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Get more value from your data
Best practices for your data sourcing strategy.
Claire Goodswen, Sr. Director, Global Information Management
May 06, 2022

Life sciences companies have access to thousands of data files coming in from across the globe. It’s a powerful yet can be a daunting position to be in. The right data and analytics capabilities can inform and influence every stage in the product lifecycle, from choosing molecules and recruiting patients, through the regulatory approval process, to ensuring a successful product launch and targeted omnichannel customer engagement.

When leveraged to their full potential, these fit-for-purpose data assets can drive considerable market value. However, many life sciences companies are struggling to make the most of these valuable assets, often 80% of the time is spent curating and standardizing data while only 20% of the time is spent on analytics as they lack a formal data sourcing and data management environment.

Hidden data

Large organizations often source healthcare data piecemeal from multiple vendors for different applications. They can include electronic medical records (EMR), insurance claims, customer queries, medical device readings, and other information for different regions, product lines, patient populations, and therapy areas.

If companies don’t have a formal process for data acquisition – and most don’t – it means different teams will acquire data sets for a specific project or purpose without making it part of the broader data environment.

This can result in multiple teams acquiring the same data for different use cases; it prevents teams from combining data for more robust insights; and it makes it difficult to establish formal processes for cleaning, storing, sharing, and updating data files to maximize benefits from these investments.

This lack of clarity and structure adds cost while diminishing the potential insights that can be found in these assets. When companies replace this patchwork approach to data collection with a formal data sourcing strategy they accelerate their access to insights, while cutting costs and ensuring all data applications adhere to rigorous data privacy rules.

Data Sourcing Best Practices

The best data sourcing strategies establish clear rules for where data is acquired, how it comes into the company, and how it is cleaned, stored, and shared. The best programs feature the following pillars:

  • Established data partners. The fastest way to gain transparency and control over the data flowing into the organization is to establish preferred vendor relationships with vetted data providers. This makes it easier to establish quality and consistency expectations, and establishes transparency over what is acquired, how often it is updated, and who has access to it.
    To enhance this oversight and transparency, IQVIA offers life sciences companies access to an in-development data marketplace where they can acquire global and local data directly through IQVIA’s resources, and via other vendors as needed. This ensures the data is standardized and speeds time to access.
  • Centralized data management. Once acquired, data should be managed and controlled centrally. Having a single source of data is the foundation of an effective data sourcing strategy. It makes it easier to oversee those acquisitions and to implement rules for cleaning and organizing data. It also allows teams to determine what data is already available to support their project needs, which eliminates duplicate acquisitions and provides opportunities for deeper analysis of existing assets.
  • A consistent data model. Having a common data model makes it easier to combine assets and generate faster, more actionable insights using multiple data types. IQVIA encourages clients to follow a standardized data format applying industry models to help transform structured and unstructured health data into a common user-friendly format, allowing for systematic analysis of disparate observational databases. IQVIA can also leverage our enterprise Global Data Model (GDM) for clients to standardize the structure of data within the organization.
  • Provide self-service options. The data repository and data model should be designed with all users in mind. Creating a user-friendly interface and easily accessible environment ensures all teams can make use of these assets, without having to rely on data analysts to leverage the data. This can be achieved by deploying a self-service application which enables all users to rapidly access, analyze, visualize, and collaborate on insights.

Life sciences companies spend millions of dollars on data every year that often goes unused or is not fully leveraged for one reason or the other. Having an effective data acquisition strategy and working with partners who can provide a robust data environment can help them generate more value from these investments and drive better decision making across the organization.

To learn more about how to create a leading edge end-to-end EIM system, check out our website

IQVIA provides the gold standard for pharmaceutical market data as well as clinical research. IQVIA employs thousands of data scientists, healthcare professionals, technology experts, and data sourcing professionals who oversee processing of more 100 billion healthcare records annually. To learn more about IQVIA’s data services, contact us here.

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