The Evolution of Clinical Data Strategies & Digital Transformation


The Evolution of Clinical Data Strategies & Digital Transformation

The data from this study can help you understand how clinical data sources are growing, how that is impacting the industry and how to mitigate disruption.

As revealed in the Tufts-eClinical Solutions Data Strategies & Transformation Study Results*, where 149 sponsor organizations were surveyed, the life sciences industry must adopt new data strategies and improve technology infrastructure to accelerate drug development.

The volume of data is growing

Sponsors are contending with more data than ever before. Over two-thirds of clinical trial sponsors are using or piloting at least four types of data


of sponsors perceive these data management activities as time-consuming and labor-intensive

Data integration and management is time consuming

Top three most time consuming:

Initiating relationships with data providers

Performing data review and cleaning

Transforming and mapping data

Followed by:

Importing or ingesting data

Curating data

Analyzing data

Tools to centralize and standardize data are unsophisticated

75% of companies are still using SAS as the primary tool for data integration and publishing.

Cycle times are increasing

40% increase since 2017
Companies using 4 or more sources are experiencing an increase of more than four weeks in last patient last visit (LPLV) to database lock cycle.

Only 1/3 of sponsors have implemented a formal data strategy

Top 3 Digital Transformation Accelerators


Defined Data

Executing these strategies decreases database lock cycle times by an average of two weeks



Sponsors using a data hub/lake rate their analytic competencies and capabilities higher



Nearly 3 out of 4 sponsors are expanding data scientists’ roles to support advanced analytics

A data strategy defines what data is needed and how it should be presented to the data consumer.
Companies with data strategies have faster cycle times.
Companies with data strategies have better analytics capabilities.

How does your organization compare?

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