7 Considerations When Implementing and Maintaining a Research Data Management Platform

May 1, 2023

Graphs and charts displaying on a modern tablet device.

Investments in digital transformations are expected to grow from $594.5 billion USD in 2022 to $1.548.9 billion USD by 2027, with the goal of improving the generation, collation, storage, and utilization of data1. This drive to better manage and protect data is not a luxury. The global datasphere is predicted to reach 165 zettabytes by 2025, and healthcare related data generation alone has already increased by 878%2.

Within this ever-expanding technology landscape, life science organizations must separate the noise from meaningful, relevant data to make real-time decisions to drive lifesaving therapeutics to market. The investments in digital transformations are, therefore, key to empowering and accelerating an organization's success; however, 70% of digital transformation assessed by BCG failed3.

In life science organizations, many factors may contribute to this observed failure, including, but not limited to, research platform implementation and maintenance. In this article, the fundamental involvement of people, processes, data, and technology will be assessed in the context of successful research data management platform implementation and on-going maintenance.

1. The perfect pair - strategy & execution

The famous Chinese military general, strategist, and philosopher Sun Tzu is quoted as saying “Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat.” In the same way, any technology change to an R&D ecosystem will require foresight, preparation, and action to be successful. A fundamental vision that outlines the journey for the coming year will allow for strategic planning, timely execution, and confidence that efforts to adopting a new technology will not be undermined with continual technology changes.

However, a vision alone is not enough. Appropriate actions are required. A clear implementation, testing, training, go-live, and post-go-live action plan must be constructed, disseminated, and used to hold accountable stakeholders responsible, to ensure that these activities are completed successfully. Furthermore, employees should have a clear understanding of why the change is occurring and the impact this change will have on them directly.

2. The cultural mindset

A successful digital transformation in life sciences requires a strategy, which is shaped by both the technology being deployed and the end users. Change management is the framework used by an organization to prepare, enable, and nurture employees affected by a change to ensure change adoption. Absent or ineffective change management strategies undermine project success3. Important aspects of change management to consider when implementing and maintaining a research platform are:

  1. Existing skill sets. Ideally, research data management platform selection should have considered the already existing technical skills and mindsets of the end users. If a skills deficiency exists in the userbase, then adequate training will be required before daily utilization of the system can be achieved. Ensure that this training is adequately factored into the strategy.
  2. The technology mindset. If your scientists have been using an established arts and craft methodology of documenting their research in paper notebook for the last 25 years, they may be just a bit resistant to the idea of moving onto an electronic system, regardless of what that system is. The training and adoption strategy utilized must account for your unique set of end users. Foster positivity and enthusiasm early in the implementation process and provide as much support to empower users at a technological level, to help improve user adoption outcomes.
  3. The training and education mindset. Training and education are powerful components for professional development; however, such activities are not always associated with a positive response in organizations, especially when employees are overwhelmed with heavy work schedules already. Take time to determine what is the best mechanism for training – in person, virtual, or asynchronous to ensure participation. Work with line managers to ensure that training time is adequately accounted for in weekly activities of their subordinates. Also, training will be more welcomed if users understand the value the platform can provide to their daily activities. Disseminate this information early on to promote a willingness to learn. Consult with your implementation service provider to leverage methodologies that will ensure value is communicated and visible to end users as soon as possible.
  4. The communication mindset. Employees are inundated with communication - email, Teams, Slack, virtual meetings, etc. Ensure that information regarding the implementation, training, and on-going maintenance of any research platform is communicated to end users in a culturally appropriate method to ensure the message is heard. Additionally, provide a mechanism for two-way communication, allowing end users to voice suggestions, concerns, and request help. Listening to end users and then facilitating appropriate responses and actions will go a long way in creating a change-positive environment, which will serve to empower future digital transformations as well.
  5. Ownership. There is a burden, both in terms of time and financial investment, to maintain and upgrade a research data management platform on an on-going basis. If these responsibilities fall onto your employees, have they been informed and do they have the skills and time to do so? If not, are there feasible options available for you to outsource this work or to depend on the software vendor directly?

3. Be aware of the revolving door.

Employee turnover in any organization is evitable. In the US, the average turnover of life sciences and medical devices employees was 20.6% in 20204. 45% of these resignations were in employees with a tenure of less than 1 year4. It is thought that this high turnover rate is due to the large availability of open life sciences positions, facilitating easy transitions. Therefore, an organization must be prepared for a constant exodus and influx of platform end users.

Generational differences in technology expectations are an important consideration here; younger generations of employees are technology savvy, utilizing simple, integrated applications at home. Siloed, manually processes, such as copying and pasting data from one spreadsheet to another, will disappoint this cohort and undermine their enthusiasm, productivity, and ultimate retention. Therefore, important questions to ask in the context of employee retention and digital transformations are:

  1. If your research platform is to be supported, in an on-going manner, by in-house individuals, what happens if these people leave?
  2. If new end users are employed after platform training and go-live, who is responsible for training these new individuals on the technology?
  3. How would changes in your workforce affect the technology mindset of employees and the metrics used to quantify successful implementation and maintenance over time?

4. Quantify your success.

To know if a therapeutic candidate is effective and safe for patient use, multiple tests and trials are conducted. The outcomes from these activities determine the developmental and manufacturing trajectory and viability of the candidate.

In the same way, implementation and maintenance of a research data management platform should be continually monitored for success and viability. The specific metrics, and thresholds thereof, utilized to gage success or to incite intervention will be organization-specific but must always be quantifiable. Quantification is particularly important for direct assessment of any improvements or further deteriorations of a metric during remediation activities. Some commonly used metrics include impacts on quality (both product and data quality), scientific efficiency, turnaround times (for example the time taken to fulfil an analytical request), and the capacity to conduct activities that were not supported in the legacy ecosystem.

5. Learning from past to improve the future.

The use of quantitative metrics to track success of all digital transformations is particularly valuable when undertaking a new technology-based project. If previous digital transformations have failed, it is time to do something different.

Consider the reasons why failure occurred, assessing for patterns in historic implementations methodologies and outcomes. Frequently, organizations try to use the same methods for each new system deployed in their technology ecosystem. Although this may work sometimes; it is more likely to fail as each system will have its own unique quirks and challenges to implement and maintain, such as specific hardware and third party, auxiliary software. Implementation may also be better suited for some software and systems in phases while others as a mass release.

Important considerations here are the interconnected nature of your teams/departments, and whether there are shared processes and/or items will be affected by a specific implementation methodology.

6. FAIR data

A spiderweb provides shelter, transportation, and food acquisition to a spider. This is achieved by individual silk strands of the web working together in an interconnected manner.

In the same way, the technology landscape works to store and protect, transport, and mediate utilization of data. To achieve this, the constitutive technologies, ranging from communication systems to laboratory instruments, must, ideally, work in unison. Such unity rarely occurs automatically. Instead, most organizations face disconnected disparate systems which natively cause data siloing.

Efforts should be made to ensure that implemented technologies align with FAIR (Findability, Accessibility, Interoperability, and Reusability) data principles to prevent data silos and enable current and future data utility. As part of these principles, considerations should be made to allow both humans and machines to easily find data. This data should always be available but under tight access controls, and stored in a manner that is understandable and in a format that supports repeated utility in a diverse array of applications and workflows. Consider, and ideally deploy, the API's and web services available to the platform, data storage formats, and the necessary resources to enable FAIR data principles within your organization.

7. Prepare for evolution.

The best fit-for-purpose research platform solution for any organization will address, when configured and deployed appropriately, an organization's current challenges and scientific and operational use cases. The word 'current' in the previous sentence is an important distinction. In five years, due to the constant evolution of science and technology, an organization may face different challenges. Therefore, an implemented system should ideally have the capacity to evolve through:

  1. Configuration flexibility to support changing or new workflows and science, and
  2. Vendor developments and enhancements to the product. But these two capabilities are not enough on their own.

An executable strategy must exist regarding the resources needed to conduct any necessary changes, upgrades and/or net new configurations. These types of services are not conventionally included within an initial software purchase; instead, these on-going support services are acquired separately and typically billed per hour of support. Organizations should do price comparisons to see how the vendor's price compares to third-party options, and, if leveraging internal resources, what the resource burdens would be in terms of personnel, time, and expertise.

Empowering digital transformations in life sciences

The details above are not an exhaustive list but should provide thought-provoking considerations to empower success during a technology-based transformation. Remember that this is not a journey you need to undertake alone. ProPharma is always ready to partner with you as you navigate your technology landscape and aid in your success. Contact us today to learn how we can help your digital transformation and research data management platform implementation and maintenance.


Random data with transparent overlaid on top of person working in a lab.

Project Management isn’t brain surgery, but why is it so hard?

Here is the scenario: you are the leader of a group and you have been given the responsibility of driving the completion of a new initiative, an early or late-stage drug or devices program, or a...

Do You Need a Resource Management Team?

In the latest PMI report Pulse of the Profession 2020 (2020), 11.4% of surveyed organizations attribute investment waste to poor project performance. What defines project performance and ultimately...

ISO13485: Understanding the Management Review Metrics Scorecard

As discussed in part one and part two of this series, ISO13485 is a regulatory standard whose focus is meeting customer requirements, including regulatory requirements, and maintaining the...