment, and drug development experiments.

The challenge that most Life Science research organizations face is the storage of this data in organizational silos. This impacts the team’s ability to access, analyze, and categorize the data appropriately. It also makes reproducing experimental results much more difficult and time-consuming than it needs to be.

The solution to this challenge involves implementing FAIR data principles in a secure, scalable way. The FAIR data management system relies on four main characteristics:

Findability. In order to be useful, data must be findable. This means it must be indexed according to terms that researchers, auditors, and other stakeholders are likely to search for. It may also mean implementing a Master Data Management (MDM) or metadata-based solution for managing high-volume data.

Accessibility. It’s not enough to simply find data. Authorized users must also be able to access it, and easily. When thinking about accessibility—while clearly related to security and compliance, including proper provisioning, permissions, and authentication—ease of access and speed can be a difference-maker, which leads to our next point.

Interoperability. When data is formatted in multiple different ways, it falls on users to navigate complex workarounds to derive value from it. If certain users don’t have the technical skills to immediately use data, they will have to wait for the appropriate expertise from a bio-IT team member, which will drag down overall productivity.

Reusability. Reproducibility is a serious and growing concern among Life Science professionals. Data reusability plays an important role in ensuring experimental insights can be reproduced by independent teams around the world. This can be achieved through containerization technologies that establish a fixed environment for experimental data.

4. Storage Solutions

The way your research team stores and communicates data is an integral component of your organization’s overall productivity and flexibility. Organizational silos create bottlenecks that become obstacles to scientific advancement, while robust, accessible data storage platforms enable on-demand analysis that improves time-to-value for research applications.

The three major categories of storage solutions are Cloud, on-premises, and hybrid systems. Each of these presents a unique set of advantages and disadvantages, which serve specific research goals based on existing infrastructure and support. Organizations should approach this decision with their unique structure and goals in mind.

Life Science research firms that implement MDM solutions are able to take important steps towards storing their data while improving security and compliance. Master data management provides a single reference point for Life Science data, as well as a framework for enacting meaningful cybersecurity policies that prevent unauthorized access while encouraging secure collaboration.

MDM solutions exist as Cloud-based software-as-a-service licenses, on-premises hardware, and hybrid deployments. Biopharma executives and scientists will need to choose a deployment style that fits within their projected scope and budget for driving transformational data management in the organization.

Without an MDM solution in place,Bio-IT teams must expend a great deal of time and effort to organize data effectively. This can be done through a data fabric-based approach, but only if the organization is willing to leverage more resources towards developing a robust universal IT framework.

5. Monetization

Many Life Science research teams don’t adequately monetize data due to compliance and quality control concerns. This is especially true of Life Science research teams that still use paper-based quality management systems, as they cannot easily identify the data that they have – much less the value of the insights and analytics it makes possible.

This becomes an even greater challenge when data is scattered throughout multiple repositories, and bio-IT teams have little visibility into the data lifecycle. There is no easy method to collect these data for monetization or engage potential partners towards commercializing data in a compliant way.

Life Science research organizations can monetize data through a wide range of potential partnerships. Organizations to which you may be able to offer high-quality research data include:

Healthcare providers and their partners.

Academic and research institutes.

Health insurers and payer intermediaries.

Patient engagement and solution providers.

Other pharmaceutical research organizations.

Medical device manufacturers and suppliers.

In order to do this, you will have to assess the value of your data and provide an accurate estimate of the volume of data you can provide. As with any commercial good, you will need to demonstrate the value of the data you plan on selling and ensure the transaction falls within the regulatory framework of the jurisdiction you do business in.

Overcome These Challenges Through Digital Transformation

Life Science research teams who choose the right vendor for digitizing compliance processes are able to overcome these barriers to implementation. Vendors who specialize in Life Sciences can develop compliance-ready solutions designed to meet the needs of drug R&D, making fast, efficient transformation a possibility.

RCH Solutions can help you capitalize on the data your Life Science research team generates and give you the competitive advantage you need to make valuable discoveries. Rely on our help to streamline research workflows, secure sensitive data, and improve drug R&D outcomes.

RCH Solutions is a global provider of computational science expertise, helping Life Sciences and Healthcare firms of all sizes clear the path to discovery for nearly 30 years. If you’re interesting in learning how RCH can support your goals, get in touch with us here. 

How Can Life Science Researchers Balance Security with Emerging Technology in Life Science Research?

Data is the currency of scientific research. Its security should not be left to chance.

Data integrity is crucial across all forms of research, including Life Sciences research.  After all, it’s the only way researchers and regulators can assure the quality, safety, and efficacy of their products.

The way Life Science companies store and communicate data has become increasingly crucial to validating the expectation that their data is safe and secure; as a result, organizations must be hyper-vigilant in mitigating data risks like cyberattacks, data breaches, and record falsification.

In fact, these expectations have grown in recent years. As the Life Science industry grows in complexity, the use of highly automated Cloud-enabled systems makes data integrity increasingly important to sustainable success. Compliance needs are driving organizations to make their data-related processes more robust and secure.

It takes more than controls, processes, and technology to implement good data practice. Life Science research firms must adopt a wider shift towards educating for data risk mitigation and develop a culture that understands and values data integrity.

5 Key Elements of Data Integrity 

Life Science research data needs to be complete, consistent, and accurate throughout the data lifecycle. Ensuring that all original records and true copies – including source data and metadata – remain un-compromised in the Life Science environment is no small feat.  It is important to focus on five key characteristics to increase data integrity:

Attributability. Data must be attributable to a specific project or process of the specific individual who creates it. Modifications must produce an audit trail so that people can follow the path data takes through the organization.

Legibility. Data must be legible and durable. If it isn’t readable by the eye, it should be readily accessible by electronic means. Containerization, a means to support legacy software without needing to maintain legacy hardware/IT, is one way Life Science researchers maintain legibility for scientific workflow applications.

Chronology. Metadata should allow auditors to create an accurate version history. Processes that create metadata should do so in an immediate and verifiable way.

Originality. Data should retain its original format whenever possible. Verified copies should also retain original formatting and avoid arbitrary changes.

Accuracy. Data must accurately reflect the activity or task that generated it. Metrics that measure data should be standardized across platforms.

These characteristics ensure that data is complete, consistent, enduring, and available. Once Life Science research firms implement solutions that maintain data integrity, they can begin operating in more risk-intelligent ways.

Life Science Data Risks are Unique 

Several factors combine to give Life Science research a unique risk profile. While many of the threats that Life Science organizations face are the same ones faced by the commercial and government sectors, there are structural risks inherent to the way Life Science research must be carried out.

Intellectual properties in the Life Sciences are incredibly valuable. Drug formulas, medical device blueprints, and clinical data are the result of years of painstaking research. These properties may have life-changing patient impacts and the potential to generate billions of dollars in revenue. Understandably, these assets are of enormous interest to hackers, including attackers sponsored by hostile nation-states.

As relevant as is the issue of hackers, the internal risk is something to combat, as well.  Research teams often exchange sensitive information within different work streams, and among a wide range of partners. While sharing data expedites research and development, it also increases the risk of data falling into the wrong hands. Even within the field, it is important to be aware of potentially untrustworthy sources with or without malicious intent. 

Life Science organizations typically rely on a global network of suppliers for hard-to-find materials and equipment. Supply chain attacks – where attackers exploit a weak link in a trusted vendor to infiltrate organizations down the supply chain – are a dangerous and growing trend.

Mergers and acquisitions within the Life Science industry also have a tendency to increase security risks. When two companies merge, they inevitably share data in a trust-oriented environment. If both companies’ IT teams have not taken sufficient action to secure that environment first (or adopted a zero-trust model), new vulnerabilities may come to light.

Implement Cloud Security and Risk Mitigation Strategies 

Life Science researchers do not have to give up on the significant advantages that Cloud technology offers. They simply must plan for security contingencies that reflect today’s data risk environment.

Mitigating Cloud risk means establishing a robust cybersecurity policy that doesn’t simply conform to industry standards, but exceeds them. Beyond well-accepted methods like multi-factor authentication, full data encryption (in-transit and at rest) and data exfiltration add layers of protection but require adopting a more proactive stance towards security as a tenet of workplace culture. For example, it’s critical that teams manage encryption keys, fine-grain security, and network access controls internally (vs. outsourcing to the Public Cloud provider). Additionally, work-flow controls and empowered data stewards help put controls in place with reduced impact(s) to collaborative work.

In summary, every research position is also a cybersecurity position. Teaching team members to maintain data integrity ensures secure, consistent access to innovative technologies like the Cloud.

RCH Solutions is a global provider of computational science expertise, helping Life Sciences and Healthcare firms of all sizes clear the path to discovery for nearly 30 years. If you’re interesting in learning how RCH can support your goals, get in touch with us here.