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5 Steps for Creating a Successful Clinical Data Strategy

Life sciences companies are doing incredible work to turn major scientific breakthroughs into new therapies that treat disease and improve human health. The way these innovative new treatments are approved is via the clinical research and development process where the data, and chain of custody of that data, that demonstrates the history and results of the trial regarding safety and efficacy are provided to regulatory authorities to determine approval. The data is the final product that is delivered along with the physical treatment – and the combination of both tells the story of the way the medicine works and the benefit it provides to the specific patient population it is intended for. As such, there are few industries where data is more important and vital to the success of an organization than Life Sciences. As the complexity of clinical trials increases and data is coming in from a multitude of sources, from wearables to genomics, it is critical to ensure that your business has a clear clinical data strategy to maximize all of its data sources, no matter where the data comes from. Here are a few basic steps to help your organization start maximizing your data’s potential.

1. Get Executive Sponsorship

Regardless of size, obtaining high-level buy-in is imperative for getting any new data initiative off the ground. This step will be especially important for engaging all business units in the process and for training everyone on the goals of the data strategy. When preparing to discuss this change with senior executives, it is critical to:

  • Show alignment between the business strategy and data strategy
  • Discuss the value of a cohesive data strategy, including more informed decision making
  • Highlight risk mitigation objectives
  • Discuss how using data more productively can support a data-driven business

A good data strategy will help the overall business by reducing wasted time and by improving the decision-making process. By ensuring that you have the business needs at the forefront of your data strategy plan, you can mitigate many of the concerns that executives at your company might have.

2. Define Scope and Set Objectives According to Your Profile

As outlined in the Harvard Business Review article “What’s Your Data Strategy,” methods can be either defensive or offensive. Defensive strategies are focused on minimizing downside risk and ensuring compliance. They tend to appeal to highly regulated industries, such as healthcare and pharmaceuticals. Offensive strategies tend to be relevant for customer-focused business units and appeal to companies with high competition for customers. Many companies focus on balancing these two when creating their data strategies. eClinical Solutions encourages companies to consider creating short-, mid- and long-term objectives.

Once you’ve established these objectives, decide what the right data is, who the right people are to receive this data and what format will be the most effective. As your business grows and changes, your data strategy should grow and change with it.

Although you may start with a defensive strategy, over time, more mature product lines and stiffer competition can shift the focus of your strategy. Data strategies should be agile to ensure that they are always evolving alongside company objectives.

3. Summarize Data Time Horizons

Decide which data categories will be needed for your short, mid and long-term goals. These don’t have to be set in stone; there will likely be revisions on the mid and long-term goals as they approach. Some key data categories to consider:

  • Source
  • Format
  • Model and mapping requirements
  • Data aggregation needs
  • User roles
  • User personas
  • Use cases for data usage
  • Access requirements

Revisit your mid and long-term goals often as they approach. Consider what progress has been achieved over time and how you can adjust these categories to keep meeting your needs.

4. Map Out Use Cases & Workflows

In order to completely understand the lifecycle of your data and all workflows associated with it, try creating use cases for the data. Outline use cases for data interactions all the way from acquisition to final decisions and actions, and finally to archive. Some questions to consider when completing this exercise are:

  • What actions must be taken with this data?
  • Who interacts with the data at each step?
  • What value is derived from each step? What is required to maximize the value? What is required to maximize the efficiency of deriving data?
  • How is data handed off to the next step? What prerequisites for the next step can be facilitated now?
  • How will you relay to the next participants that the previous step has been completed?

Know what the purpose of your data will be and work through each step needed to meet your objectives. Be meticulous! It is better to over analyze which steps will be taken than to miss a valuable part of the workflow.

5. Define Analytics Strategy

Once you’ve defined your use cases, it’s time to define your data analytics strategy. For each of the defined use cases, consider questions like what action should be driven by the use case, what decisions need to be made to drive that action, what supplemental information is needed to make these decisions and what underlying technical infrastructure is needed to visualize data to support your desired outcomes. By starting from the end of the process, the preferred action from the use case, it allows you to work backwards to find each step that the data needs to take. Companies that go through this process should see results quickly. Decision making is more data driven and there are shared sources of information that align with key business objectives. For more information about how to develop a clinical data strategy, you can download the white paper here.


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