How to Build a Clinical Trial Data Strategy

Data Strategy – What Is It?

You can consider a data strategy as a road map guiding you on how you will collect, manage, store, and report your clinical trial data. The best, most comprehensive strategies define, at a minimum:

  • How you will collect and clean your data
  • Any therapeutic area-specific data needs
  • A data integration plan
  • Where you will store your data
  • How you will report your data

Key Benefits of Implementing a Strategy (and Risks of Not)

Today’s clinical trials have an ever-increasing number of data sources. Your current sources might include:

  • EDC system to capture most site-based data
  • eConsent component
  • ePRO or eCOA component
  • RTSM component
  • Central safety laboratory
  • Central ECG
  • Central specialty lab
  • PK data source
  • Central imaging source
  • And potentially much more!

All of these must be able to be integrated and reconciled in order to give you the complete database necessary to support the reporting you need for your clinical trial data.

This increase in data sources is driving a corresponding increase in time to database lock at the conclusion of a trial. This in turn can potentially prolong the time to delivery of individual study results as well as increase integrated reporting efforts to support regulatory submissions. An effective, efficient, and clear clinical trial data strategy will yield increased data quality and access, shorten timelines, and reduce costs.


By defining a data strategy, you will be required to create a plan for all of the data you will collect. This means less time to integrate and reconcile the data since you will define formats and processes in advance of need.

The core benefits of a clinical trial data strategy include:

  • Less time to integrate data: both within a trial and across trials
  • Easer and faster management of incoming data
  • Reduced vendor oversight burden and increased oversight compliance
  • Shorter timelines to database locking and reporting
  • Streamlined data visualization and analytics across your trials

Potential Risks

What happens if you do not design and implement an appropriate data strategy? The main potential risks include:

  • Disparate data sources that cannot speak to each other within a trial and across trials
  • Last-minute cleaning and management of incoming data
  • Increased risk of missing, incomplete, and/or incompatible data due to vendor(s) changing the data format(s)
  • Extended time to database lock and reporting due to incompatible data
  • Lack of access to your data ahead of integrated reporting and no possibility to visualize your full database

Success Factors: Clinical Trial Data Strategy Development

Regardless of what your data strategy includes, it should be built on several critical success factors. Based on our experience, we in CROS NT outline the following factors as crucial for success:

  • Sponsorship – understanding who will own and champion the development throughout the company. This person will drive the strategy throughout its development lifecycle.
  • Definition – clarifying what you need to include in your data strategy. Is it enough to include only data collection in the strategy or do you need to include more, for example reporting or long-term storage?
  • Development – defining what process are you will follow to develop your strategy
  • Buy-in – getting buy-in from all of the stakeholders who will be impacted by your data strategy. This likely includes any biometrics team members such as data managers, statisticians, and programmers, as well as clinical operations. Other stakeholders might include pharmacovigilance who will be affected by any reconciliation processes and your IT department that will address storage and access.
  • Implementation – understanding what is your timeline for implementation. Will your new strategy be implemented early in a development program? It is paramount also to develop a plan to manage consistent implementation.
  • Adherence – determining what level of adherence you will require and how you will ensure that it is properly monitored. You want your strategy, and any standards within it, to be a little challenging to deviate from, so you maintain control and a high level of standardization.

How to Build a Clinical Trial Data Strategy: Clinical Success Factors

These success factors apply regardless of the scope of your strategy: a single trial, a development program, or on a company-wide basis. However, they will look different in the application based on the range of impacted stakeholders.

Key Steps to Developing Your Clinical Trial Data Strategy

So what are the key steps to developing your data strategy? There are four of them and they are all based on the gap analysis:

  • Analyze: you need to start with analyzing and understanding where you are now
  • Define: you need to define what you want your ideal future state to look like
  • Identify and evaluate: you will identify the gaps between where you are now and where you want to be and evaluate options to fill those gaps
  • Create and implement: you will need to create and implement your plan.

Analyze: Who, What, Where, and How

When you think about the analyzed phase you can consider who, what, where, and how questions:

  • Who:
    • Who do you have in your company now and who do you think you might have in the future?
    • Do you have a team in place that can help to manage how you are storing your data?
  • What:
    • What are your quality management system and your SOP status?
    • What do your SOPs cover? Only clinical activities or biometrics activities as well?
    • What is your full clinical development program – what has been done, what is ongoing, what you might do in the future?
    • What you are doing with your data now?
  • Where:
    • Where is it stored during the study and can you see it integrated if you want to?
    • Where is your data stored after a study is done? Can you see it if you want to?
  • How:
    • How is your study data managed now? Are disparate sources integrated into a single system?
    • How do you manage your data outsourcing strategy at the moment?
    • How did you decide to take this outsourcing approach?

Answering these questions will help you to understand your current situation so you can more easily identify your ideal future.

Define: Standards and Strategies

Once you know where you are, you can think about where you want to be. We outline eight   components to consider when you are thinking about your ideal future:

  • SOPs/Guidelines – What activities should be covered by your own SOPs?
  • CRF Pages/Forms – What standards or templates should be in place?
  • Transfer Requirements – How often do you want data transfers occurring? In what format?
  • Data Checks and Reconciliation – How frequently should data checks and reconciliation occur? Who is responsible for these activities?
  • Data Integration and Conversion – When do you want to integrate data (either disparate sources within a study or across studies)? When will you start CDISC conversions if required for submission?
  • Documentation – What study documents should have templates?
  • Reporting – What standard tables or reports do you want to see?
  • Storage and Access – Where will you store your data and how do you want to access it?

Addressing each of these components will become another step to building your own successful data strategy and gathering benefits from it.

How to Build a Clinical Trial Data Strategy

Identify and Evaluate: Standards and Strategies

Once you have determined where you are and where you want to be, you can identify the gaps and evaluate the solutions that will help you achieve your ideal data strategy.

This means working on the same eight components on which you worked during the previous step, but now implementing the planned actions:

  • SOPs/Guidelines – develop a list of what’s missing from your set. In the context of data strategy, these will most likely be related to data, but you may find that you have some other related SOPs that need some updating.
  • CRF Pages/Forms – develop your templates to CDASH if you aren’t already doing so.
  • Transfer Requirements – set the benchmarks for frequency and format as well as the transfer method.
  • Data Checks and Reconciliation – consider both standard data source and minimum required for ad-hoc data sources.
  • Data Integration and Conversion – decide timelines and/or decision points to manage on a single study basis as well as on a program basis.
  • Documentation – decide which documents you want standardized, make a list of which templates need to be developed, and define a priority list.
  • Reporting – develop a set of templates for all of your tables, figures, and listings.
  • Storage and Access – identify your preferred storage location – this can either be in-house or with a partner vendor.

Create and Implement: Your Clinical Trial Data Strategy

Once you have defined the missing parts of each component and evaluated what additional items need to be put in place, you can create and implement your data strategy. Since you know everything that is missing, you can assess which components you can address on your own, and for which you will need support.

If you need support, you can find a specialized data vendor who can consult with you on any individual component or develop and implement the entire strategy with you.

It is important to set timelines and owners for each stage of the process so that you can move purposely and efficiently through the development process. Finally, after your strategy is complete, you can sit back and relax and watch your data manage itself (not quite – but close!).

The last component of any effective data strategy is the creation of a system for continuous oversight and management. This is a crucial step to ensure ongoing adherence to the strategy as well as continued acceptability of the standards you have put in place.