What makes a good data manager?

Posted by Cytel

Jun 14, 2018 10:30:00 AM

In this blog, Paul Fardy, Executive Director of Data Management at Cytel shares his thoughts on how the data manager role has evolved. 

India boats -1

Returning from a recent business trip to India I was reminded how the profile of a data manager had changed since my last visit 10 years ago. The first major offshoring activity for data management was in 2003, in which a large pharmaceutical company transferred 100 data manager jobs to an outsourcing group based in Bangalore. The second type of model to have a major effect on the industry was also begun in 2003, that of Functional Service Provision (FSP) between another major pharmaceutical company and a number of CRO partners.

Advances in technology have impacted the role

Both models have stood the test of time, although it has become clear that the skill sets required by data managers in both traditional and non-traditional models has needed to adapt significantly. One of the main drivers for this change is technology – the leap from paper to EDC was far from swift with the adoption for use in clinical trials taking much longer than originally anticipated. However, the last 5-10 years has seen an explosion in new forms of technology, ranging from easy to use data capture methods such as tablet, mobile and wearables to more sophisticated methods for imaging, translation and data visualization. A knowledge of these technologies, together with an understanding of the benefits that they could bring to the Sponsor and to the patient are crucial when considering the best strategy to deploy.

Regulatory knowledge is fundamental for compliance 

Underpinning these technologies are the regulations that need to be adhered to in order to protect subjects privacy and ensure data protection. A data manager needs to be aware of these regulations when involved in the running of clinical trials, from the setting up of a database or module for data collection to the application of risk management activities required by regulation ICH E6 R2. Tools exist to de-identify and anonymize (or pseudonymize) subjects, but they need to be used carefully and considerately so that there are no issues in their implementation and should not compromise the integrity of the subject.

GDPR has brought about its own challenges for the data manager, including the need to know where the data is being stored, be responsible for knowing how 3rd party vendor data is being handled as well as providing the capability for data to be erased if requested. However, there is no implementation guide for this new guideline, and many data managers have been left wondering how to go about managing clinical trial data within the boundaries that have been set. A framework should be established at the company level by which privacy awareness is embedded within the company culture, operational procedures and personal objectives. The data manager must take responsibility to proactively engage in all of these elements since their knowledge of managing data is second to none.

New challenges when handling big data 

The evolution of Real World Evidence (RWE) data, together with the unlimited capacity of "big data" will lead to a huge amount of unstructured data to be “curated”, a challenge that the data manager will need to face in order to turn data into information and ultimately into knowledge.

Sources of data have grown exponentially in the last few years, culminating in the Big Data phenomenon. What this means for data managers is an impact on key elements such as data integration, data reliability and data validation. The risk based approach I referred to earlier can play an important part in ensuring that the appropriate validation occurs and that there is an assurance of data reliability, despite the huge number of data points. The data manager will play a key role in establishing risk measures and determining the most appropriate risk assessment plans. The data manager will also need to have the ability to manage a variety of 3rd party vendors and establish plans and responsibilities for the handling of data from multiple sources.

Key competencies for a successful data manager 
So are the skills of a data manager that were required 15-20 years ago much different today? I would suggest not; there is still the need for data managers to be analytical, have good communication skills, to understand the links between various functions and to apply common sense. However, there is a need for the data manager to have a much broader perspective, and to be able to handle more tasks at the same time. The shift from on-site monitoring to remote monitoring has given the data manager an increased responsibility for looking at the data in real time and to allow decisions to be made on a site and patient level on an ongoing basis. The skills of the data manager in the future will be called upon to make sense of an ever expanding, ever changing world, although the core principles of subject safety and efficacy will always be central to the work that is carried out in this field.

 

Paul Fardy Photo Informal
For further information about data management at Cytel please contact Paul Fardy, paul.fardy@cytel.com or click the button below.
 
Data Management
 
About Paul Fardy
Paul Fardy is Executive Director of Data Management at Cytel and leads our clinical data management operations in the USA, Europe, and India. Prior to joining Cytel, he has held leadership positions within CROs and pharmaceutical organizations including Eisai, Ipsen, and Glaxo SmithKline.  

 

Topics: clinical data management, big data, Biometrics, clinical trials, real world evidence

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